Description
Developing close relationships with third-party logistics providers (3PLs) has been acknowledged in the literature as a beneficial strategy for 3PLs and customer firms. It has been shown that customers embedded in close relationships with 3PLs achieve higher levels of operational and financial performance.
ABSTRACT
Title of Document: DETERMINANTS OF CUSTOMER
PARTNERING BEHAVIOR IN LOGISTICS
OUTSOURCING RELATIONSHIPS:
A RELATIONSHIP MARKETING
PERSPECTIVE
Adriana Rossiter Hofer, PhD, 2007
Directed By: Professor Martin E. Dresner
Department of Logistics, Business, and Public
Policy
Developing close relationships with third-party logistics providers (3PLs) has
been acknowledged in the literature as a beneficial strategy for 3PLs and customer
firms. It has been shown that customers embedded in close relationships with 3PLs
achieve higher levels of operational and financial performance. 3PLs also benefit
from engaging in these relationships by generating higher levels of customer
satisfaction, customer retention, and referrals to new customers. In order to
complement these findings, this study integrates theories and empirical evidence
drawn primarily from relationship marketing to develop a model of the antecedents of
customer partnering behavior in logistics outsourcing relationships.
It is proposed that a combination of key interorganizational conditions and
customer characteristics directly impacts a customer’s partnering behavior with a
3PL. More specifically, a customer embedded in a relationship with a 3PL in which
there are high levels of dependence, trust, and satisfaction, is more likely to exhibit
higher levels of partnering behavior with a 3PL. In addition, a customer’s prior
experiences with partnering, and policy of engaging in interactive relationships with
customers, will also positively impact its partnering behavior with a 3PL.
Antecedents of dependence and trust are also identified in the model.
Data are collected through a web-based survey with customers of a large
Brazilian 3PL and the model tested using structural equation modeling. The results
support several of the hypotheses proposed in the model. In particular, evidence is
found that customer-specific characteristics, such as a customer relationship
marketing orientation and prior experience with 3PL partnering, have a positive effect
on a customer partnering behavior with a 3PL, above and beyond the effect of
interorganizational conditions, as advocated in traditional behavioral models.
Contributions of this research include the depiction of the interplay between
environmental forces, interorganizational conditions, and firm-specific factors that are
hypothesized to impact a customer’s partnering behavior with its 3PL. With an
understanding of the mechanisms on which a customer’s partnering behavior is built,
3PLs can take effective action in the pursuit of the development of closer
relationships with their customers, contributing to the maintenance and expansion of
their customer base.
DETERMINANTS OF CUSTOMER PARTNERING BEHAVIOR IN LOGISTICS
OUTSOURCING RELATIONSHIPS:
A RELATIONSHIP MARKETING PERSPECTIVE
By
Adriana Rossiter Hofer
Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, College Park, in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2007
Advisory Committee:
Professor Martin E. Dresner, Chair
Professor Curtis M. Grimm
Professor William J. DeWitt
Professor Donna B. Hamilton
Professor A. Michael Knemeyer
© Copyright by
Adriana Rossiter Hofer
2007
ii
Dedication
To the memory of my mom Glaucia who always encouraged me to pursue my dreams
and
to two wonderful and inspirational women:
grandmas “Vovó” Tildes and “Vovó” Teca.
iii
Acknowledgements
I would like to thank, first and foremost, my husband Christian, my sister Gal,
and my brother Nel, for their endless love and support throughout these five years of
doctoral studies.
I would like to acknowledge that the completion of my PhD and this project
would not have been possible without the guidance and friendship of Professor
Martin Dresner, who has been an inspirational example of a mentor and scholar to
me.
I would like to thank Dr. Donna B. Hamilton, Dr. Bill DeWitt, and Dr. Curt
Grimm for kindly agreeing to serve on my dissertation committee and to share their
expertise. I would also like to express my gratitude to Dr. Mike Knemeyer, whose
support and encouragement have been crucial in the growth of my understanding of
the logistics outsourcing research stream.
I would like acknowledge that this study was only possible thanks to the
support of Rapidão Cometa by providing access to their customer base and assistance
during the survey design and implementation. I would like to thank Rapidão
Cometa’s executives Américo Pereira Filho, Celso Queiroz, Vanessa Ramos, and
Fernanda Rogrigues, for their consistent collaboration and assistance on the
realization of this study.
I would like to thank Dr. Gregory Hancock for kindly and patiently guiding
me through the structural equation modeling process. I would also like to thank him
for his unforgettable statistics lectures.
iv
I would like to thank special staff members of the R. H. Smith School of
Business, Mary Slye, Ann Stevens, and Hazel Wentt, for always kindly assisting me
with administrative issues.
Finally, I would like to thank all my professors, friends and colleagues for
their friendship and support during the doctoral studies.
v
Table of Contents
Dedication..................................................................................................................... ii
Acknowledgements...................................................................................................... iii
Table of Contents.......................................................................................................... v
List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii
Chapter 1: Introduction................................................................................................. 1
Chapter 2: Literature Review...................................................................................... 11
2.1. Logistics outsourcing....................................................................................... 11
2.1.1. Third-party logistics (3PL) providers defined .......................................... 12
2.1.2. An overview of the 3PL industry.............................................................. 13
2.1.3. Logistics outsourcing in Brazil ................................................................. 15
2.1.4. Logistics outsourcing research.................................................................. 16
2.2. Partnering......................................................................................................... 26
2.2.1. 3PL-customer partnership defined............................................................ 26
2.2.2. Distinguishing between partnerships and other interorganizational
relationships ........................................................................................................ 31
2.2.3. Previous research on the antecedents and outcomes of partnering........... 34
2.3. Relationship Marketing.................................................................................... 39
2.3.1. Relationship marketing defined ................................................................ 40
2.3.2. Theoretical foundations of relationship marketing................................... 42
2.3.3. A brief introduction to social exchange theory......................................... 44
2.3.4. Relationship marketing literature focused on business-to-business
relational exchange ............................................................................................. 47
2.3.5. Relationship marketing orientation........................................................... 51
2.4. Ganesan’s (1994) model of long-term orientation........................................... 53
2.5. Cultural differences and logistics outsourcing................................................. 63
2.6. Conclusion ....................................................................................................... 66
Chapter 3: Model Development and Hypotheses ....................................................... 67
3.1. Conceptual model ............................................................................................ 67
3.2. Hypotheses development ................................................................................. 71
3.2.1. Primary antecedents.................................................................................. 71
3.2.2. Antecedents of dependence....................................................................... 82
3.2.3. Antecedents of trust .................................................................................. 93
3.3. Hypothesized model......................................................................................... 98
3.4. Contrasting the model of customer partnering behavior with Ganesan’s model
of long term orientation ........................................................................................ 102
3.5. Conclusions.................................................................................................... 105
Chapter 4: Methodology ........................................................................................... 106
4.1. Research design ............................................................................................. 106
4.2. Measurement of the constructs ...................................................................... 108
4.2.1. Dependent construct: customer partnering behavior .............................. 110
4.2.2. Primary antecedents................................................................................ 111
vi
4.2.3. Antecedents of dependence..................................................................... 117
4.2.4. Antecedents of trust ................................................................................ 122
4.3. Survey design................................................................................................. 123
4.4. Survey implementation.................................................................................. 126
4.5. Conclusions.................................................................................................... 128
Chapter 5: Data Analysis and Results....................................................................... 129
5.1. Final sample and respondents characteristics ................................................ 129
5.2. Descriptive statistics of the constructs........................................................... 133
5.3. Tests for non-response bias............................................................................ 136
5.4. Structural equation modeling......................................................................... 139
5.4.1. Data preparation and preliminary analysis ............................................. 140
5.4.2. Measurement phase................................................................................. 143
5.4.3. Structural phase....................................................................................... 148
5.5. Results............................................................................................................ 151
5.6. Conclusions.................................................................................................... 158
Chapter 6: Discussion and Concluding Remarks...................................................... 160
6.1. Discussion of model results ........................................................................... 160
6.2. Contributions.................................................................................................. 166
6.3. Managerial implications................................................................................. 169
6.4. Limitations ..................................................................................................... 172
6.5. Future research............................................................................................... 173
6.6. Summary and concluding remarks................................................................. 175
Appendices................................................................................................................ 177
Bibliography ............................................................................................................. 192
vii
List of Tables
Table 1. Summary of the findings of Ganesan’s (1994) model .................................. 60
Table 2. List of hypotheses of the determinants of customer partnering behavior..... 99
Table 3. Position profile of the respondents ............................................................. 130
Table 4. Respondents’ Industries.............................................................................. 130
Table 5. Number of employees of the respondent firms........................................... 131
Table 6. Number of functions outsourced................................................................. 132
Table 7. Respondents’ logistics functions outsourced.............................................. 133
Table 8. Descriptive statistics of the constructs........................................................ 134
Table 9. Correlation matrix for the averages of the constructs................................. 135
Table 10. Comparison of vector means between early vs. late respondents............. 137
Table 11. Manova comparison of vector means (respondents vs. non-respondents) 138
Table 12 Variance extracted of the constructs.......................................................... 145
Table 13. Test for discriminant validity for construct pairs with high covariance... 146
Table 14. Construct reliability results....................................................................... 148
Table 15. Summary of fit indices for the full model................................................. 150
Table 16. Standardized path coefficients. ................................................................. 151
Table 17. Summary of Results.................................................................................. 153
viii
List of Figures
Figure 1. Continuum of relationship styles (extracted from Gardner et al 1994)....... 31
Figure 2. Ganesan’s (1994) model.............................................................................. 55
Figure 3. Ganesan’s (1994) model with results .......................................................... 59
Figure 4. Conceptual model of customer partnering behavior in logistics outsourcing
relationships ................................................................................................................ 70
Figure 5. Primary antecedents of customer partnering behavior in logistics
outsourcing relationships. ........................................................................................... 72
Figure 6. Sub-model of antecedents of dependence. .................................................. 84
Figure 7. Sub-model of the antecedents of trust ......................................................... 93
Figure 8. A model of the determinants of customer partnering behavior in logistics
outsourcing relationships .......................................................................................... 101
Figure 9. Ganesan’s (1994) model of long term orientation..................................... 103
Figure 10. New variables introduced in the model of customer partnering behavior104
Figure 11. Model of customer partnering behavior in logistics outsourcing
relationships. ............................................................................................................. 109
Figure 12. Daily counts of survey completion.......................................................... 137
Figure 13. Statistically significant path coefficients................................................. 152
Figure 14. A simplified model of customer partnering behavior in logistics
outsourcing relationships .......................................................................................... 161
1
Chapter 1: Introduction
“Companies will be looking for more flexibility and service
partners who can allow them to focus on their core business and
spend less time managing the supply chain.” (Dan Colleran,
President Suddath Logistics Group, Inbound Logistics, July
2003, p. 102)
“Shippers who work with their carriers will get the trucks. Those
who don’t will pay much more for transportation.” (Lance
Craig, Inbound Logistics, January 2006, p. 166)
Outsourcing logistics functions to third-party logistics providers (3PLs) -
independent firms that perform single or multiple logistics services on behalf of a
shipper (Sink et al 1996) - is not a new phenomenon. For decades, firms have
outsourced transportation and warehousing activities, and more recently have started
to purchase complex and customized services, such as consulting for supply chain
solutions and customer service management. Many advantages accrue from logistics
outsourcing. 3PLs can provide logistics expertise and cost benefits to their customers,
since firms that outsource logistics services do not have to spend large amounts of
capital to own and manage expensive assets, such as trucks and warehouses
(Bolumole 2003). In addition, 3PLs can also offer advantages of economies of scale,
since they may use the assets more efficiently by sharing them among many
customers.
A strong trend in the logistics outsourcing industry refers to the change in the
nature of the relationship between 3PLs and their customers; i.e., the buyers of their
services (Leahy et al 1995). Due to globalization, many 3PL customers face greater
competition and more rapidly changing customer needs. These factors strengthen the
2
pressures for cost-reduction and increased customer service levels through the pursuit
of operational efficiencies, introduction of new products, and improved product
quality. In addition, expanding their business geographically through sourcing,
manufacturing and distributing overseas – which means longer distances, language
barriers, different regulations, etc – has brought increased complexity to logistics
operations and coordination of supply chains. Moreover, recent capacity constraints,
in terms of port congestion and restricted transportation supply, have imposed extra
burdens on logistics managers. This challenging reality has brought the need for firms
to change the nature of their relationships with their 3PLs in order to focus on their
core competencies and compete in today’s global markets. But 3PLs, as well, face
many challenges. The 3PL industry has already grown to a considerable size
(Berglund et al 1999). In the U.S., for example, it was estimated that payments for
3PLs services exceeded US$ 103 billion in 2005
(http://www.3plogistics.com/3PLmarket.htm).The landscape of their market has
continuously changed. Larger 3PLs have merged and have expanded their operations
geographically (Lieb and Bentz 2005). New 3PLs have entered the market, with
origins from the most unexpected areas, such as information technology and
consulting (Berglund et al 1999). Due to capacity constraints, efficiently coordinating
the operations of customers has become increasingly complex. While encountering
continuous pressures to reduce prices, offer supplementary services, and expand
geographic coverage, 3PLs face rising fuel prices and operating costs. 3PLs, then,
also understand the need to collaborate with their customers. This is reflected in 3PL
advertising campaigns, in which they emphasize their role as reliable partners. In
3
short, both 3PLs and customers of their services have realized the need to adapt to a
new business environment. One way they both can do this is by developing long-
term, collaborative relationships.
Accordingly, the changing nature of logistics outsourcing relationships has
received much attention in the academic logistics literature, including the redefinition
of “3PL.” The “modern” definition of 3PL follows Murphy and Poist (1998), who
define third-party logistics as “a relationship between a shipper and third party which,
compared to basic services, has more customized offerings, encompasses a broader
number of service functions, and is characterized by a longer-term, more mutually
beneficial relationship.” The 3PL literature, based mainly on case studies and surveys
with 3PLs and their customers, has largely emphasized the importance of nurturing
close relationships between 3PLs and their customers. For example, Leahy et al
(1995) surveyed 3PLs and considered customer orientation and dependability as the
most important determinants of successful relationships. Larson and Gammelgaard
(2001), based on a survey with logistics providers and with case studies, found
evidence that close collaboration between buyers, suppliers, and 3PLs provided
benefits, such as greater flexibility, higher inventory availability, and more on-time
pick up and delivery.
The few studies with theory-testing in the 3PL literature have also emphasized
that relational elements are important for satisfactory logistics outsourcing
relationships, and ultimately for achieving higher performance. Knemeyer et al
(2003), for example, found that customers whose relationships with 3PLs involve
higher operational and strategic integration, exhibit higher levels of key relationship
4
marketing elements, such as trust in the partner, commitment with the relationship,
and dependence on the partner. These “closer relationships” also exhibited higher
levels of relationship marketing outcomes, such as customer retention, and referrals
of the 3PLs to other potential customers. Knemeyer and Murphy (2004)
complemented their previous findings, showing that relationship marketing elements
had a stronger impact on marketing outcomes than the effects of firm characteristics,
such as size and number of functions outsourced. Positive effects from engaging in
relationships with 3PLs were also found by Panayides and So (2005). They found that
firms that engaged in relationships with 3PLs with higher levels of trust, bonding,
communication, shared value, empathy, and reciprocity, developed higher levels of
key organizational capabilities, such as organizational learning and innovation,
promoting an improvement in supply chain effectiveness and performance. Sinkovics
and Roath (2004) also found a positive effect of customer collaboration with 3PLs on
a customer’s market and logistics performance. In the same manner, Stank et al
(2003) showed a positive impact of relational performance between a 3PL and its
customer on the customer’s market share. From the examples above, it can be noted
that the relationships between 3PLs and their customers differ in terms of operational
and strategic integration, and that, in general, closer relationships lead to greater
benefits to the parties involved.
It is relevant to note that, although the measures adopted to describe the nature
of the relationships between 3PLs and their customers differ across studies, they all
convey the same concept: a “close relationship.” All of the measures capture, to some
extent, dimensions of a relational exchange. A relational exchange differs from a
5
discrete exchange in the sense that it extends over time, and the participants can be
expected to engage in social exchange and derive non-economic, personal
satisfaction. Relational exchanges, however, can be translated into patterns of
behavior. One construct that captures these facets of relational behavior is partnering
behavior. Partnering can be defined as an “on-going relationship between two firms
that involves a commitment over an extended time period, and mutual sharing of
information and the risks and rewards of the relationship” (Ellram and Hendrick
1995). Partnerships are a “hybrid” governance mechanism in which the coordinative
forces include trust and commitment (Rese 2006). Partnering behavior exhibits the
characteristics of joint-planning, sharing benefits and burdens, extendedness,
systematic operational exchange, and mutual operating controls (Gardner et al 1994).
Using the concept of partnering, the objective of this dissertation is to
complement the existing theory-based 3PL literature (that focuses on the outcomes of
close relationships) and, with a theoretical framework, answer the following research
questions:
1) For firms already outsourcing logistics services, under what
conditions will they be more likely to exhibit a partnering behavior
with their 3PLs?
2) What is the interplay between environmental forces,
interorganizational conditions and firm specific factors in shaping
such behavior?
6
3) Which factors have a stronger effect on shaping this behavior? Are
interorganizational conditions created in the relationship stronger
predictors than customer-specific factors?
In order to answer these questions, a relationship marketing perspective is
adopted. Relationship marketing, often referred to as a “major shift in marketing
theory and practice” (Rao and Perry 2002), is a widely used perspective in marketing
research that investigates the creation, development, and maintenance of committed,
interactive, and profitable relationships with selected partners over time (Harker
1999). Given that the objective of relationship marketing is to establish, develop and
maintain successful, mutually beneficial relational exchanges (Morgan and Hunt
1994, Hewett and Bearden 2001), bringing relationship marketing to 3PL research is
an appropriate theoretical perspective to investigate the determinants of partnering.
The relationship marketing perspective draws on many theories and schools of
thought, among which social exchange theory (SET) is used to investigate the
formation and dynamics of relational exchanges (Rao and Perry 2002). According to
social exchange theory, relationships are developed when exchange partners perceive
that they accrue higher rewards from the relationship than would be possible outside
the relationship (Thibaut and Kelley 1959). Dependence on the partner and trust in
the partner are considered the main antecedents of relational exchanges (Lambe et al
2001). In addition, satisfaction with previous outcomes of a relationship has been
shown to impact a relationship’s continuity and development (Ganesan 1994, Dwyer,
Schurr and Oh 1987). These three factors are captured in the seminal marketing piece
of Ganesan (1994), who investigated the determinants of long-term orientation in
7
buyer-seller relationships; i.e., the perception that the relationship outcomes are
expected to benefit the exchange partners in the long run.
Following the foundational premises of relationship marketing and social
exchange theory, this dissertation builds upon and extends Ganesan’s (1994) model
into the context of logistics outsourcing relationships. More specifically, following
Ganesan, it is hypothesized that a 3PL customer’s partnering behavior is positively
influenced by: 1) a customer’s dependence on a 3PL, 2) a customer’s trust in a 3PL,
and 3) a customer’s satisfaction with a 3PL. However, it is hypothesized that these
three factors are not sufficient to explain the customer’s partnering behavior. SET
predicts that the relationship dynamics are the major forces in explaining relationship
development, but a partner’s particular history (e.g., Uzzi 1996, Ho et al 2003) and
internal orientation (e.g., Bolumole 2001, Sin et al 2005a) may affect relationship
behavior as well. Firms that have had earlier partnership-type outsourcing
relationships may have developed a capability that facilitates partnering with the
current 3PL. Moreover, according to the relationship marketing literature, firms may
have unique strategic orientations towards engaging in relationships with main
stakeholders (e.g., customers, partners), or a relationship marketing orientation, that
might also influence the decision to engage in partnerships with the 3PL.
In order to test the model briefly described above, the customers of a large
Brazilian 3PL provider called Rapidão Cometa are surveyed. Rapidão Cometa
(www.rapidaocometa.com.br) is an asset based company with broad geographical
coverage both in Brazil and overseas through an operational alliance with a global
logistics provider (i.e., FedEx). The firm has been in business for over 60 years, and
8
has over 3,000 employees and 7,000 active customers. It provides an entire array of
logistics services, ranging from simple transportation and warehouse management to
customized consulting for supply chain solutions. It also utilizes information
technology in the provision of services, such as electronic data interchange - EDI and
warehouse management systems – WMS. Its customer base is composed of small and
large firms from a variety of industries, such as apparel, auto, and electronics, among
others. Given its strong reputation, logistics capabilities, and its diverse customer-
base, Rapidão Cometa is an appropriate source of information to address the research
questions proposed in this dissertation.
This dissertation contributes to the logistics and marketing literatures and to
managers as well. Contributions of this dissertation include:
1) Contributing to the 3PL literature by developing and testing a
theoretically-driven model. As noted above, few examples in the
literature on logistics outsourcing relationships have used hypotheses
testing, and even fewer articles have been built on theory;
2) Extending Ganesan’s model by: 1) including new explanatory
variables using different theoretical perspectives; 2) including other
dimensions of relational exchange in the dependent variable (long-
term orientation in Ganesan’s (1994) model is one dimension of
partnering behavior);
3) Identifying whether a firm’s particular experience with partnering or
its specific orientation towards partnering with stakeholders is a
stronger predictor of its partnering behavior than interorganizational
9
factors, such as trust in the partner, satisfaction with the partner, and
dependence on the partner;
4) Using structural equation modeling (SEM), a powerful multivariate
technique that can be used to investigate relationships among latent,
unobserved variables. SEM is a more advanced analytical approach
than those commonly in use in 3PL research, such as percentages or
means testing. This provides a contribution to the 3PL literature, in
which many papers lack a “formalized, advanced methodological
approach” (Maloni and Carter 2005);
5) Expanding the geographical coverage of 3PL research by collecting
data from Brazil, an important market with strong growth potential. As
Maloni and Carter (2005) point out, “much of the existing 3PL
research assessed one geographical region, generally the United
States.” Other studies, however, have focused on Western Europe, the
United Kingdom, the Netherlands, Australia, China, Singapore and
Malaysia. An extended geographical scope in 3PL research can be
beneficial, especially for practitioners given the importance of Brazil
as an important U.S. trade partner. Also, since many constructs have
been already tested with U.S. firms, there is an opportunity for future
cross-cultural comparison studies;
6) And for managers, considering the performance benefits of close
relationships for 3PLs and customers, the identification of what factors
have a stronger effect on a customer partnering behavior can guide
10
3PL managers on the nurturing of partnerships with their customers,
thus helping them maintain and develop their customer base.
The structure of this dissertation is as follows. Chapter 2 presents a literature
review of the main research streams that are related to this dissertation, including
logistics outsourcing, partnerships, relationship marketing, and a brief description of
Ganesan’s model of long-term orientation in buyer-seller relationships. Chapter 3
presents the conceptual model and describes the rationale for the hypotheses in detail.
Chapter 4 describes the methodology to be undertaken in order to measure the
constructs, survey design and implementation, and data collection. Chapter 5 presents
the steps followed in the data analysis and the model results. Finally, Chapter 6
presents a discussion of the findings of this dissertation, contributions, limitations,
and avenues for future research.
11
Chapter 2: Literature Review
In order to understand in greater detail the model proposed in this dissertation,
this chapter provides an overview of the various areas related to the research question,
as well as a theoretical background for the hypotheses development, the subject of
Chapter 3. The first section describes the literature in logistics outsourcing, with a
focus on the relationships between 3PLs and their customers. The second section
provides the definition of customer partnering behavior and a brief overview of the
various research streams in the logistics, marketing, and strategy literatures that have
investigated the formation of “hybrid governance structures”, of which partnerships is
one type. The third section presents an introduction to relationship marketing, with
special attention to social exchange theory, the theoretical perspective that serves as
the basis for the development of the model. Next, Ganesan’s (1994) model is briefly
described, along with the literature that has extended his work. Finally, some
comments on the effects of cultural differences in logistics outsourcing are addressed.
2.1. Logistics outsourcing
This section provides an introductory overview of logistics outsourcing
concepts, industry trends, and academic research. First, due to the various
terminologies used in the literature, the definition of third-party logistics providers
(3PL) is provided, along with a brief characterization of the 3PL industry and its main
trends both in North America and in Brazil, where the data were collected. Finally, an
overview of the main research questions that have been addressed in the 3PL
literature is presented with a focus on the empirical work dedicated to 3PL-customer
12
relationships (see Razzaque and Sheng 1998, or Maloni and Carter 2005 for
comprehensive literature reviews on 3PL research).
2.1.1. Third-party logistics (3PL) providers defined
The involvement of 3PLs in the supply chain is becoming increasingly
necessary for a firm’s survival in the global and competitive environment (Bask
2001). Increased competition and globalization, and the need to reduce cycle times
and inventory levels, have created a need for more responsive processes based on
efficient supply chain partnerships. These pressures have encouraged management to
re-examine a firm’s individual and collective positions within the supply chain, and
have increased the interest in outsourcing a broad array of logistics services.
Outsourcing logistics services to 3PLs has become not only a means to cost-
efficiency, but also a strategic tool for creating competitive advantage through
increased service and flexibility (Skjoett-Larsen 2000).
3PLs are independent firms that provide single or multiple logistics services
on behalf of a shipper (Sink et al 1996, Berglund et al 1999). For example, they can
just provide transportation services, or, conversely, a broad array of logistics services,
such as customs clearance, information technology (IT) based services for inventory
and customer management, and consulting for supply chain solutions. The concept of
3PL, however, is often not well-defined, either in the academic or the industry
literature. The earlier definitions of 3PL do not consider a crucial element of the
current state of logistics outsourcing: the nature of the relationship between the
provider and the customer (Murphy and Poist 2000). The clear trend in the literature
is towards the notion that “modern” 3PL logistics involves long-term, mutually
13
beneficial relationships (Leahy et al 1995, Papadoupoulou and Macbeth 2001).
Therefore, the definition of 3PL adopted for this dissertation follows Murphy and
Poist (1998) and considers that third-party logistics involves “a relationship between
a shipper and a third-party which, compared to basic services, has more customized
offerings, encompasses a broader number of service functions and is characterized by
a longer-term, more mutually beneficial relationship” (p. 26). The characteristics of
long-term and mutual benefits are in line with the concept of partnering, the focus of
this dissertation.
2.1.2. An overview of the 3PL industry
The main firms in the 3PL industry come from a variety of backgrounds, but
can be categorized into three groups (Berglund et al 1999): 1) traditional
transportation companies that have expanded their services into logistics; 2) parcel
and express companies (e.g., DHL, TNT, UPS), that entered the logistics market
based on their worldwide networks and experience with expediting freight; and
finally 3) players from other areas, such as information technology, management
consulting, and financial services.
The 3PL industry has achieved significant growth over the past several years
(Berglund et al 1999). Although no official statistics are available, it is estimated that
the U.S. 3PL/contract logistics market has grown from approximately US$ 31 billion
in revenues in 1996 to US$ 85 billion in 2004 (Capgemini et al. 2005). This trend is
mirrored by the number of firms outsourcing logistics services. Lieb and colleagues
(1999, 2003, 2005) performed annual surveys of large U.S. manufacturers. The
results show that the percentage of firms using 3PL services has grown from
14
approximately 65% in 2003 to 80% in 2004. This finding is consistent with the
findings of the annual surveys conducted by Langley Jr. with industry partners (e.g.,
Capgemini et al. 2003, 2004, 2005). They have found that the percentage of 3PL
users has increased from 72% in 2000 to 80% in 2005. Concurrent with its growth,
the 3PL industry has also experienced fundamental changes. There are more
competitors in the market. The array of services provided by 3PLs has increased in
response to customer desires for one-stop shopping. Aside from the traditional service
offerings of warehousing and outbound and inbound transportation services, other
frequently outsourced activities are customs brokerage, customs clearance, and
freight forwarding. Other more complex activities are also outsourced, including
those directly related to customers (e.g., order fulfillment, customer service and order
entry/processing), information technology (IT), and strategic services, such as
consulting, procurement of logistics, and 4PL services
1
(Capgemini et al. 2005). In
addition, pressures for reducing prices, providing multiple services, and expanding
geographical coverage, have forced 3PL providers to engage in mergers, acquisitions,
and/or strategic alliances (Foster 1999, Lieb 2003, 2005
. As a result, 3PLs face the
challenges of working closely with their partners. Moreover, major 3PLs have
become more selective about customers and have shifted their focus towards longer-
term relationships (Lieb 2005), with greater emphasis on the overall logistics
processes rather than on isolated task-based operations (Eyefortransport 2006). In this
dynamic and challenging environment, the 3PL industry offers relevant research
opportunities in the logistics and supply chain management arenas.
1
4PL can be defined as an integrator that combines its own resources with other organizations’
resources in order to design, build, and run comprehensive supply chain solutions.
15
2.1.3. Logistics outsourcing in Brazil
The state of logistics outsourcing in Brazil is, in many ways, similar to what is
found in the U.S. Although Brazil is a smaller market, there are similarities in terms
of challenges 3PLs face and in industry trends. Local 3PLs have merged and allied
with global 3PLs in their search for larger market shares and broader geographical
coverage. Although no official statistics are available, according to the Brazilian
magazine Tecnologística
2
, there are about 200 3PLs operating in Brazil, realizing in
2001 approximately US$ 2.36 billion in total revenues (www.guiadelogistica.com.br).
In Brazil, a great variety of industries outsource logistics services (e.g., chemical,
pharmaceutical, electronics, furniture, apparel, wholesaling, and retailing), reflecting
a diversity in terms of logistics complexity (COPPEAD and Booz-Allen, 2001).
About 90% of the Brazilian 3PLs have roots as companies that provide basic
transportation and warehousing services. Although some of these firms have
increased their portfolios of services offered, many still offer only the basic services;
i.e. transportation and warehousing. More recently, large American and European
providers have entered the Brazilian market. About 70% of the logistics providers are
asset-based firms and have grown, in part, due to the absence of a good public
warehouse infrastructure, and their willingness to provide reliable transportation
services.
In 2001, the consulting firm Booz Allen and the Brazilian academic institution
CEL/COPPEAD (Center for Logistics Studies at the Business Graduate Studies and
Research Institute at the Federal University of Rio de Janeiro) conducted a study of
2
According to Tecnologística magazine, a 3PL “provides services related to the logistics area.”
16
the contract logistics market in Brazil. From a survey of 67 3PLs and additional in-
depth interviews, they identified many challenges facing the 3PL industry in Brazil
(Booz Allen 2001): First, the substantial differences in taxes among the different
regions and states of Brazil hinder the optimization of logistics networks. Another
major problem refers to the poor transportation (both in terms of physical conditions
and security) and public warehousing infrastructures, which diminishes the ability of
3PLs to operate efficiently. Another barrier to the expansion of the industry is the
lack of qualified human resources in logistics. More importantly, Brazilian 3PLs
complain about the lack of customer maturity; i.e., customers not being able to
specify expectations and needs. Customers, on the other hand, argue that 3PLs are not
able to meet their expectations. This disagreement between 3PLs and their customers
is an indicator that the “culture of customer-3PL collaboration” may not be mature;
i.e., that there is room for 3PL-customer relationships to develop. As such, compared
to the U.S. 3PL industry, one might expect fewer long-term relationships, and fewer
activities outsourced in Brazil.
2.1.4. Logistics outsourcing research
The academic 3PL literature is primarily based on surveys and case studies
that capture customer and 3PL perspectives on the following topics (Razzaque and
Sheng 1998): the current and future state of the 3PL industry (Murphy and Poist
1998, Berglund et al 1999, Sum and Teo 1999); identification of drivers for
outsourcing, the extent of logistics outsourcing, enablers and hinderers of logistics
relationships (Wilding and Juriado 2004); and the investigation of the dynamics of
logistics outsourcing relationships; i.e., how relationships grow and what factors
17
affect their evolution and decline (Knemeyer 2003, 2004, 2005). Overall, the articles
emphasize the growing potential of the industry and the benefits to supply chains
through logistics outsourcing, not only as a means to cost-efficiency, but also as a
strategic tool for creating competitive advantage through increased service and
flexibility (Skjoett-Larsen, 2000).
As the following paragraphs show, much of the academic 3PL literature has
been exploratory in nature. There have been few examples of theory testing (Maloni
and Carter 2005), indicating an opportunity gap for advancement of theory and status
of academic work in this field. At this point, it is relevant to note that empirical work
in the 3PL industry presents many challenges. First, the size of the industry is difficult
to estimate, since governmental statistics are often not available. Also, many
providers are part of larger companies that do not break out data on subsidiaries
(Berglund et al 1999). Another problem relates to the confusion regarding
terminology. As Skjoett-Larsen (2000) states, new concepts, such as third-party
logistics, are characterized by multiple definitions (i.e., some researchers consider any
transportation carrier as a logistics provider whereas others include only providers
that offer a larger array of services). Berglund et al (1999), for example, mention that
many transportation companies call themselves logistics companies, or even supply
chain partners.
The current and future state of the industry. A number of articles focus on
describing the current practices and trends in logistics outsourcing from both the 3PL
and the customer perspectives. Longitudinal studies conducted by Lieb et al (e.g.,
1999, 2003, 2005) and Langley (Capgemini et al 2003, 2004, 2005), with the support
18
of industry partners, reveal relevant information. The industry has grown
continuously over the past several years, and the number and complexity of the
functions outsourced has increased. 3PL CEOs consider supply chain integration as
the most significant opportunity for 3PL providers (Lieb and Kendrick, 2003). Users
are generally satisfied with their relationships with 3PLs, but point out that some
areas should be improved, especially those related to advanced services, such as
technology innovation (Capgemini et al 2005). In this sense, some observers predict
that the 3PL entrants that emerged from the information technology and consulting
areas may be more likely to have greater competitive advantage due to their skills in
assisting supply chain optimization and integration activities (Berglund et al 1999).
Overall, the 3PL industry may be reaching maturity as 3PLs start to focus activities
on market segmentation (Lieb 2005).
Drivers and extent of logistics outsourcing. Frequently cited primary reasons
for outsourcing logistics functions include (Boyson et al 1999, Maloni and Carter
2005): cost reduction, service improvements and efficiency, and focus on core
competencies. In order to achieve these objectives, logistics outsourcing can occur at
different levels, both in terms of scope of logistics activities to be outsourced and
degree of integration between the 3PL and the buyer of the service. In this matter, a
common research stream in the 3PL literature comprises the investigation of the types
of relationships between 3PL providers and customers (Knemeyer et al 2003), and
normative frameworks regarding the make or buy decision related to logistics
activities; e.g., the steps and factors relating to the decision to outsource (Sink and
19
Langley, Jr , 1997, Maltz and Ellram, 1997), or the decision as to what kinds of
services 3PLs should provide (Hanna and Maltz, 1998).
The variety of existing outsourcing relationships was captured by Knemeyer
et al (2003). From a survey of logistics managers across the U.S. compiled from a
trade magazine subscriber list, the authors found that more developed partnerships (in
which more operational and/or strategic integration is in place) exhibit higher levels
of relationship marketing elements (commitment, investment, dependence,
communication, attachment, reciprocity) and outcomes (retention, referrals,
recovery). Overall, many factors may impact the role that 3PL providers have on
customer operations and strategies. Bolumole (2001), for example, examined 3PL
relationships in the UK petrol industry. She identified four factors that determine the
supply chain role of 3PL providers: 1) the competitive strategic orientation of the
outsourcing organization, which influences the firm’s logistics strategy; 2) the focal
firm’s perception of the 3PL role within the logistics strategy; 3) the nature of the
3PL-customer relationship (adversarial versus collaborative), and; 4) the extent to
which logistics functions are outsourced. Rabinovich et al (1999) surveyed 372
logistics managers and their results clarified different patterns of choice of logistics
functions to be outsourced. They found that firms commonly bundle transactional and
physical functions within inventory and customer service areas, with the purpose of
achieving economies of scale (efficiency) and improving customer service levels
without committing significant amounts of financial resources.
Regarding the process of outsourcing, Sink and Langley, Jr (1997), for
instance, provided a framework to guide industrial buyers in the purchasing process
20
of third-party logistics services. The proposed process contains five steps: to identify
need to outsource logistics, to develop feasible alternatives, to evaluate and select the
supplier, to implement the service, and to assess the ongoing service. Maltz and
Ellram (1997) proposed an analytical framework for the logistics outsourcing
decision based on the concept of “total cost of relationship”, an adaptation of the total
cost of ownership (TCO) procedures, traditionally used by manufacturers to
incorporate non-price considerations into the make or buy decision. Meade and Sarkis
(2002) developed a methodology to select and evaluate third-party reverse logistics
providers. It consists of a decision network hierarchy, in which elements related to the
product life cycle, the reverse logistics functions, organizational performance criteria,
and the organizational role of reverse logistics, along with their relative importance,
are simultaneously considered. Bask (2001) provided a strategic perspective on the
relationships among 3PL providers and members of supply chains. Translated into a
normative framework, she proposes that the purchased logistics services should
match the supply chain strategies employed by 3PL customers. She argues that if, for
example, a firm has a full speculation supply chain strategy
3
, it will be better off
purchasing routine logistics services since it requires less coordination with a 3PL.
Conversely, firms that employ manufacturing postponement
4
, which is operationally
more challenging, should purchase customized logistics services. Hanna and Maltz
(1998), focusing on the 3PL provider perspective, used transaction costs economics to
investigate the specific decision of Class I less-than-truckload (LTL) carriers to
3
In a full speculation strategy, manufacturing is centralized, goods are produced to inventory, and
finished products are stocked close to customers.
4
In a manufacturing postponement strategy, final manufacturing operations occur after the customer’s
order is placed. Early stages of manufacturing are centralized, and final manufacturing operations
occur in locations close to the customer.
21
expand into warehousing. They found that increased asset specificity (e.g., warehouse
with special store equipment, or with a strategic location) is associated with the
probability of warehouse ownership, and that larger carriers are more likely to own
warehousing assets used to expand their business.
Success factors / Barriers. Of great interest are the factors that contribute to
the success (or lack thereof) of logistics outsourcing relationships. Surveys and case
studies are the common methods utilized in such studies and the results obtained are
usually descriptive. Success factors are found to be present not only during, but
before the initiation of the outsourcing relationship. Factors that are cited as
determinants of successful relationships relate to the importance of the customer in
clearly specifying expectations prior to the relationship and in developing and
monitoring performance metrics (Boyson et al 1999, van Laarhoven et al 2000).
Boyson et al (1999), for example, in a survey of logistics managers across the U.S.,
emphasized the crucial importance of the contracting agreements and the need to have
in-house knowledgeable managers to audit and monitor 3PLs. Sink et al (1996) made
use of a focus group of experienced customers to capture observations of the U.S.
third party logistics market. They highlighted the importance of understanding the
various interests in contracting logistics in order to implement an efficient and
effective marketing strategy. Developing and monitoring performance metrics are
indeed very important. Through a telephone survey of third party logistics providers,
van Hoek (2001) found empirical support for the contention that performance
measurement contributes to the expansion of third party logistics alliances in terms of
offering supplementary services (e.g., product configuration, packaging, etc).
22
Aside from performance monitoring, the willingness to collaborate and
communicate with the 3PL is mentioned as a key element for relationship success.
Leahy et al (1995) surveyed fifty-one 3PLs and found that customer orientation and
dependability are the most important determinants of successful logistics outsourcing
relationships. Murphy and Poist (2000) investigated perspectives of both 3PL
providers and users. Although both providers and users expressed high levels of
satisfaction with 3PL-customer relationships, the authors noted that there is room for
improvement. They came to the conclusion that there is an apparent mismatch
between the services offered by the 3PLs and the logistics services required by 3PL
customers. This result reinforced the importance of ongoing communication between
both parties. In this respect, effective and ongoing communication is key for
anticipating customer needs and delivering solutions to problems when they emerge.
The role of information technologies (IT), including hardware, databases,
software, and other devices that support any information systems, has also been
presented as a crucial capability that enhances the 3PL-customer relationship. Lewis
and Talalayevsky (2000), for example, emphasized that global competition and the
rapid evolution of IT have contributed to the significant trend toward outsourcing of
logistics services among major U.S. firms. They highlighted how information
technology has allowed users of logistics services to focus on their core competencies
(e.g., manufacturing, marketing, etc). Using case studies with three logistics
providers, van Hoek (2002) demonstrated that technology impacts operational
relations in the supply chain and helps 3PLs improve their operations offerings.
Sauvage (2003), in a survey of French logistics service providers, showed that the
23
success of logistics outsourcing relationships is enhanced by the 3PL’s technological
ability to improve supply chain reactivity in industries immersed in a competitive
context characterized by “time compression” (i.e., shorter product life cycle times,
shorter order cycle times, etc).
Dynamics of logistics relationships. Some researchers have relied on theory
to develop models and test propositions related to the dynamics of logistics
outsourcing relationships; i.e., how these relationships evolve and what factors
influence their development. One example of a conceptual, theory-based article is
Skjoett-Larsen (2000), who viewed third party logistics from an interorganizational
point of view, using network theory to develop (but not test) propositions about the
dynamics in third party cooperation. From three case studies, Skjoett-Larsen
emphasized the importance of both exchange (e.g., technical, information, and social)
and adaptation processes (e.g., mutual modification of systems and operations) in
developing a relationship, since past and present experiences play a major part in the
development of third party cooperation. Another example of a theory-based research
can be found in Hertz and Alfredsson (2003). Adopting a social network perspective,
and using three case studies on new entrant 3PL providers and their customers, they
showed that 3PLs are influenced by customers’ customers in the development of their
business.
Empirical tests of propositions derived from theory are relatively rare in 3PL
research. van Hoek (2000) built a transaction cost economics (TCE) framework to
test propositions related to the governance structure of 3PL-customer relationships,
including the types of services, contracts, frequency of communication at different
24
organizational levels, frequency of reports, and content of coordination and
communication. He found that the offering of supplementary services, relationship
coordination, and frequency of contact are positively associated with detailed
contracts. In later research, through a telephone survey of third party logistics
providers, van Hoek (2001) found empirical support for the hypothesis that
performance measurement contributes to the expansion of third party logistics
partnerships. Another example of empirical tests of propositions can be found in
Moore (1998), who tested a model of logistics alliances from a 3PL customer
perspective. His results indicated that 3PL customers who perceive 3PLs to be
trustworthy were committed to maintaining the alliance relationships, thus decreasing
the risk of opportunism.
Knemeyer and Murphy (2004) adopted relationship marketing as a theoretical
basis and found linkages between relationship marketing activities and the perceived
performance of 3PL arrangements. More specifically, the levels of trust and
communication were found to influence customer perspectives of various 3PL
performance factors, such as operations performance and channel performance. In a
later study, Knemeyer and Murphy (2005) investigated the impact of select
relationship characteristics (e.g. communication, reputation) and customer attributes
(e.g. size, number of functions outsourced) on 3PL relationship outcomes (e.g.
customer retention, service recovery). They found that relationship characteristics
have a stronger impact than customer attributes on relationship outcomes, reinforcing
the importance of nurturing relationships regardless of the type and size of customer.
25
Sinkovics and Roath (2004), adapting the structure-conduct-performance
paradigm, found that internal capabilities (operational flexibility and cooperation)
mediate the relationship between two dimensions of firm strategic orientation
(competitor and customer orientation) and customer market performance. Although
they obtained mixed results for their hypotheses, operational flexibility was the most
salient capability, and it augments competitor orientation to impact logistics and
market performance. Positive effects of engaging in relationships with 3PLs were also
found in Panayides and So (2005). They found that relationship orientation, measured
in terms of trust, bonding, communication, shared values, empathy and reciprocity,
had a positive influence on key organizational capabilities, such as organizational
learning and innovation, thereby promoting an improvement in supply chain
effectiveness and performance. A similar result was found by Stank et al (2003), who
showed a positive impact from the relational performance between a 3PL and its
customers on the customer’s market share.
From the examples presented above, it can be seen that most work on 3PLs
has focused on exploratory surveys, case studies, or conceptual frameworks to guide
users and providers on the processes of the decision to outsource, what functions to
outsource, selection of the provider, and maintenance and monitoring of the
relationship. Theory grounded, or empirically tested research, appears with much less
frequency in the literature. It can also be noted that research has shown that
relationships between 3PLs and customers differ in terms of the functions that 3PLs
provide and in terms of operational and strategic integration. It is also shown that, in
26
general, collaborative and interactive relationships exhibit higher satisfaction and
performance.
Although the motivations to outsource seem to be consistent across studies, an
overall theoretical framework of the conditions under which closer, collaborative
relationships between 3PLs and customers will more likely occur remain unexplored.
There is much research in marketing and strategy that has investigated the formation
of interorganizational relationships, and, more specifically, interorganizational
relational exchanges and partnering. Bringing these perspectives and applying them
to the 3PL literature is, therefore, one of the main contributions of this dissertation.
2.2. Partnering
This subsection discusses relevant aspects of the partnering behavior, the
focus of this dissertation. Initially, customer partnering behavior is defined. Next, a
brief discussion of the concept of partnering is provided, distinguishing partnering
from other ‘hybrid’-type relationships. Finally, research on antecedents and outcomes
of partnering is reviewed.
2.2.1. 3PL-customer partnership defined
The pressures of increasing global competition and rapidly changing customer
tastes and preferences have turned the integration and control of the supply chain and
logistics functions into a critical activity for enterprises. In order to achieve supply
chain coordination and integration, scholars and practitioners have emphasized the
strategy of developing and nurturing long-term cooperative partnerships between
supply chain members. The literature generally supports the ability of partnerships to
27
achieve cost savings, and as a result partnerships are increasingly cited as a common
source of efficiency and competitive advantage (Gentry and Vellenga 1996, Mentzer
et al 2000, Duffy and Fearne 2004).
Partnerships are a “hybrid” governance mechanism in which the coordinative
forces include trust and commitment, in addition to price (Rese 2006). The
partnership concept is borrowed from the relational contracting literature (e.g.,
Macneil, 1978, Dwyer et al 1987) and encompasses dimensions of relational
governance (Joshi and Campbell, 2003), in which participants engage in social
exchange and take not only economic, but also non-economic, social benefits into
consideration. In a partnership, as with any relational exchange, each transaction is
viewed in terms of its historical context and its anticipated future prospects (Kim and
Chung 2003); i.e., as opposed to a discrete exchange that is relatively short term with
limited communication. As well, with partnerships, as in relational exchanges,
relational norms or expectations of behavior are developed over time. The expectation
of continuity and/or the relational norms act as controls against possible opportunistic
behaviors. Trust, commitment, and exchange norms complement more formal
mechanisms, such as detailed contracts.
The exact definition of partnership, however, is not trivial, as can be noted in
the academic literature:
- Mohr and Spekman (1994) define partnerships as “purposive strategic
relationships between independent firms who share compatible goals, strive
for mutual benefit and acknowledge a high level of interdependence” (p. 135);
28
- Gardner et al (1994) have a broader perspective on the concept and consider a
partnership as the “relational contract” in Macneil’s (1980) language; i.e., a
relationship style present within the continuum of interorganizational
relationships from arm’s length to vertical integration;
- Ellram and Hendrick (1995) define partnering as an on-going relationship
between two firms that involves a commitment over an extended time period
with a mutual sharing of information, risks, and rewards from the relationship;
- Lambert et al (1996) classify partnerships into three types. In type I
partnerships, firms recognize each other as partners and coordinate activities
and planning on a limited basis. Type II partnerships are related to firms that
have moved from simply coordinating activities to the integration of activities
with a longer term orientation and involving multiple areas within the firm.
Finally, firms involved in type III partnerships share a significant level of
operational and strategic integration;
- Mentzer et al (2000) distinguish between strategic and operational partnering.
While strategic partnering is an “on-going, long-term interfirm relationship for
achieving strategic goals, which delivers value for customers and profitability
to partners,” operational partnering is an “as-needed, shorter term relationship
for obtaining parity with competitors.” An operational partnering orientation
seeks improvements in operational efficiency and effectiveness;
- Rinehart et al (2004) classify partnerships as a “hybrid” system that is
contained within a range of relational governing systems (from informal
agreements to franchising), and is differentiated from mere activity-based or
29
functional systems for its emphasis on relational characteristics that guide the
actions of parties. They argue that there are key distinguishing attributes
among the types of partnerships in the transaction-relationship-ownership
continuum, such as trust, interaction frequency, and commitment;
- Lambert et al (2004) define a partnership as “a tailored business relationship
based on mutual trust, openness, shared risk and shared rewards that result in
business performance greater than would be achieved by the two firms
working together in the absence of partnership” (p. 22). The key point in this
definition is that the relationship is customized and cannot be uniform for all
customers, since the tailoring process consumes managerial time and effort.
From the definitions above, it can be noted that partnership agreements are
unique and possess elements of relational exchange. Gentry (1996) points out that,
although the definitions differ in the literature, partnerships usually share the common
characteristics of:
- long term commitment;
- open communications and information sharing;
- cooperative, continuous improvements in cost reductions and increased
quality;
- sharing of risks and rewards.
These partnering characteristics are among the main elements that exist in a
relational exchange (Gardner et al 1994). Therefore, “partnership” is an appropriate
and relevant construct to be investigated when studying relational exchanges. From a
3PL’s perspective, investigating the relevant antecedents to a customer’s relational
30
behavior is very useful in that a 3PL may be proactive and focus on these antecedents,
enhancing the relationship and fostering the continuity and success of these
relationships. As Ivens (2004) found in his study with members of a German market
research association, a service provider’s relational behavior exerts considerable
influence on a customer’s economic and social satisfaction.
In this dissertation, Gardner et al’s (1994) broader definition of partnerships is
adopted – “partnering behavior” can exist in different degrees at any point on the
continuum between discrete exchanges and vertical integration (see Figure 1). In their
words, “partnership would be any relationship that falls to the right of the continuum,
beyond arm’s length” (p. 122). More specifically, in this dissertation the dependent
variable is the 3PL customer’s partnering behavior which corresponds to the
customer’s perception that its relationship with a 3PL possesses the following
behavioral elements (Gardner et al 1994, p. 127):
- “planning: integration of the operations of the two firms, smoothing the
disturbances from expected and unexpected environmental factors;
- sharing of benefits and burdens: reflects the willingness of both parties to
accept short-term hardships with the expectation that the opposite party will
do the same. In this way both firms win in the long run;
- extendedness: refers to loyalty and long-term expectations of the two parties
involved;
- systematic operational information exchange refers to the systems designed to
provide accurate, concise, and usable day-to-day information transfers. These
31
systems would include automated and non-automated systems; EDI being a
good example;
- mutual operating controls: reflects each party’s willingness to allow managers
of the other party to have a meaningful say in its operations. The goal would
be to build more efficient total systems and to verify optimal performance.”
Figure 1. Continuum of relationship styles (extracted from Gardner et al 1994)
2.2.2. Distinguishing between partnerships and other
interorganizational relationships
An important point to highlight is the unclear distinction between partnerships
and other forms of relational exchanges, especially alliances. There is no agreement
in the literature as to whether these terms are synonymous or two independent
concepts. Although the distinction between partnerships and other forms of relational
behaviors is beyond the scope of this dissertation, this issue is relevant, given that
both partnerships and alliances are relational governed, hybrid systems, and the
understanding of their drivers and consequences follows the same logic. In addition,
No partnership
present
Range of relationship styles
Arm’s Length
Relationship Style, e.g.,
Commodities Markets
Just Short of
Full Vertical Integration,
e.g., Corporate
Vertical Marketing Systems
Many elements of partnership
present
32
the literature on alliances is also used in this dissertation for insights into the
development of the partnering model.
Few scholars have aimed to clarify the behavioral dimensions of partnerships,
as well as to differentiate partnerships from other relationship types. Gardner et al
(1994) identified the five partnership dimensions presented in the previous subsection
– planning, sharing of benefits and burdens, extendedness, systematic operational
exchange, and mutual operating controls - and tested for partnership as a first-order
factor. Although they were able to discriminate the partnership dimensions, their
sample was too small for statistical significance, and relatively few of the potential
influencing factors had clear, significant correlations with the overall measure of
partnership (thus testing the validity and reliability of a second-order factor for
partnerships is a contribution this research aims to make). Mohr and Spekman (1994)
argued that partnerships possess behavioral attributes, such as commitment and trust,
communication behaviors, and conflict resolution techniques.
Empirically distinguishing partnerships from other interorganizational
relationships has not proven to be an easy task. Rinehart et al (2004), for example,
explored whether different types of business relationships (e.g. non-strategic
transactions, administered relationships, contractual relationship, partnerships, joint
venture and alliances) exhibit different attribute levels (trust, interaction frequency,
and commitment) through cluster analysis and concluded that this issue is more
complex than traditional classifications would predict. The authors expected that
closer relationships would exhibit higher levels of the behavioral attributes, but this
was not found. Strategic alliances, for instance, did not exhibit higher levels of all the
33
behavioral dimensions than partnerships. As well, joint ventures exhibited lower
levels of trust, which might be indicative of why greater investments are required for
joint ventures.
In particular, the distinction between partnerships and alliances is not clear in
the literature. Partnerships are a hybrid governance mechanism in which the
coordinative forces include trust and commitment (in contrast to pure market
transactions, in which price is the coordinative force) (Rese 2006). Indeed, as Mohr
and Spekman (1994) point out, “closer, more intimate bonds are what separate these
partnerships from a more transaction-based set of exchanges which are limited in
scope and purpose.” Alliances on the other hand have been defined as “voluntary
arrangements between firms involving exchange, sharing, or co-development of
products, technologies, or services” (Gulati 1998), or “a form of inter-organizational
cooperation involving pooling of skills and resources to achieve common objectives
of alliance partners, but retaining their separate entities” (Xie and Johnston 2004).
Some researchers do not agree that alliances mean keeping separate entities and
consider joint ventures and contracting agreements (e.g., licensing, distribution etc) as
governance forms of alliances (e.g., Osborn and Baughn 1990). Zineldin and
Bredenlow (2003), for example, argue that strategic alliances encompass agreements
between firms needed to achieve some strategic objective, and can range from a
simple handshake agreement to licensing, outsourcing, and equity joint-ventures.
In many instances, partnerships and alliances terms are considered to be the
same concept (e.g., Gentry and Vellenga 1996, Wong et al 2005). However,
partnerships are often distinguished from alliances. Webster (1992), for example,
34
distinguishes partnerships from long-term relationships in the sense that, in
partnerships, cooperation substitutes for arm’s length and adversarial behaviors that
might exist in long-term relationships. Then, he distinguishes strategic alliances from
partnerships arguing that strategic alliances are an entirely new venture where
partners work towards a long-term, strategic goal. In his opinion, this strategic
objective is one distinguishing feature that separates strategic alliances from other
forms of inter-firm cooperation. Gardner et al (1994) view partnerships as a behavior
style with some behavioral elements/characteristics (planning, sharing benefits and
burdens, extendedness, operational information exchange, and mutual operating
controls). Other types of relationships possess elements of partnerships (alliances,
joint-ventures, small account selling) to a different extent. Gardner et al’s (1994) view
is adopted for this dissertation.
2.2.3. Previous research on the antecedents and outcomes of
partnering
As outlined earlier, partnerships are a hybrid form of inter-organizational
governance, in which relational behavior elements are present, and pure market forces
and prices are no longer the only controlling mechanisms. The objective of this
dissertation is to identify the antecedents of partnering behavior in the context of
logistics outsourcing. For this reason, understanding the drivers of partnering requires
a broad overview of the different research streams that have been used to investigate
the drivers, structures and outcomes of interorganizational relationships in general.
Research on interorganizational relationships has been conducted in the
marketing, strategy and logistics literatures. In all fields, researchers have drawn upon
35
various theories, such as: transaction costs economics (e.g., Osborn and Baughn
1990), resource dependency (e.g., Pfeffer and Salancik 1978, Thompson 1967),
contract law and social exchange theory (Anderson and Narus 1984, Dwyer, Schurr
and Oh 1987, Frazier 1983), and social network theory (Gulati 1998, 2000) to
investigate the drivers and selection of governance structures. Aside from specifying
the behavioral dimensions and attributes of partnerships described in the previous
subsection (Gardner et al, 1994, Ellram and Hendrick, 1995, Rinehart et al, 2004), the
main research questions addressed have been related to: partner selection (Ellram
1990, Rese 2006); partnership antecedents (Oliver 1990, Whipple et al 1996, Gulati
1998); partnership satisfaction (Anderson and Narus 1990, Walton, 1996, Mohr and
Spekman 1994 Lambert et al, 1996, 1999, 2004), and partnership performance
(Kleinsorge et al, 1991, Duffy and Fearne, 2004). As the paragraphs below show, the
research on partnership formation focuses on either environmental,
interorganizational, and firm-specific characteristics. The model proposed in this
dissertation contributes to this literature by combining these three elements.
Partnering motivations and formation. Scholars have identified several
factors that motivate firms to engage in close, collaborative relationships with other
organizations. Mohr and Spekman (1994) argue that partnerships are primarily
motivated to gain competitive advantage in the market place. Whipple et al (1996)
cite cost reduction, performance improvement, operating stability, the desire to
become more customer oriented, and access to the partner’s expertise as motivations
to enter alliances (or partnerships). They argued, however, that the list of potential
motivations to engage in alliances is unlimited in scope and many times specific to
36
the position within the marketing channel. Oliver (1990), based on a comprehensive
review of the interorganizational relationship literature, identified six motivations to
establish a wide range of business-to-business relationships: necessity, asymmetry,
reciprocity, efficiency, stability, and legitimacy. Bucklin and Sengupta (1993) pointed
out that, regardless of the motivations, firms expect significant strategic and/or
operational benefits that accrue from relationships to outweigh the costs of
maintaining them.
Some researchers have focused on modeling the conditions that trigger the
formation and shape the development of interorganizational relationships, such as
partnerships and alliances. A traditional perspective is transaction costs economics
(TCE) that aims to balance transaction and production costs in order to achieve an
economically efficient governance structure (e.g., Osborn and Baughn 1990).
Resource dependence theory examines the role of the external environment in
shaping such decisions. Conversely, the resource-based view focuses primarily on the
existing competence (or lack thereof) that may propel firms to ally with other firms
(e.g., White 2000). A fourth perspective is the network theory, which builds on the
notion that firms’ actions are influenced by the social context in which they are
embedded (Gulati 1998). TCE, resource dependency, resource-based view, and social
network theories are widely used in strategy research. Another very common
theoretical perspective, usually applied by marketing researchers, is social exchange
theory (e.g., Dywer, Schurr and Oh 1987). Social exchange theory (SET) is thus an
appropriate lens to investigate 3PL – customer relationships and is the main
37
theoretical perspective adopted in this dissertation. SET is reviewed in more detail in
the next section.
Many researchers have linked theoretical perspectives. Joshi and Campbell
(2003) investigated the effect of manufacturers’ downstream environmental
dynamism on the relational governance between manufacturers and their suppliers.
They found that in dynamic environments, manufacturers adopt relational governance
with suppliers when a manufacturer’s collaborative belief is high and when a
supplier’s knowledge is high. Izquierdo and Cillan (2004) combined resource
dependency theory, transaction cost economics, and relationship marketing. They
found that trust strengthens the effect of interdependence on the relational exchange
between suppliers and manufacturers in the automotive industry. White and Lui
(2005) distinguished sources of costs of cooperation and control in alliances. They
found that cooperation costs and transaction costs affect the level of time and effort a
manager spends in the alliance. In summary, although different theories focus on
firm, environmental, and inter-organizational factors, all factors seem to play a role in
decisions to build and maintain partnerships.
A common ground among researchers is that no one partnership type is
always appropriate. Zinn and Parasuraman (1997), for example, created a typology
that classifies logistical alliances along the dimensions of scope (broad versus
narrow) and intensity (high versus low). They emphasize that an alliance
characterized by a broad scope is not necessarily better or more effective than one
characterized by a narrow scope. Both broad and narrow scope strategic alliances can
be equally cost effective under appropriate conditions. Indeed, as Lambert and
38
Knemeyer (2004) point out, partnerships are costly to implement and are justified
only if the benefits of a partnership exceed those of not partnering. In a conceptual
piece that explored how, why, and when to establish a wide range of possible B2B
relationships, Cooper and Gardner (1993) suggest that firms should concentrate on
developing good business relationships, which may have varying levels of partnership
characteristics. Considering that partnerships may not be appropriate under all
circumstances, Rese (2006) developed a normative decision model for managers to
evaluate whether partnerships as a coordinative form are really the best choice in
given situations. The decision to partner should be taken based on two criteria: the
degree of standardization/individualization of the product purchased, and the
possibilities to allocate revenue to the several partners in the network.
Partnering outcomes. The effects of partnering on performance and
satisfaction have also been investigated. Duffy and Fearne (2004), using a sample of
UK retailers and fresh produce suppliers, found a positive effect of main partnership
dimensions on supplier performance (measured by future growth and current costs
and sales). Walton (1996) found a positive relationship between the five partnership
dimensions of planning, sharing benefits and burdens, interdependence, operational
information exchange and extendedness, and partnership satisfaction. Mohr and
Spekman (1994) showed that partnerships attributes (e.g., commitment, coordination,
interdependence, trust), communication behavior and conflict resolution techniques
do affect partnership success in terms of partner satisfaction and increases in sales.
Gentry and Vellenga (1996), in a conceptual paper, propose that logistics alliances are
a source of competitive advantage in the marketplace in that this allows for access to
39
superior skills and resources. Jonsson and Zineldin (2003) proposed a conceptual
model of dealer satisfaction in long-term working relationships between suppliers and
dealers. They found that reputation and close ties are key elements to achieving
satisfactory relationships when trust and commitment are high, and that it is possible
to achieve satisfactory relationships even if trust and commitment are lacking.
Research has also focused on the development of models that identify the
factors that influence partnership formation and management and provide guidelines
for managers to successfully implement partnerships. Lambert, Emmelhainz and
Gardner’s (1996, 1999) model, for example, provides managers with a series of steps
to be followed in order to identify the drivers, the components, or the activities of the
potential partnership, performance measures, etc. Tuten and Urban (2001) identified
three main factors that make a partnership successful: improved communication in
terms of frequency, characteristics of strong relationships (e.g. trust, reliability,
honesty and fairness), and satisfactory performance indicators (e.g. profitability,
market share, sales) in line with expectations. In a recent article, Lambert, Knemeyer
and Gardner (2004) validated Lambert, Emmenhainz and Gardner’s model based on a
facilitation of 20 partnerships cases.
2.3. Relationship Marketing
This subsection introduces the concept of relationship marketing, and its
theoretical foundations, with a focus on social exchange theory. A brief overview of
the extant literature related to business-to-business exchange is presented. Finally, the
concept of relationship marketing orientation, one of the main constructs of the
proposed model for this dissertation, is discussed.
40
2.3.1. Relationship marketing defined
Although considered by some as a mere restatement of the marketing concept,
thus “redundant and unnecessary” (Gruen 1997), relationship marketing has
undeniably become a “hot topic discipline” (Möller and Halinen 2000), and has been
referred to as “a major shift in marketing theory and practice” (Rao and Perry 2002).
This shift is based on the fact that in the relationship marketing philosophy, the
relationship between buyers and sellers becomes the core of the firm’s operational
and strategic thinking (Tse and Sin 2004). This view is different from transactional
marketing, where the customer remains faceless, and future interactions between
buyers and sellers are not a major concern. Indeed, some researchers believe that
relationship marketing is the opposite of transactional marketing (Rao and Perry
2002).
A comprehensive definition of relationship marketing is provided by Morgan
and Hunt (1994): “Relationship marketing refers to all marketing activities directed
towards establishing, developing, and maintaining successful relationship exchanges”
(p. 22). Although many other definitions of relationship marketing exist in the
literature, recent articles have often followed Harker (1999) who identified as many
as seven conceptual categories and 26 definitions of relationship marketing, arrived at
the following definition: “An organization engaged in proactively creating,
developing and maintaining committed, interactive and profitable exchanges with
selected customers [partners] over time is engaged in relationship marketing”(Harker
1999, p. 16). Note that the word “partners” indicate that the objectives of relationship
marketing are to build, maintain, and when necessary, terminate relationships not
41
only with customers, but with stakeholders as well; i.e., suppliers, partners, and even
competitors (Rao and Perry 2002).
Morgan and Hunt (1994) explain that in order to fully understand the nature of
relationship marketing, the first step is to distinguish between a transactional
exchange and a relational exchange. A discrete transaction involves a single, short-
time exchange, and has a sharp beginning and ending. A relational exchange,
however, encompasses multiple exchanges and usually involves both economic and
social bonds (Rao and Perry 2002). To illustrate the broad range of possible forms of
relationship marketing, Morgan and Hunt (1994) present ten examples: the partnering
involved in relational exchanges between manufacturers and their goods suppliers, as
in JIT procurement; relational exchanges with service providers; strategic alliances
between firms and their “competitors”; co-marketing alliances and global strategic
alliances; alliances with nonprofit organizations; partnerships for joint development;
long-term exchanges with ultimate customers; relational exchanges with working
partners, as in channels of distribution; exchanges involving functional departments;
exchanges between a firm and its employees; within firm exchanges such as among
subsidiaries or business units.
The central idea underlying the relationship marketing concept is, therefore, to
build and nurture lasting and mutually beneficial relationships (Hewett and Bearden
2001). The expected benefit of systematically developing cooperative and
collaborative partnerships is the decrease in exchange uncertainty through customer
collaboration and commitment (Andersen 2002). As a consequence, a higher share of
each customer’s lifetime business is attained (Gruen 1997). This notion was born
42
from the fact that organizations have realized that in today’s competitive
environment, firms need to collaborate in order to compete (Perlmutter and Heenan
1986). Interdependence and cooperation become, therefore, efficient tools to create
value and achieve sustainable competitive advantage (Gruen 1997).
2.3.2. Theoretical foundations of relationship marketing
An important ongoing debate amongst marketing researchers is related to the
scope and theoretical foundations of relationship marketing. Some articles have
discussed the theoretical roots and future directions of the relationship marketing
discipline (e.g., Möller and Halinen 2000, Rao and Perry 2002). Möller and Halinen
(2000), for example, argue that a theory of relationship marketing has not been
developed yet, but only a “variety of partial descriptions and theories focusing on the
broad content of the phenomena researchers have labeled relationship marketing” (p.
34). Indeed, the academic background of relationship marketing contributors is
extremely diverse (Harker 1999). For some researchers, however, this combination of
seemingly unrelated strands of marketing thought makes relationship marketing an
attractive concept and can become, in fact, its biggest strength (Harker 1999, Zinkhan
2002).
There is no agreement on the classification of the various relationship
marketing schools of thought (for examples see Zinkhan 2002, Rao and Perry 2002,
Möller and Halinen 2000). One common ground, however, is that the two major
disciplinary roots of relationship marketing are the Nordic school (Gummerson et al
1997) focusing on services marketing, and the industrial marketing school developed
43
by the international marketing and purchasing group (IMP). The service marketing
school focuses on explaining the management of services with special attention to the
relationship between the consumer and the personnel that provide the service. The
major questions investigated are the management of service encounters and service
quality (e.g, Parasuraman, Zeithman and Berry 1985). The industrial marketing
(marketing channels) school focuses on explaining governance structures and the
modeling of socio-economic behaviors of channel members and draws on socio-
economic theories (Spekman and Carraway 2006). Aside from the service and
industrial marketing schools, database marketing and the network approach are also
cited as strands of thought in the relationship marketing discipline (Möller and
Halinen 2000). Another research stream comes from the work on market-oriented
organizations, in which the culture of the firm places the customer as a primary
stakeholder (e.g., Narver and Slater 1990). Given the broad scope of relationship
marketing studies, a comprehensive literature review of all these schools of thought is
beyond the scope of this dissertation. Therefore, this section focuses on the
application of relationship marketing to business-to-business relationship formation
and development.
A common topic examined in relationship marketing is the effect of
characteristics of exchange relationships (e.g., trust, dependence) on outcomes (e.g.,
retention, referrals) that represent desired behaviors on the part of one or more of the
partners in the exchange (Hewett and Bearden 2001). Other studies, however, focus
on identifying the antecedents of relational behavior, such as trust (e.g., Morgan and
Hunt 1994) and long-term orientation (e.g., Ganesan 1994). In addition, many
44
marketing scholars have developed models in order to explain the development of
relationships between exchange partners (e.g., Dwyer, Schurr and Oh 1987). They are
usually process models that suggest that relationships that facilitate relational
exchanges develop in stages through exchange interactions over time. During the
interactions, trustworthiness of suppliers and buyers are tested and norms of behavior
are developed (Andersen 2002). These models are typically composed of phases that
involve initiation, maintenance and termination (Dwyer, Schurr and Oh 1987, Frazier
1983).
The studies described above have drawn on a variety of theories (Harker
1999), including interorganizational theory (van de Ven 1992, Reve and Stern 1979),
transaction-cost economics, resource dependency theory, and industrial network
theory (Larson 1992, Johanson and Mattson 1987). However, one of the earliest
approaches is social exchange theory (SET), which is the theoretical basis for this
dissertation. For this reason, the next subsection presents a brief description of social
exchange theory and provides a literature review on the development of relationships,
especially from a SET perspective.
2.3.3. A brief introduction to social exchange theory
Marketing scholars have relied widely on social exchange theory (hereafter,
SET) to explain relational governance in business-to-business relational exchanges.
SET focuses on the relationship between partners, and advocates that relational
control in the form of personal relations can be an effective means of governance.
This is opposed to early research that focused solely on power and dependence
45
(Lambe et al 2001). This governance mechanism is built on the foundation of trust,
commitment, and exchange norms that replace or complement more formal
governance mechanisms, such as detailed contracts. In SET, the relationship is the
unit of analysis and the key to relational exchange success.
Continuous interactions are said to build a relationship in stages. Anderson
(1995), for example, explains that relationship development is experienced as a series
of exchange episodes. Each exchange episode is composed of four events: defining
the purpose of a relationship, setting relationship boundaries, creating relationship
value, and evaluating exchange outcomes. Dwyer, Schurr and Oh (1987) stress the
evolution of exchange relationships and propose that relationships develop through
five phases, including awareness, exploration, expansion, commitment, and
dissolution.
According to SET, firms engage in and maintain relationships because they
expect that doing so will be rewarding (Blau 1964). Therefore, parties will remain in
a relationship as long as the parties judge the relationship satisfactory (in other words,
that the benefits of the relationship outweigh the costs). SET acknowledges that these
rewards may come in various forms, such as: economic, information, product or
service, and social rewards (such as emotional satisfaction, view sharing, etc). These
rewards are acquired through a history of interactions; the relationship being the lens
through which firms anticipate future costs and benefits. If previous experiences have
been positive, SET assumes that firms will expect future interactions to have positive
outcomes as well.
46
From a SET perspective, in order to assess whether rewards (i.e., benefits
minus costs) are satisfactory, social and economic outcomes are compared to two
standards that may vary from party to party (Thibaut and Kelley 1959): the benefit
standard one feels is deserved in a given kind of relationship – the comparison level
CL; and the overall benefit that one believes can be obtained from the best possible
alternative exchange relationship – the comparison level of alternatives CL
alt
. Note
that the comparison level CL is based upon present and past experiences with similar
relationships, and knowledge of other firms’ relationships (Anderson and Narus
1984). In other words, firms evaluate the economic and social outcomes from each
transaction and compare them to the level it is felt that the firm deserves (i.e., CL) as
well as to the level of benefits provided by other potential exchange partners (i.e.,
CL
alt
). If the outcomes level is above of what the firm believes is deserved (i.e., CL),
some degree of satisfaction will occur. If rewards acquired from a given exchange
relationship exceed CL
alt
, Thibaut and Kelley (1959) suggest that the party will have a
degree of dependence on the relationship. SET also suggests that, if positive
outcomes (that exceed CL and CL
alt
) and reciprocal beneficial actions occur, trust is
built over time and the process of creating trust creates social obligations. Therefore,
trust contributes significantly to the level of partner commitment to the relationship.
Aside from the creation of trust, with continuous interactions, explicitly and/or tacitly
determined rules of behavior, or relational exchange norms, are created. Relational
exchange norms are very important because they increase the efficiency of a
relationship and reduce the degree of uncertainty.
47
In a nutshell, the above paragraphs describe the four premises of social
exchange theory (Lambe et al 2001, p. 6): “1) exchange interactions result in
economic and/or social outcomes; 2) these outcomes are compared over time to other
exchange alternatives to determine the dependence on the exchange relationship; 3)
positive outcomes over time increase firms’ trust of their trading partner(s) and their
commitment to the exchange relationship; and 4) positive exchange interactions over
time produce relational exchange norms that govern the exchange relationship.”
2.3.4. Relationship marketing literature focused on business-to-
business relational exchange
There is a substantial body of research on business-to-business relational
exchange that uses and operationalizes SET (for a review, see Lambe et al 2001).
This research can be divided into two groups (Lambe et al 2001). The first group has
examined how antecedents contribute to a business-to-business exchange (Ganesan
1994, Morgan and Hunt 1994, Anderson and Weitz 1992, Frazier 1983, Dwyer
Schurr and Oh 1987). In this case, the dependent variable is the degree to which the
exchange is relational and the independent variables are derived from SET’s other
fundamental premises: economic/social outcomes from interactions, and
trust/commitment. The second group has investigated the outcomes or benefits of
relational exchanges (Anderson and Narus 1984, 1990, Bucklin and Sengupta 1993).
As a general observation, dependence and trust are commonly found to influence
relational behavior, and a positive effect of relational behavior on outcomes, such as
satisfaction and performance, is consistently found.
48
As mentioned above, researchers have investigated the antecedents of
relational behavior and the factors that have most importance in explaining relational
exchange. Anderson and Weitz (1992), for example, modeled commitment in
distribution channel relationships as a function of (1) each party’s perception of the
other party’s commitment, (2) self-reported and perceived pledges (idiosyncratic
investments and contractual terms) made by each party, and (3) other factors, such as
communication level, reputation and relationship history. Transaction-specific
investments and contractual terms (constraining contractual clauses; e.g., territorial
exclusivity, exclusive dealing, limit termination if some performance is not achieved)
function as important pledges to build and sustain commitment, affecting each party’s
perceptions of the other party’s commitment. Morgan and Hunt (1994), in their
seminal “commitment-trust theory” paper, showed that trust and commitment are key
mediating variables in explaining important relationship marketing outcomes. More
specifically, trust and commitment have a positive effect on acquiescence (degree to
which a partner accepts or adheres to another’s specific requests or policies) and
cooperation, while having a negative effect on the propensity to leave a relationship,
functional conflict, and decision-making uncertainty. Interestingly, it has been shown
that personal characteristics and the experience with an exchange partner also play
roles in relational behavior. Coulter and Coulter (2002), for example, showed that
person-related (e.g., empathy, politeness) and offer-related (customization, reliability)
service representative characteristics have an impact on trust, moderated by the length
of the relationship. They found that person-related service provider characteristics
had a greater effect on trust when customers are in the early stages of a particular
49
service relationship. As customers gained more direct product experience,
competence became more important. Izquierdo and Cillán (2004), in the context of
supplier-manufacturer relationships in the automobile industry, found that trust
enhances the effect of interdependence on the relational orientation of the exchange.
Other researchers have focused on the effects of relational behavior on
specific marketing outcomes, such as satisfaction or performance. Bucklin and
Sengupta (1993) developed a model of successful co-marketing alliances, which are
relationships between firms at the same level in the value chain, and found that a
history of interactions between partners increase the effectiveness (what they called
success) of the relationship. Moreover, reducing power and managerial imbalances
can foster gains in effectiveness as well. Smith and Barclay (1997) tested the effects
of organizational differences and trust on the effectiveness of selling partner
relationships. Their model showed that key organizational differences, mutual
perceived trustworthiness, and mutual trusting behaviors, all help explain perceived
task performance and mutual satisfaction. Hewett and Bearden (2001) developed a
model of success in relationships between foreign subsidiaries and headquarters
marketing operations. In their study, trust and dependence are modeled as antecedents
of relational behaviors (acquiescence and cooperation). In line with Smith and
Barclay’s (1997) findings, their results show that cooperative behaviors are positively
associated with product performance (index function of profitability, sales and market
share) in the subsidiaries’ markets. Anderson and Narus (1984) developed a model of
the distributor’s perspective of distributor-manufacturer relationships and found
support for SET premises. They found that distributors that perceived higher levels of
50
outcomes given CL
alt
perceived lower levels of manufacturer control. Manufacturer
control was found to be negatively related to distributor cooperation/satisfaction.
Also, outcomes given CL positively affected distributor cooperation/satisfaction. In a
later article, Anderson and Narus (1990) found that outcomes given CL, relative
dependence, and communication are critical constructs in the explanation of “on-
going” manufacturer and distributor working partnerships.
In addition, other researchers, such as Frazier (1983) and Dwyer, Schurr and
Oh (1987), conceptualize the process of exchange behavior between organizations
within marketing channels. As outlined above, these are process models in which the
events occur in stages. Frazier (1983)’s framework, for example, includes processes
of initiation, implementation, and review. His model also suggests that one source of
power is based on dependence. A series of interactions occurs between firms during
an exchange. Cooperation is high when communication is effective and participative
decision making occurs. Satisfaction is influenced by a variety of social and
economic factors. Dwyer, Schurr and Oh (1987) also propose a framework to
describe the development of exchange relationships, drawing a parallel with a marital
relationship model. They propose that relationships evolve in five general phases
identified as (1) awareness, (2) exploration, (3) expansion, (4) commitment, and (5)
dissolution. Each phase represents a major transition in how parties regard one
another.
The objective of this dissertation is to identify the antecedents of partnering
from a relationship marketing perspective. In the literature, Ganesan’s (1994)
“determinants of long-term orientation” model incorporates the major variables
51
(dependence, trust, satisfaction, and their antecedents) considered in relationship
marketing research. This dissertation applies and expands Ganesan’s model to the
context of logistics outsourcing relationships. A detailed description of Ganesan’s
model is presented in the next section.
2.3.5. Relationship marketing orientation
The objective of relationship marketing is to attract and develop mutually
beneficial, profitable exchanges with customers and other stakeholders (Harker
1999). In order to achieve this objective, scholars have argued that the relationship
marketing concept has to be incorporated into the organization’s culture and values,
placing the buyer-seller relationship “at the center of the firm’s strategic or
operational thinking” (Tse and Sin 2004). As Day (2000) pointed out, in order to
continually attract and keep customers, a relationship orientation must be immersed in
the mind-set, values, and norms of the organization. Following this logic, relationship
marketing scholars have recently developed the concept of relationship marketing
orientation – RMO (Tse and Sin 2004, Sin et al 2005 a, b), which captures the
behaviors and activities dedicated to relational exchange processes.
Although relational behavior is the core of the relationship marketing
discipline, RMO is a fairly new concept. In the marketing literature, the traditional
construct that captures a firm’s marketing behavior has been the market orientation
(MO) construct, which is defined as the “organizational culture that most effectively
and efficiently creates the necessary behaviors for the creation of superior value for
buyers and, thus, continuous superior performance for the business” (Narver and
52
Slater 1990). MO is composed of three behavioral dimensions - competitor
orientation, customer orientation and interfunctional coordination – and two decision
criteria – long-term focus and profitability. Although some research has highlighted
the positive relationship between MO and relational norms (e.g., Siguaw et al 1998),
Helfert et al (2002) were among the first to argue explicitly that the concept of MO
should be explored with particular focus on inter-organizational relationships. This
should occur since market oriented firms focus on understanding customer needs and
are willing to commit themselves to customers. Moreover, market oriented firms are
likely to provide financial, physical, and technical resources for relationships as they
value these relationships as a source of information generation and dissemination.
Although researchers in the service and industrial marketing schools have
indicated that relationship marketing has a positive effect on firm performance, very
limited empirical research has formally measured the RMO construct. Sin et al
(2005b), however, developed and validated a scale with six components – bonding,
communication, shared value, empathy, reciprocity, and trust – and found a positive
relationship between RMO and firm performance. In a second study, Sin et al (2005a)
investigated the moderating role of economic ideology and industry type in the
relationship between RMO and firm performance. They tested and found a positive
relationship between RMO and performance in two models: one for Hong Kong, and
another for Mainland China. RMO was found to be a stronger predictor in the service
sector in China, and in the manufacturing sector in Hong Kong. Tse and Sin (2004)
showed that the effects of RMO on performance are contingent on the competitive
53
strategic type of organizations. Also, the effect is stronger for market followers and
market “nichers” than for market leaders.
In the context of logistics outsourcing, investigating whether buyers of
logistics services engage in a relationship marketing philosophy is important to 3PLs
in that 3PLs can better select a marketing strategy to be employed with that specific
customer. As Day (2000) notes, some customers only want the timely exchange of
products or services with a minimum of hassles. Therefore spending resources and
effort on attempting to develop a relationship with these customers is not worthwhile.
This fact was observed in Garbarino and Johnson’s (1999) study with the customer
base of a nonprofit professional theater company. They demonstrated that the
decision to employ relational or transactional marketing should depend on the
relational orientation of the customer. For low relational customers (individual ticket
buyers and occasional subscribers), overall satisfaction is the primary mediating
construct between the customer attitudes towards the actors and the play and future
intentions of attending and subscribing to the theater. For the high relational
customers (consistent subscribers), trust and commitment, rather than satisfaction, are
the mediators between customer attitudes and future intentions. Therefore, the extent
to which 3PL customers engage in relationship marketing is an important
consideration when investigating a customer’s propensity to engage in partnerships
with their 3PL providers.
2.4. Ganesan’s (1994) model of long-term orientation
Since this dissertation builds upon Ganesan’s (1994) model of long-term
orientation in retail buyer – vendor relationships, and tests the model in the context of
54
logistics outsourcing, an overview of Ganesan’s model is appropriate. This section
briefly describes the model, its main variables and its hypothesized relationships. The
rationale for each of his propositions is described in detail in Chapter 3, along with
the propositions for this study.
The model. Ganesan, based on the premises of relationship marketing,
developed and tested the antecedents of long-term orientation in retail buyer – vendor
relationships. A special feature of his research is that he tested both vendor and
retailer perspectives, and was thus able to identify commonalities and differences
between the two groups. Note that since this study investigates the partnering
behavior from the 3PL customer’s perspective (i.e. the buyer of the service), the
discussion and analysis of Ganesan’s model in this section is from the buyer’s (i.e.,
the retailer’s) perspective.
Ganesan defined a retailer’s long-term orientation as the “perception of
interdependence of outcomes in which both a vendor’s outcomes and joint outcomes
are expected to benefit the retailer in the long run.” This means that while retailers
with short-term orientation are concerned with the outcomes of the current period,
retailers with long-term orientation are concerned with both current and future
outcomes, while emphasizing future conditions. However, Ganesan pointed out that
none of the orientations is altruistic, but focus on maximizing the outcomes obtained
through the channel. Retailer’s long-term orientation was modeled as a function of
two main elements: dependence and reliance on trust (see Figure 2). More
specifically, perceived dependence of a retailer on a vendor and retailer’s trust in a
vendor, are both positively associated with a retailer’s long-term orientation. In
55
addition, a retailer’s satisfaction with previous outcomes was hypothesized to have a
direct effect on retailer’s long-term orientation.
Figure 2. Ganesan’s (1994) model
The antecedents of a retailer’s dependence on a vendor are: the environmental
diversity and volatility in the market of the product that the retailer buys from the
vendor, as well as transaction specific investments
5
by both firms. Environmental
volatility, which is related to the extent that there are rapid fluctuations in demand
and inability to predict trends, was hypothesized to be positively related to a retailer’s
dependence on a vendor. Conversely, environmental diversity, which is related to the
5
Investments in tangible and intangible assets that are specific to the relationship and that have little
salvage value in case the relationship is terminated.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
56
presence of multiple competitors, products, etc., was hypothesized to be negatively
related to a retailer’s dependence on a vendor. Transaction-specific investments to the
relationship, when made by the retailer, were hypothesized to increase the retailer’s
dependence on a vendor, whereas investments made by the vendor were hypothesized
to have the opposite effect.
Ganesan (1994) operationalized trust with two components: credibility and
benevolence. Vendor’s credibility is related to the belief that the vendor is reliable,
whereas benevolence is related to the intentions and motivations of the vendor when
unanticipated circumstances arise. The antecedents of trust were modeled as
transaction specific investments undertaken by the vendor, reputation of the vendor,
retailer’s experience with the vendor, and retailer’s satisfaction with previous
outcomes of the relationship. All these elements were hypothesized to increase the
perceived trust of the retailer in a vendor.
Ganesan’s hypotheses are the following:
“H1: Trust in a vendor’s credibility and benevolence is positively related to retailer’s
long-term orientation.
H2: Dependence of a retailer on a vendor is positively related to the retailer’s long-
term orientation.
H3: Perceived dependence of a vendor on a retailer is negatively related to the
retailer’s long-term orientation.
H4: A retailer’s satisfaction with past outcomes is positively related to the retailer’s
long-term orientation.
H5: Reputation of a vendor is positively related to the retailer’s perception of
vendor’s credibility.
H6: A retailer’s satisfaction with past outcomes is positively related to the retailer’s
perception of a vendor’s benevolence and credibility.
57
H7: A retailer’s experience with a vendor is positively related to the retailer’s
perception of the vendor’s benevolence and credibility.
H8: A retailer’s perception of vendor TSIs is positively related to the retailer’s
perception of the vendor’s benevolence and credibility.
H9: Environmental volatility is positively related to a retailer’s dependence on a
vendor.
H10: Environmental diversity is negatively related to a retailer’s dependence on a
vendor.
H11: Retailer’s TSIs are positively related to a retailer’s dependence on a vendor
and negatively related to the retailer’s perception of the vendor’s dependence on the
retailer.
H12: A retailer’s perception of vendor’s TSIs is negatively related to a retailer’s
dependence on a vendor and positively related to the retailer’s perception of the
vendor’s dependence on a retailer.”
Ganesan’s data were obtained from two separate surveys. First, he mailed a
survey to retail buyers, who were asked to choose a specific vendor and respond to
questions about their relationships with those vendors. Then, a second questionnaire
was sent to the vendors indicated by the respondent retailers who were asked about
their relationships with the retailers. In his sample, the vendors represented a variety
of product lines, some of which had many competitors, others of which had few
competitors. The retailers, thus, had various levels of dependence on the selected
vendors, and vice-versa.
Results. Ganesan obtained excellent support for the primary antecedents of
retailer’s long-term orientation. He found that dependence, trust (credibility and
benevolence), and satisfaction have an impact on a retailer’s long-term orientation of
a relationship. Figure 3, below, depicts the results of the model. The overall model fit
was good (?2 = 39.95. df = 31). All the five factors hypothesized to affect the
58
retailer’s long-term orientation were significant except one: a retailer’s perception of
vendor benevolence. More specifically, dependence of a retailer on a vendor was
positively related to a retailer’s long-term orientation, and a retailer’s perception of a
vendor’s dependence is negatively related to a retailer’s long term orientation. A
retailer’s perception of the vendor’s credibility was positively associated with a
retailer’s long-term orientation. A retailer’s satisfaction with previous outcomes was
positively related to the retailer’s long term orientation. These four variables
explained 75.2% of the variance associated with a retailer’s long-term orientation.
The dependence of a retailer on a vendor was influenced by the availability of
alternative vendors (environmental diversity) and the retailer’s TSI in the
relationship. Vendor TSI was a main predictor of retailer’s trust in a vendor’s
credibility and benevolence. A retailer’s satisfaction with a vendor and a retailer’s
experience with a vendor were not significant predictors of a vendor’s credibility or
benevolence.
It is important to point out that Ganesan also tested all the antecedents of trust
and dependence in his model for their indirect effects on a retailer’s long-term
orientation. None of the indirect effects were significant, suggesting that the effects of
the independent variables on long-term orientation were mediated through the
dependence and trust constructs.
59
Figure 3. Ganesan’s (1994) model with results
With respect to the hypotheses related to the antecedents of trust, the vendor’s
reputation had a positive effect on vendor’s credibility, but not on benevolence,
supporting H5. H8 was also fully supported; i.e., a retailer’s perceptions of
transaction specific investments by a vendor affect the retailer’s perception of a
vendor’s credibility and benevolence. H7 was not supported; i.e., no effect was found
for the retailer’s experience with the vendor on the vendor’s credibility and
benevolence. Similarly, no significant relationship was found between satisfaction
with previous outcomes and trust.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
.112
.297*
.419*
-.266*
.323*
.442*
.433*
.755*
-.49
.039
-.135 .755
-.324*
.119
.397*
.257*
.588*
* Statistically significant p < .01
60
Mixed results were found for the hypotheses related to the antecedents of
dependence. Environmental diversity had a significant, negative effect on retailer’s
dependence, providing support for the hypothesis, H10, whereas environmental
volatility did not have a significant effect on a retailer’s dependence on a vendor, as
hypothesized in H9. Retailer’s transaction specific investments had a significant,
positive effect on the retailer’s dependence on the vendor, and on a retailer’s
perception of vendor’s dependence, providing partial support for H11. Finally,
perception of a vendor’s TSIs also had a significant, positive effect on a retailer’s
dependence on the vendor and on a retailer’s perception of a vendor’s dependence,
providing partial support for H12. Table 1, below, summarizes the findings.
Table 1. Summary of the findings of Ganesan’s (1994) model
Hypotheses (Retailer’s perspective) Result
Vendor’s credibility ? retailer’s long-term orientation Supported
Vendor’s benevolence ? retailer’s long-term orientation Not supported
Dependence of a retailer on a vendor ? retailer’s long-term
orientation
Supported
Perceived dependence of a vendor on a retailer ? retailer’s long-
term orientation
Supported
Retailer’s satisfaction with previous outcomes ? retailer’s long
term orientation
Supported
Reputation of a vendor ? vendor’s credibility Supported
Retailer’s satisfaction with past outcomes ? vendor’s
benevolence
Not supported
Retailer’s satisfaction with past outcomes ? vendor’s credibility Not supported
Retailer’s experience with a vendor ? vendor’s benevolence Not supported
Retailer’s experience with a vendor ? vendor’s credibility Not supported
Retailer’s perception of vendor TSI ? vendor’s benevolence Supported
Retailer’s perception of vendor TSI ? vendor’s credibility Supported
Environmental volatility ? retailer’s dependence Not supported
Environmental diversity ? retailer’s dependence Supported
61
Table 1 (cont.) Summary of the findings of Ganesan’s (1994) model
Hypotheses (Retailer’s perspective) Result
Retailer’s TSIs ? retailer’s dependence Supported
Retailer’s TSI ? retailer’s perception of vendor’s dependence Not-supported
Retailer’s perception of vendor TSI ? retailer’s dependence Not-supported
Retailer’s perception of vendor TSI ? retailer’s perception of
vendor’s dependence
Supported
Research that extends Ganesan’s. The results achieved by Ganesan (1994)
demonstrate the relevance of the predictors in explaining the perceived long-term
orientation of exchange partners. Although numerous scholars have referred to
Ganesan (1994) to support their hypotheses development, to the best of this author’s
knowledge, no research has explicitly replicated, or expanded Ganesan’s model.
However, subsequent research has focused on other facets of long-term orientation,
such as: the effect of long-term orientation on performance (e.g., Kalwani and
Narayandas 1995), the effect of long-term orientation on other relational behavior
characteristics (Lusch and Brown 1996), other antecedents of long-term orientation
(Schultz and Good 2000), and the potential negative impacts of long-term
relationships (e.g., Grayson and Ambler 1999).
Additional explanatory variables have been used as predictors of long-term
orientation, such as procedural and distributive justice (Griffith et al 2006) and
customer orientation of the seller (Schultz and Good 2000). Based on social exchange
theory, Griffith et al (2006) showed that a distributor’s perception of a supplier’s
procedural and distributive justice in its policies enhanced the distributor’s long-term
orientation and relational behaviors. In Schultz and Good’s (2000) model, a seller’s
orientation towards its customer was a predictor of long-term orientation. In this case,
62
interorganizational factors (i.e., trust, dependence) were not included in the model.
One of the contributions of the present dissertation will be to test these two factors
together. In addition, the present dissertation differs from previous models in that it
tests the effect of a relational orientation of the 3PL customer with its own customers
on the partnering behavior with a third party service provider.
The effect of long-term orientation on performance has also been investigated.
Kalwani and Narayandas (1995), for example, showed that long-term orientation with
select customers achieves higher profitability, but the same sales growth as does a
transactional approach to servicing customers. Lusch and Brown (1996) consider
long-term orientation as a mediator between dependence, type of contracts, and
relational behavior (i.e., flexibility, information exchange, solidarity). All of these
variables were found to have a positive impact on performance. The downside of
long-term relationships has also been discussed. For example, Grayson and Ambler
(1999) found that the dynamics of shorter relationships are different than those of
longer relationships. For example, the effect of trust on commitment was found to be
more important in earlier phases of a relationship. As well, in longer relationships,
rising expectations and perception of loss of objectivity might occur, leading to
dissatisfaction. Similar results were found by Claycomb and Frankwick (2005).
Industrial buyers perceive the costs of maintaining relationships with key suppliers
differently in the various relationship development phases. For example, information
search costs about suppliers are higher in the early stages of a relationship. Buyer
uncertainty is reduced over time, but human interaction costs increase substantially.
63
2.5. Cultural differences and logistics outsourcing
As noted in the previous section, most tests of theory-based models in
logistics outsourcing have been conducted with firms in the U.S (e.g. Knemeyer
2003, 2004, 2005, Stank et al 2003). The studies that investigated logistics
outsourcing in other countries have primarily relied on case studies and exploratory
surveys to depict the reality of logistics outsourcing in those countries, such as
Australia (Sohal, Millen, and Moss, 2002), Singapore (Sum and Teo 1999), New
Zeland (Sankaran, Mun, Charman, 2002), India (Sahay and Mohan 2006), and Saudi
Arabia (Sohail, Sadiq, and Obaid 2005). These articles focus on describing current
logistics outsourcing practices undertaken in these countries and identifying future
trends. The general claim is that logistics outsourcing practices are more developed in
the U.S. and Western Europe than in developing countries. This claim also holds for
Brazil, where the sample firms for this study are located. Indeed, as noted in the
Booz-Allen report discussed earlier (COPPEAD and Booz-Allen 2001), logistics
outsourcing is a recent trend in Brazil, given that the majority of firms still focus on
short-term, arm’s length relationships with 3PLs.
Given that most studies reinforce the notion that logistics outsourcing
relationships differ between U.S. and developing countries, a fair concern is that the
findings from this dissertation, that are drawn from a survey of Brazilian firms, may
not be generalizable to 3PL relationships in developed countries, such as the U.S. In
other words, the question is whether the relationships among the constructs proposed
here (e.g., trust, dependence, and partnering behavior) can be directly comparable to
the findings of studies conducted with U.S. firms.
64
Cultural differences and interorganizational relationships. As Anderson and
Weitz (1989) state, differences in cultures influence the nature of interorganizational
interactions. A sizable work on cross-cultural differences follows this logic and has
focused on how culture can shape firms strategies. A seminal piece is the work of
Hofstede (2001) who surveyed over 88,000 employees from more than 40 countries
and identified four dimensions upon cultures vary: 1) Power distance, which assesses
human inequalities of prestige, wealth, and power, 2) Uncertainty avoidance, which
indicates how people feel threatened by uncertainties or unknown situations, thus
preferring stability and rule orientation, 3) Individualism, which assesses how cultures
emphasize individuality versus collectivity, 4) Masculinity, which assesses the
importance cultures place on careers and money as opposed to social goals, such as
relationships or protection of the physical environment. Comparing Brazil and the
United States, for example, Brazil has higher scores of power distance and
uncertainty avoidance than the U.S., but much lower scores of individualism. Various
studies have validated these dimensions of cultural differences or have used them to
identify differences in business practices.
Other studies focus on cross-cultural comparisons and find that culture does
indeed play a role in the way business is conducted. In the context of headquarter –
subsidiaries relationships, Hewett and Bearden (2001) found that individualism
moderates the relationship between trust and cooperation. In other words, trust has a
stronger effect on cooperation in cultures with higher levels of collectivism. Kogut
and Singh (1988) found that the foreign direct investment (FDI) mode was influenced
by the cultural distance between the home country of the entering firm and the host
65
country. Lin and Germain (1998) found support for the contention that cultural
similarities affect joint venture performance.
One study that partially contradicts previous findings is Morris (2005). He
replicated a model previously tested with American firms, the seminal KMV – “Key
Mediating Variable” model of Morgan and Hunt (1995) that identifies the antecedents
and consequences of trust. In Morgan and Hunt’s (1995) study, the respondents were
retailers from the tire industry who normally do business with domestic suppliers.
Morris (2005), on the other hand, surveyed a sample of U.S. purchasers in different
industries that procure from international suppliers. He found an overall agreement
with Morgan and Hunt’s findings. Morris (2005) also calculated the cultural distance
between the purchasers and customers in the sample and tested two models: a
culturally distant sample (composed of firms that procured from culturally distant
countries), and a culturally similar sample (composed of firms that procured from
culturally similar countries). Interestingly, he found that the relationships in the
model were very similar for the two samples, implying that cultural differences did
not impact the general relationships between the constructs.
Therefore, there is mixed evidence as to how generalizable the findings from
one country can be applied to another country, given the cultural differences between
them. On the one hand, it should be noted that the theoretical bases for this
dissertation were developed by socio-psychologists and with no mention of potential
cultural issues in the development of their hypotheses. On the other hand, certain
studies have indicated that relationships between variables are intensified or
weakened in the presence of different cultural traits. As the work of Hofstede (2001)
66
shows, for example, the U.S. has a higher individualistic culture as opposed to Brazil,
which is more collectivistic. It might be the case that these differences in culture can
affect the results of this dissertation.
In conclusion, as with any other empirical work, the results from this
dissertation should be replicated in other industries and in other countries in order to
determine the generalizability of the results. Since some of the constructs measured
here are similar to those measured with American logistics outsourcing firms, a cross-
cultural comparison study should be feasible.
2.6. Conclusion
This chapter presented a review of the relevant literature that serves as the
basis of the development of the hypotheses presented in Chapter 3. First, the literature
in logistics outsourcing was discussed, with a focus on the relationships between
3PLs and their customers. Then the definition of partnering behavior adopted in this
dissertation was presented, along with a brief overview of the various research
streams in the logistics, marketing and strategy literatures that have investigated the
formation of “hybrid governance structures”, of which partnerships is one type. Next,
the relationship marketing perspective was introduced, with special attention to social
exchange theory that serves as basis for the development of the model in this
dissertation. Next, a brief description of Ganesan’s (1994) model of long-term
orientation in buyer-seller relationships was presented, upon which this dissertation
builds. Finally, a discussion of the generalizability of the findings of this study was
presented in light of the literature on cross-cultural differences.
67
Chapter 3: Model Development and Hypotheses
The objective of this chapter is to present the development of a model of the
antecedents of customer partnering behavior in logistics outsourcing relationships.
The initial section of the chapter describes the conceptual model based on the
relationship marketing perspective and, more specifically, social exchange theory.
Next, the rationale for the each of the hypotheses that compose the model is
discussed.
3.1. Conceptual model
In this dissertation, a customer’s partnering behavior in the relationship with a
3PL corresponds to the customer’s perception that this relationship presents the
following behavioral elements (Gardner et al 1994): planning, sharing of benefits and
burdens, extendedness, systematic operational information exchange, and mutual
operating controls. In order to identify the antecedents of this type of behavior, a
relationship marketing perspective is adopted.
Traditional relationship marketing models that investigate the development of
interorganizational relationships follow the premises of social exchange theory (SET)
and focus on the dynamics of the relationship under investigation (i.e. they focus on
interorganizational factors). In these models, trust and dependence are consistently
used as motivators for each partner to engage in and develop lasting and mutually
beneficial relationships (Hewett and Bearden 2001). In addition, social exchange
theory has emphasized that partners engage in relationships if they are rewarding or
satisfactory (Lambe et al 2001). This dissertation follows the traditional social
68
exchange theory rationale (e.g., Ganesan 1994, Hewett and Bearden 2001) and
includes trust, dependence, and satisfaction as main antecedents of customer
partnering behavior in the relationship with a 3PL. More specifically, it is
hypothesized that a customer’s trust in a 3PL’s credibility and benevolence,
dependence on a 3PL, perception of 3PL dependence on a customer, and satisfaction
with the relationship with a 3PL will be related to a customer’s partnering behavior.
However, largely based on premises of relationship marketing, and in
particular, social exchange theory, it is hypothesized that interorganizational factors,
such as dependence, trust, and satisfaction are not the only elements that explain a
customer’s partnering behavior in the relationship with a 3PL. It is proposed that
some customer-specific characteristics will also impact a customer’s partnering
behavior as well.
One of the foundational premises of social exchange theory is that social and
economic outcomes of an exchange are compared to a specific comparison level
(CL). This CL represents the benefits that a firm feels is deserved from a relationship
and is unique to each firm (Lambe et al 2001). In the logistics outsourcing context,
customers have also had unique partnering experiences with other 3PLs. This prior
experience may affect their expectations regarding their relationship with the current
3PL (i.e., its respective CL). It can be inferred that if a 3PL customer had positive
experience partnering with other 3PLs, then it would likely be more willing to exhibit
partnership behavior in the present. This argument is consistent with network theory,
in that one of the main assumptions is that experience from earlier relations is crucial
69
to understand the development of current cooperative behaviors (Skjoett-Larsen
2000).
Moreover, relationship marketing scholars propose that some firms have a
particular orientation towards engaging in relationships with their main partners.
More specifically, some firms have a “relationship marketing orientation” (RMO)
incorporated in the firm’s values and culture. Awareness of a customer’s relationship
marketing orientation can be crucial to identifying whether relational marketing is an
appropriate strategy to adopt (Rao and Perry 2002). For example, Garbarino and
Johnson (1999) found that for the customers of a New York repertory theater
company, trust and commitment were mediators only for high relational customers,
not for low relational customers. Therefore, in the 3PL context, it may be the case that
even if the 3PL is performing efficiently and effectively, certain customers will not
engage in partnerships with the 3PL simply because they are not focused on building
close relationships or partnerships.
In sum, the present model hypothesizes that the following interorganizational
conditions and customer-specific characteristics will influence a customer’s
partnering behavior with its 3PL:
- a customer’s dependence on its 3PL;
- a customer’s perception of a 3PL’s dependence on the customer;
- a customer’s trust in its 3PL’s credibility;
- a customer’s trust in its 3PL’s benevolence;
- a customer’s satisfaction with previous outcomes of the relationship with the
3PL;
70
- a customer’s prior experiences with partnering with other 3PLs;
- a customer’s relationship marketing orientation.
In addition, the model proposes antecedents for the interorganizational factors
as well (i.e., antecedents of both components of dependence and trust). Figure 4,
below, depicts the conceptual model to be detailed in the following section.
Figure 4. Conceptual model of customer partnering behavior in logistics outsourcing
relationships
In the pages that follow, the conceptual model is described in more detail
through the development of specific hypotheses. The primary antecedents of
customer partnering behavior are presented first, followed by the antecedents of
dependence and trust.
Antecedents of customer
and 3PL dependence
Antecedents of trust
Satisfaction
With 3PL
Customer Trust
in 3PL
(credibility and benevolence)
Customer and
3PL
Dependence
Customer
Partnering
Behavior
Interorganizational conditions
Customer characteristics
Customer
Relationship Mkt.
Orientation
Customer
Partnering
Experience
Customer
Relationship Mkt.
Orientation
Customer
Partnering
Experience
71
3.2. Hypotheses development
In this section the rationales for the hypotheses are presented. First, the
primary antecedents of customer partnering behavior are discussed. Next, the
antecedents of both customer dependence on a 3PL and the perception of 3PL
dependence on a customer are discussed. Finally, the development of the hypotheses
for the antecedents of both dimensions of trust (i.e. credibility and benevolence) is
presented.
3.2.1. Primary antecedents
The rationale for the hypotheses related to primary antecedents of customer
partnering behavior is based on the premises of social exchange theory, network
theory, and the relationship marketing orientation perspective. They are related to
both interorganizational conditions and customer specific characteristics. Five
interorganizational conditions are identified: 1) customer’s perception of its
dependence on a 3PL; 2) customer’s perception of a 3PL dependence on the
relationship with a customer; 3) customer’s trust in a 3P’s credibility; 4) customer’s
trust in a 3PL’s benevolence; 5) customer’s satisfaction with previous outcomes of
the relationship. Throughout the section, the discussion of both dimensions of
dependence and both dimensions of trust will be presented jointly. The customer-
specific characteristics hypothesized to impact customer partnering behavior with a
3PL are: 1) a customer’s prior experience with partnering with 3PLs and 2) a
customer’s relationship marketing orientation.
72
Figure 5, below, depicts the sub-model comprising the primary hypotheses. In
the paragraphs that follow, the rationale for each hypothesis is discussed in detail.
Figure 5. Primary antecedents of customer partnering behavior in logistics outsourcing
relationships.
Customer dependence, 3PL dependence, and customer partnering behavior.
In the marketing literature, dependence has been viewed as both an antecedent and an
outcome of a relationship. Dwyer et al (1987), for example, define dependence as
“the recognition by both partners that the relationship provides greater benefits than
either party could attain alone or that outcomes obtained from the exchange
Customer’s
dependence on 3PL
Customer’s
dependence on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Perception of 3PL’s
dependence on customer
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation +
+
+
+
+
+
-
73
relationship are greater than those possible from other business alternatives.” On the
other hand, Lambe et al (2000) argue that dependence is built over time as the
partners: 1) invest in the exchange relationship; 2) determine mutually compatible
goals; and 3) see positive outcomes from the relationship. Most studies in channels of
distribution, however, have viewed dependence as a determinant of organizational
conduct and strategic behavior (Ganesan 1994).
In this research, dependence of a customer on a 3PL is considered as an
antecedent of the relationship style between a customer and a 3PL, and is defined as a
customer’s need to maintain the channel relationship to achieve desired goals
(Frazier, 1983). Following the social exchange theory rationale, this study proposes
that dependence of a customer firm on its 3PL occurs when the benefits accruing
from the relationship are higher than those that could be obtained outside the
relationship, either through an alternative partner or with no partner at all (Thibaut
and Kelley 1959). As well, following Pfeffer and Salancik’s (1978) resource
dependence rationale, it is considered that dependence of a customer on a 3PL is
caused by the perceived need of a 3PL’s critical resource; i.e., the expertise and
capability of planning and performing complex logistics activities more efficiently
and more effectively. According to the resource dependency theory, the need to
acquire these critical resources creates a situation of dependency, and in order to
maintain a consistent supply of these resources, a firm (i.e. customer) may choose to
generate alliances with the supplier organization (i.e. 3PL) (Sakaguchi et al 2004).
Pfeffer and Salancik (1978) suggest that the typical solution to problems of
dependence and uncertainty involves increasing coordination, which means
74
increasing mutual control over one another’s activities. For the retailer-vendor case,
Ganesan (1994) proposes that one way for retailers to gain control over important and
critical vendors is to have a long-term orientation, and to improve the overall
profitability of both parties through investments in the relationship. Investing in the
relationship with both tangible and intangible resources will eventually reduce
asymmetries in dependence and increase mutual dependence. Examples of such
investments in the 3PL setting are: compatible software, training of personnel,
investment in physical assets such as warehouses, and so forth. Extending Ganesan’s
rationale and following Pfeffer and Salancik (1978), this study proposes that a 3PL
customer’s perceived dependence on a 3PL will lead to the customer’s close
involvement in the 3PL’s activities. This can be achieved by means of partnering.
Research has consistently shown the key role that dependence plays in
nurturing cooperation and adaptation in relational exchanges, thus contributing to
partner commitment (Knemeyer et al 2003). Sakaguchi et al (2004), for example,
created and tested a model of supply chain integration, theoretically grounded in the
resource dependency perspective (tested with U.S. small businesses). They adopted
the IT integration model of Chwelos, Benbasat and Dexter (2001) and found that
companies with a higher level of resource dependency were more likely to integrate
their supply chains compared to those with less resource dependency.
The opposite situation, however, might occur: a customer might perceive that
a 3PL is dependent on it. The same rationale discussed above will hold in this
situation. When a customer perceives a 3PL to be dependent on their relationship, a
customer may be less willing to assume the costs of maintaining a close relationship.
75
The net benefits provided by this 3PL may not be perceived to be greater than what
could be provided by alternative 3PLs (Ganesan 1994). In this situation, a customer
has little incentive to exhibit partnering behavior with this 3PL. Based on the above
discussion, two hypotheses are presented:
H1: Customer dependence on a 3PL is positively related to the customer’s partnering
behavior.
H2: Perceived dependence of a 3PL on a customer is negatively related to the
customer’s partnering behavior.
Customer’s trust in a 3PL’s credibility and benevolence, and customer
partnering behavior. As discussed previously, the relationship marketing literature
has emphasized that dependence is not sufficient to explain the decision to engage in
business-to-business relationships (Ganesan 1994, Lambe et al 2001). Firms with
exclusively high levels of dependence and asset specificity may seek to escape this
dependence (Ganesan 1994). With trust, however, the focus is on future conditions:
exchange partners weigh their outcomes through the lens of anticipated past and
future exchanges and the social benefits of compromise. Moreover, when reciprocal
motivations for developing relationships are in place, partners have the objective of
obtaining mutual benefits by means of cooperation, collaboration, and coordination
(Oliver 1990).
Trust is defined as the belief in an exchange party’s reliability and integrity
(Morgan and Hunt 1994) or as the belief that a party’s word is reliable and that a
party will fulfill its obligations in an exchange (Pruitt 1981). Previous research has
operationalized trust in a number of ways. Many studies operationalize trust as a
unidimensional factor (Morgan and Hunt 1994, Doney and Cannon 1997, Hewett and
76
Bearden 2001, Nicholson et al 2001). Knemeyer (2000) defines trust as a construct
with three dimensions: achieving results, acting with integrity, and demonstrating
concern. Achieving business results is related to the ability to perform tasks in which
the trustee is expected to be an expert. Demonstrating concern is equivalent to
benevolence, while acting with integrity is related to the trustor’s perception that the
trustee adheres to a set of principles that the trustor finds acceptable. This study
follows Ganesan (1994) and defines trust as a construct with two components:
credibility and benevolence. Credibility is based on the extent to which the customer
believes that a firm has the required expertise to perform the job effectively and
efficiently. This dimension is related to the consistency and stability of the trustee’s
behavior. Benevolence is based on the extent that the customer believes that a firm
has intentions and motives beneficial to the customer when new conditions arise;
conditions to which a commitment has not been made (i.e., it focuses on the
intentions of the exchange partner rather than on the exchange partner’s specific
behavior).
Trust is a major construct in most relationship marketing models (Wilson
1995) and the key social variable in explaining interfirm cooperation and long-term
relationships (Izquierdo and Cillán 2004). Indeed, Morgan and Hunt’s (1994) finding
that trust leads to acquiescence, cooperative behaviors, and a decrease in uncertainty,
supports the argument that from a relational perspective, trust is an important
mechanism for persuasion and fostering future exchanges (Hewett and Bearden
2001). Doney and Canon (1997), likewise, find that trust enhances the likelihood of
future interactions among parties. Pruitt (1981) indicates that trust is highly related to
77
a firm’s desires to collaborate. In the outsourcing and 3PL literatures, trust has been
often presented as an important driver or mediator of successful 3PL-customer
relationships. Zineldin and Bredenlow (2003), for example, in a case study with two
Swedish manufacturers involved in strategic outsourcing relationships, emphasized
that a long-term relationship does not guarantee success without trust and
commitment. Similarly, Knemeyer and Murphy (2004) surveyed 3PL users and found
a positive relationship between trust and the 3PL customer’s perceived performance.
In a 3PL-customer relationship setting, it is expected that a customer’s trust in
a 3PL increases customer partnering behavior. More specifically, a customer’s trust in
a 3PL affects its decision to enter into a partnership in three ways (Ganesan 1994): 1)
it reduces the perception of risk associated with opportunistic behavior by the 3PL; 2)
it increases the confidence of the retailer that short-term inequities will be resolved
over a long period, and; 3) it reduces the transaction costs in an exchange relationship
(Williamson, 1981). From the above discussion, it is hypothesized that higher levels
of customer trust in a 3PL are related to a higher level of customer partnering
behavior.
H3: A customer’s trust in a 3PL’s credibility is positively related to a customer’s
partnering behavior.
H4: A customer’s trust in a 3PL’s benevolence is positively related to a customer’s
partnering behavior.
Prior partnering experience and customer partnering behavior. Network
and social exchange theories propose that the earlier experiences that a firm has had
with other partners play a role in explaining a firm’s behavior in present relationships
(Skjoett-Larsen 2000). Network scholars, for example, emphasize the important role
78
of prior experience with other partners as a factor that will shape the organization’s
expectations regarding the new relationships and increase the likelihood of future
endeavors (Uzzi 1996). As Skjoett-Larsen (2000) emphasizes, one of the main
assumptions in the network model is that not only the “chemistry between
individuals” within the parties, but also the actual (positive) experience from earlier
relations is crucial to understanding the development of cooperative behaviors
between 3PLs and their customers. From a social exchange theory perspective, each
firm has a social and economic benefit standard that it feels is deserved in a
relationship; i.e., the so called comparison level CL (Thibaut and Kelley 1959). This
level is compared to the outcomes from a particular relationship. In a logistics
outsourcing relationship context, it can be inferred that customer firms with positive
previous experiences with partnering (with other 3PLs) have passed through the
inherent difficulties and challenges of the process. As a result, these firms have
acquired a capability to plan and coordinate operational and administrative logistics-
related activities and manage a partnership-type relationship with an external
organization. These firms are then more likely to have realistic expectations towards
their present relationship and to engage in a partnership with their 3PLs.
This line of reasoning is supported in several empirical studies. Ho et al
(2003), in the context of spin-off IT outsourcing (i.e., an IT department within an
organization gets “spun-off into a separate external entity”), found that firms with
prior outsourcing experience with other third-parties experienced less managerial
conflicts, with this previous experience having a positive impact on performance. In
his study of alliances and networks, Gulati (1999) noted that by participating in
79
alliances, firms can develop managerial capabilities that result from their experiences
and learning. This learning can enhance the likelihood of engaging in new alliances.
Gulati found that the greater a firm’s alliance formation capabilities, the greater the
likelihood for that firm to enter a new alliance. In another study, Gulati et al (2000)
noted that firms that forged a greater number of alliances appeared to extract more
value from their alliances over time. He suggested that experience with alliances can
be a source of strategic advantage.
Therefore, it is hypothesized that customers that had prior positive
experiences in partnering with 3PLs are more likely to exhibit higher levels of
partnering behavior with the present 3PL.
H5: Prior partnering experience with 3PLs is positively related to customer’s
partnering behavior with the focal 3PL.
Relationship Marketing Orientation and customer partnering behavior. The
marketing discipline has been reshaped with a relationship marketing orientation (Sin
et al 2005), in which short term transactional exchanges are replaced with long-term
buyer-seller relationships. When exhibiting a relationship marketing orientation, a
firm’s strategy emphasizes relationship building by cultivating trust, empathy,
bonding and reciprocity between a firm and its customers (Tse et al, 2004). The
nurturing of market relationships is considered in the literature as a top priority for
most firms (Day, 2000) and a valuable resource (Helfert et al, 2002).
Gronroos (1991) argued that the purpose of relationship marketing is to
“establish, maintain, and enhance relationships with customers and other parties at a
profit by mutual exchange and fulfillment of promises.” After a comprehensive
80
review of 26 definitions of relationship marketing, Harker (1999) proposed that “an
organization engaged in proactively creating, developing and maintaining committed,
interactive and profitable exchanges with selected customers (partners) over time is
engaged in relationship marketing” (p. 16). In order to maintain relationships with
valuable customers, a relationship orientation must pervade the mind-set, values, and
norms of the organization (Day, 2000). In other words, the buyer-seller relationship
must be at the center of the firm’s strategic or operational thinking (Tse et al 2004,
Sin et al 2005). One interesting point to highlight is that relationship marketing is in
line with the concept of supply chain management (SCM), since the one main
characteristic of the SCM philosophy is “a strategic orientation toward cooperative
efforts to synchronize and converge intrafirm and interfirm operational and strategic
capabilities into a unified whole, as well as a customer focus to create unique and
individualized sources of customer value, leading to customer satisfaction” (Min and
Mentzer 2004).
In the logistics outsourcing literature, the 3PL customer’s orientation towards
building and maintaining lasting relationships with customers and partners has been
considered to be a crucial factor in determining the supply chain role of logistics
providers (Bolumole, 2001). Larson and Gammelard (2001), for example, argued that
close cooperation between buyer and supplier may lead to plans to bring a carrier into
the collaborative process. In a study of the role of carriers within buyer-supplier
partnerships, Gentry (1996) enforced that logic and proposed that increasing the
involvement of carriers within these partnerships may enhance cost savings and
service improvements, as all parties work together to improve quality and operational
81
efficiencies. She found empirical support for the contention that carriers utilized in
buyer-supplier partnerships were viewed differently from carriers used in non-
partnering buyer-supplier relationships. More specifically, she found that carriers
within existing buyer-supplier partnerships were more likely to embody the
dimensions of: (1) long term commitments, (2) open communications and
information sharing, (3) cooperative continuous improvements on cost reductions and
increased quality, and (4) the sharing of risks and rewards of the relationship.
Based on the above discussion, it is hypothesized that organizations that
engage in a relational approach with customers and suppliers have a more external
focus and are thus more likely to perceive the 3PL as an integral part of their supply
chain, as a facilitator of supply chain integration. In other words, it is proposed that a
firm with a relationship marketing orientation towards (i.e. collaborates and bonds
with) channel partners and is using a 3PL provider will more likely exhibit
characteristics of partnering with a 3PL.
H6: A customer’s relationship marketing orientation is positively related to a
customer’s partnering behavior with a 3PL.
Satisfaction with previous outcomes and customer partnering behavior. As
social exchange theory emphasizes, firms engage in relationships because they expect
the benefits to exceed the costs of maintaining them. In exchange relationships, firms
utilize the history of a relationship to anticipate the costs and benefits of continuing
and developing the relationship (Lambe et al 2001). Although most studies include
satisfaction as an outcome variable of relational exchange, Ganesan (1994) considers
satisfaction with previous outcomes as a predictor of relational exchange (long-term
82
orientation is one dimension of relational exchange). Network scholars share the
perspective for this argument, and argue that connections between firms become
closer (i.e., become an “embedded tie”) if expectations are met, or in other words,
some level of satisfaction is achieved (Uzzi 1996). Therefore, if a 3PL customer is
satisfied with its relationship with the 3PL, it is reasonable for the customer to assume
that continuing the relationship is appropriate.
H7: A customer’s satisfaction with past outcomes of the relationship with a 3PL is
positively related to the customer’s partnering behavior with that 3PL.
3.2.2. Antecedents of dependence
Dependence, itself, is caused by a number of factors. Heide and John (1988)
follow Emerson’s (1962) theory of dependence and identify four circumstances in
which dependence is increased: 1) when the outcomes of a relationship are highly
valued; 2) when the outcomes of a relationship are higher than those obtained from
alternative relationships (notion of comparison of outcome levels); 3) when there are
few available alternative sources of exchange (concentration of resources); and 4)
when there are fewer potential alternative sources of exchange.
Ganesan (1994), as well as many other researchers (e.g., Anderson and Narus
1990, Anderson and Weitz 1992, Heide and John 1988), have emphasized the roles of
transaction specific investments and environmental volatility and diversity as
predictors of dependence. However, in the context of logistics outsourcing, it is also
proposed that the nature and complexity of logistics operations will impact the level
of perceived dependence of a customer on a 3PL. If any firm is operating
83
internationally, for example, it has to deal with complicating factors, such as customs
and local import/export regulations. In this case, the expertise of a 3PL may be much
more valued by a customer than if the firm only has domestic operations. Another
example relates to the breadth and complexity of the distribution or sourcing network:
If it is broad and complex, the network should require more expertise than if the
network is simple. Finally, according to the capabilities perspective, a firm’s
consideration of its internal resources and capabilities vis-à-vis the capabilities of
potential partners may also impact the decision to partner (White 2000).
In summary, the antecedents of dependence identified in this study include:
environmental volatility and diversity in the 3PL and product markets, transaction
specific investments, complexity of logistics operations, and internal logistics
capabilities. Figure 6, below, depicts the antecedents of customer and 3PL
dependence. Each of these antecedents is discussed in depth below.
84
Figure 6. Sub-model of antecedents of dependence.
Internal logistical capabilities and dependence. Resource based view and
dynamic capabilities literatures have defined capabilities or distinctive competencies
as “those attributes, abilities, organizational processes, knowledge and skills that
allow a firm to achieve superior performance and sustained competitive advantage”
(Peteraf 1993, Morash et al 1996). In the logistics setting, Morash et al (1996)
propose that logistics capabilities can be divided into two major groups: demand
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
Dependence of
customer on 3PL
TSI by 3PL TSI by 3PL
TSI by customer TSI by customer
3PL
dependence
3PL
dependence
Customer logistics
capabilities
Customer logistics
capabilities
Logistics
complexity
Logistics
complexity
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
-
-
-
+
+
+
+
+
+
-
85
oriented and supply oriented capabilities. Demand oriented capabilities emphasize
customer closeness and responsiveness to the target market, whereas supply oriented
capabilities are related to operational excellence, usually with an internal focus and an
emphasis on cost reduction. They point out that no strategy is necessarily superior to
the other. Each firm’s logistics strategies should be designed to support the firm’s
overall strategies.
The capabilities perspective postulates that a firm’s decision to make, buy, or
ally is generally made after consideration of not solely external competitive factors,
but also internal capability-related factors (White 2000). Each firm has unique
capabilities that incur in unique production costs that by their turn influence strategic
decisions, including the formation and development of interorganizational
relationships. Empirical evidence for this argument is shown in several studies. In a
case study article within a multidivisional firm that produced industrial goods for the
electronics, telecommunications, aerospace and electric power industries, Argyres
(1996) observed that firms vertically integrated into those activities in which they
have greater production experience and/or organizational skills (i.e., capabilities) than
their potential suppliers. Combining internal capabilities and TCE perspectives,
White (2000), in a study of state-owned pharmaceutical firms, found that firms with
prior experience in new compound development were more likely to be involved in
undertaking development activities. His rationale was that these firms had developed
capabilities that allowed them to do so.
The effect of firms’ logistics capabilities on their logistics outsourcing
decisions has been acknowledged in the logistics literature as well. As Gilley et al
86
(2004) point out, it is essential to include both internal and external antecedents of
outsourcing for the development of a general theory of outsourcing. Bolumole (2001)
explains that a firm’s outsourcing strategies will largely depend on the way a firm
perceives its own capabilities compared to its perception of 3PL abilities. Similarly,
Rao et al (1994) argue that one obstacle to the expansion of logistics outsourcing is
that many customers believe that their own departments provide more cost-effective
service than that provided by a 3PL.
Following this logic, this study proposes that a customer’s perception of its
logistical competencies will impact the degree of its perceived dependence on its
3PL. It is proposed that when a customer perceives its logistics capabilities to be
adequate, it feels self-sufficient and not dependent on its 3PL. Conversely, a customer
that has lower logistics capabilities perceives itself to be more dependent on its 3PL.
In this case, the outcomes obtained through the relationship with the 3PL are more
highly valued. It is proposed, then:
H8: A customer’s logistics capabilities are negatively related to a customer’s
dependence on a 3PL.
Environmental volatility, environmental diversity, and dependence.
Decision-making uncertainty refers to the degree to which a firm is not able to predict
or anticipate the environment. In the strategy literature, environmental uncertainty is
linked to different dimensions, such as demand unpredictability or difficulty in
anticipating actions from actual and potential competitors (Boyd, 1990). Following
Ganesan (1994), this research investigates the effects of two key dimensions of
environmental uncertainty - environmental dynamism and environmental complexity
87
– on dependence in buyer-seller relationships. Environmental volatility (or
dynamism) refers to rapid and unpredictable changes or fluctuations in demand in an
industry, representing the level of turbulence or instability facing an environment. In
a highly volatile environment, the difficulty to forecast demand and predict trends
increases substantially. The second dimension is environmental diversity (or
environmental complexity), which is defined as the heterogeneity of resources in an
environment (Boyd, 1990). A diverse environment is composed of many products,
vendors, and competitors.
The effects of these two sources of environmental uncertainty on firm strategy
and behavior have been extensively studied in the strategy, outsourcing, and
relationship marketing literature. Environmental complexity, for example, was found
to have a positive effect on firm linkages in terms of number of interlocks
6
(Boyd
1990). Environmental volatility or dynamism, often called “the strongest determinant
of environmental uncertainty” (Joshi and Campbell 2003), was found to be positively
associated with: 1) relational governance between manufacturers and suppliers (Joshi
and Campbell 2003), 2) outsourcing activities of small firms (Gilley et al 2004) and,
3) degree of modularity
7
(Schilling and Steensma 2001).
The question, then, is why uncertainty leads to dependence. In the retailer-
vendor context, Ganesan (1994) examines dimensions of uncertainty in the retail
market. Ganesan proposes that environmental volatility increases the dependence of a
retailer on a vendor because in a high volatile environment, in which sales fluctuate
6
An interlock between two firms occurs when one director of a firm also sits on the board of directors
of the second firm (direct interlock), or when two firms have representatives on the board of a third
firm (indirect interlock).
7
Usage of “flexible” organizational forms, such as contract manufacturing, alliances, or alternative
work arrangements.
88
and sales forecasts are difficult to predict, retailers may not be able to foresee all
circumstances in a contract. Therefore, they may engage in long term relationships
with vendors in order to prevent possible opportunistic behaviors. On the other hand,
Ganesan proposes that environmental diversity is negatively associated with a
retailer’s dependence on a vendor. He argues that in markets with a variety of
products and alternate vendors, retailers may have difficulty in developing
appropriate strategic programs for each product. The retailers, therefore, may be
encouraged to develop flexible and temporary channel structures with multiple
channel partners.
In the logistics outsourcing setting, it is proposed that environmental volatility
and diversity should be investigated in two distinct markets: 1) the market for the
product the 3PL customer buys from the 3PL; i.e., logistics services, and 2) the
market for the product the 3PL customer sells to its own customers. As the
paragraphs that follow illustrate, the proposed rationale for understanding the effects
of uncertainty differs between these two markets.
In the market for 3PL services, a source of dependence is related to
availability of alternative 3PLs to the one currently used by the customer, i.e., the
diversity of the market for 3PL services. If the customer perceives the 3PL industry to
have many competitors and service offerings (i.e., diverse) it will perceive itself to
have more alternatives to the focal 3PL, reducing the level of dependence on the focal
3PL. On the other hand, if the service offerings in a 3PL market is perceived to be
volatile, due to capacity problems or high demand, the customer may feel itself to be
more dependent on a 3PL (i.e., to lock-in supply).
89
Diversity and volatility in the market for the product the customer ships with
the 3PL (here called the product market) will also impact perceived dependence of on
a 3PL. In the context of logistics operations, when a firm is embedded in a volatile
environment, shipment sizes and locations may change rapidly, leading to higher
complexity in operational planning. (Cooper and Gardner 1993). Having a close
relationship with a 3PL may increase the probability of 3PL assistance in these
circumstances. With respect to environmental diversity (e.g., high level of
competition, short product life cycles), firms may more likely focus on their core
competencies and outsource its logistics functions (Quinn and Hilmer 1994;
Rabinovich et al. 1999). Therefore, a firm will tend to strengthen links with a 3PL
provider in order to gain better control over its operations.
Based on the above discussion, the following hypotheses are presented:
H9: Environmental diversity in the market for 3PL services is negatively related to a
customer’s dependence on a 3PL.
H10: Environmental volatility in the market for 3PL services is positively related to a
customer’s dependence on a 3PL.
H11: Environmental diversity in the product market is positively related to a
customer’s dependence on a 3PL.
H12: Environmental volatility in the product market is positively related to a
customer’s dependence on a 3PL.
Logistics complexity and dependence. With the advent of globalization and
internationalization, many firms have extended their geographic activities and product
scope, and are now dealing with a more diversified range of customers with different
tastes and preferences. These firms must face multifold and simultaneous pressures:
the need for ceaseless innovation to cope with shorter product-life cycles, the
90
requirements for consistent efficiency improvements in order to compete in highly
competitive global markets; and the need to meet customers increasing demands for
on-time performance, more frequent deliveries, etc. These pressures are not restricted
to multinationals. Even those firms focusing on domestic markets must compete with
foreign rivals and develop a global perspective (Mentzer et al 2000). These
challenging requirements increase the complexity of a firm’s logistics operations.
Rao and Young (1994) propose that logistics complexity has to do with the
following: 1) the volume and variety of logistics transactions, impacting both physical
and information tasks; 2) divergence in the number and sequence of transactions that
must be performed for the various products moving in different regions of the world,
and 3) interdependency of tasks within the supply chain process, which places a
premium on co-ordination and control. More specifically, they argue that logistics
complexity is composed of three components that affect the difficulty of coordinating
material and information flows:
Network complexity refers to both the geographic
dispersion of a firm’s trading partners as well as the
intensiveness of transactions with selected trading partners
which can give rise to volume leveraging effects.
Process complexity refers to time and task compression
(or lack thereof) in the supply chain. When the logistics
process is complicated by the number of tasks which have to be
performed and coordinated within a short span of time, such as
in JIT environments, numerous cost/service tradeoffs and
functional interdependency arise in operations.
Product complexity refers to the special circumstances
required by products and materials due to the complexity of the
environment (temperature, humidity, etc.) governing their
transportation, storage and handling. Hazardous materials,
goods with short shelf lives or that are susceptible to damage,
and other physical properties make logistics more difficult.
91
According to the resource dependence perspective, one critical factor that
increases the degree of perceived dependence is the importance of the resource to the
firm (Pfeffer and Salancik, 1978). It is proposed that 3PL customers, whose
businesses involve complex logistics operations in terms of network, process, and
product complexity, perceive logistics to be crucial to their businesses and thus may
perceive themselves to be dependent on the 3PL provider. It is proposed, then:
H13: A customer’s logistics complexity is positively related to a customer’s
dependence on a 3PL.
Transaction-specific investments (TSI) by customer and 3PL, and
dependence. 3PLs and their customers may have to undertake investments in assets
that may be specific to their particular relationships and not be easily deployed in
other relationships. Examples of such investments include: cold storage areas,
customized trailers, special warehouse material-handling equipment (Cooper and
Gardner, 1993), training of warehousing personnel, and the provision of “dedicated
electronic linkups for inventory control for a particular partner’s account” (Knemeyer
et al, 2003). These are called transaction-specific investments – TSI (Williamson,
1981), key considerations in make-or-buy decisions (Aersten 1993) and widely used
as antecedent factors affecting the degree of channel and supply chain integration
(e.g., Wu et al 2004).
TSIs have several relationship stabilizing properties (Wu 2004): for example,
they act as important pledges in the channel relationship and have a positive effect on
the partner commitment to the relationship (Anderson and Weitz 1992); they are
92
useful in minimizing opportunistic behavior, and; they facilitate expectations of
continued exchange (Heide and John, 1990). Indeed, TSIs are the most frequent
demonstration of commitment to a relationship (Rinehart et al 2004). In addition, all
other things being equal, as the need to invest in relationship-specific assets increases,
firms may seek to incorporate additional partnership elements into their relationship
(Cooper and Gardner 1993). Ganesan (1994) argues that TSIs create exit barriers to
the investing party, thus increasing dependence on its partner. In his research setting,
Ganesan proposes that retailer TSIs are positively related to the retailer’s dependence
on a vendor, and that a retailer’s perception of vendor TSIs are negatively related to a
retailer’s dependence on a vendor.
It can be argued that the same rationale holds in the context of 3PL-customer
relationships. A customer that has invested in specific assets, such as capital
investments or in training and equipment (or in the present context, has divested of
assets that are replaced by those of the 3PL) has created exit barriers and may
perceive itself to be more dependent on the 3PL. But 3PLs will often invest in
tangible and intangible assets dedicated to specific customers. In this case, specific
investments made by the 3PL decrease the customer’s perceived dependence on the
3PL because they reduce the threat that the 3PL provider might abandon the
relationship. Therefore, it is hypothesized that:
H14: A customer’s TSIs are positively related to a customer’s dependence on a 3PL.
H15: A customer’s TSIs are negatively related to a customer’s perception of a 3PL’s
dependence on a customer.
H16: A customer’s perception of a 3PL’s TSIs is negatively related to a customer’s
dependence on a 3PL.
93
H17: A customer’s perception of a 3PL’s TSIs is positively negatively related to a
customer’s perception of a 3PL’s dependence on a customer.
3.2.3. Antecedents of trust
The antecedents of a customer’s trust in a 3PL are related to the 3PL behavior
towards the relationship, experience of the customer with the 3PL, and satisfaction
with previous outcomes with the relationship. Figure 7, below, depicts the
antecedents of both dimensions of trust (i.e. credibility and benevolence) to be
discussed in detail in the following paragraphs.
Figure 7. Sub-model of the antecedents of trust
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
3PL reputation
3PL TSI
Satisfaction with
previous outcomes
+
+
+
+
+
+
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
3PL reputation 3PL reputation
3PL TSI 3PL TSI
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
+
+
+
+
+
+
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TSIs by 3PL and trust. A customer’s perception of a 3PL’s specific
investments in a relationship provides a signal that the 3PL can be trusted (Ganesan
1994). An investment specific to a relationship is tangible evidence that a party is
committed to the relationship, and that it cares for such relationship (Anderson and
Weitz 1992). Indeed, these resources directed specifically towards the other party are
the most frequent demonstration of commitment to the relationship (Rinehart et al
2004). In addition, as mentioned earlier, a party that has invested in a relationship has
increased exit barriers and is less likely to exhibit opportunistic behavior (Heide and
John 1990), which are two factors that reduce the level of trust (Morgan and Hunt
1994). Therefore, it is hypothesized that:
H18. A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s credibility.
H19. A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s benevolence.
3PL reputation and trust. Firm reputation is defined as the opinion or
perception that stakeholders have about a firm’s knowledge, honesty, and care
(Doney and Cannon 1997, Deephouse 2000). Reputation is one of the most powerful
factors in acquiring and retaining customers (Jonsson and Zineldin 2003) and has
been referred to as a means to achieve competitive advantage (Barney 1991).
Bharadway et al (1993) refer to reputation as “brand equity” and define it as “a set of
brand assets and liabilities linked to a brand, its name and symbol that add or subtract
from the value provided by a product to a firm and/or that firm’s customers.” They
argue that firms having strong brand names and symbols are better positioned to
95
mitigate customer perceptions over variability in quality. Firms with strong brands
can, therefore, differentiate themselves from the competition.
The reputation of a firm is built over time through the demonstration of
consistent and reliable behavior (Ganesan 1994). Therefore, if a firm enjoys a
credible reputation in a market, it can be inferred that the firm is trustworthy in
relationships. Kwon and Suh (2004), for example, in a survey of members of four
organizations, found a positive relationship between a partner’s reputation in the
market and the level of trust in the partner.
This study follows Ganesan’s (1994) model and proposes that a 3PL’s
reputation will have a positive effect on a customer’s perception of its credibility, but
not on benevolence. As Knemeyer (2000) explains, reputation for fairness and
effective performance is easily transferable across firms. Therefore, when a customer
perceives its 3PL to have a reputation for achieving the desired results and for being
efficient, it is likely that it will trust the 3PL to perform correctly (i.e. credibility). On
the other hand, caring for the partner and demonstrating concern (i.e. benevolence) is
relationship specific. Perceiving this characteristic can only be realized through actual
interaction, not via word-of-mouth communication.
It is proposed that, when a 3PL has the reputation for effective performance, it
is likely that its customers will trust its credibility and its ability to achieve the desired
results (Knemeyer 2000).
H20: The reputation of a 3PL is positively related to its customer’s perception of the
3PL’s credibility.
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Experience with 3PL and trust. Outsourcing logistics activities enables firms
to achieve operational flexibility and efficiency but, on the other hand, requires firms
to develop capabilities in order to coordinate their relationship with the 3PL.
Managing an interorganizational relationship involves using appropriate governance
mechanisms, developing inter-firm knowledge-sharing routines, making appropriate
relationship-specific investments, and initiating necessary changes to the partnership
as it evolves, while maintaining the partner’s expectations (Gulati et al 2000). In the
initial stages of a relationship, lack of experience working with the new partner can
put significant demands on management time, efforts and energy (Zineldin and
Bredenlow 2003). Failure is then more common in the initial period of relationships,
whereas longer relationships are less vulnerable to threats (Bucklin and Sengupta
1993) since older relationships have survived phases of adjustment and
accommodation (Anderson and Weitz 1989). Indeed, as Doney and Cannon (1997)
state, partners within older relationships are more familiar and more comfortable
working with each other.
Based on the above rationale, both relationship marketing (Dwyer, Schurr and
Oh 1987) and network perspectives (Gulati et al 2000) postulate that experience with
the partner is a crucial element in explaining increasing levels of trust and strategic
integration (Wu et al 2004). Relationship marketing scholars, such Dwyer, Schurr
and Oh (1987), argue that as experience with a vendor increases, a vendor-customer
dyad is more likely to have passed through critical shakeout periods in the
relationship. Bucklin and Sengupta (1993), in a study of co-marketing alliances,
argued that a long and stable history of business relations between partners builds
97
trust and commitment, achieving greater effectiveness of the relationship. Heide and
John (1990) found a positive association between the historical length of an alliance
and the expected continuity of future interaction. Network scholars share the same
view. Powell et al (1996)’s empirical work of “cycles of learning” in the
biotechnology industry has shown that initial collaborative relationships trigger the
development of experience in managing ties, thus enabling firms to become more
central in a network. This leads to the continuation of the ties, sustaining a positive
feedback process. In the 3PL context, Skjoett-Larsen (2000) defended the importance
of network theory to better understand the dynamics of third party cooperation, and
emphasized the importance of the exchange and adaptation processes in developing
the 3PL-customer relationship, since past and present experience play a major part in
the development of third party cooperation.
Therefore, it is proposed that experience in a relationship with a 3PL provider
will positively impact the customer’s perception of the 3PL’s credibility and
benevolence. Specifically:
H21: A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s credibility.
H22: A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s benevolence.
Satisfaction with previous outcomes and trust. One of the foundational
premises of social exchange theory is that over time, positive outcomes increase trust
(Lambe et al 2001). As Knemeyer (2000) points out, social exchange theory
postulates that outcomes affect behaviors in subsequent periods. Therefore, with
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mutual exchanges of beneficial action over time, trust and cooperation can be
developed.
Ganesan (1994) proposes that satisfaction with outcomes positively impact the
perception of a partner’s credibility and benevolence. This rationale can be applied to
the 3PL-customer setting. A 3PL customer’s satisfaction is likely to affect the
customer’s perception of the 3PL’s credibility, because it means that the 3PL has
performed its services in an appropriate manner. Similarly, a customer’s satisfaction
is likely to affect the customer’s perception of 3PL benevolence, since it shows that
the 3PL is concerned for the welfare of its customer (Knemeyer 2000). This leads to
the following hypotheses:
H23: A customer’s satisfaction with past outcomes is positively related to the
customer’s perception of the 3PL’s credibility.
H24: A customer’s satisfaction with past outcomes is positively related to the
customer’s perception of the 3PL’s benevolence.
3.3. Hypothesized model
In sum, the overall model of the determinants of customer partnering behavior
in logistics outsourcing relationships (see Figure 8) is composed by the following 24
hypotheses presented in Table 2, below.
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Table 2. List of hypotheses of the determinants of customer partnering behavior
Number Hypotheses
Primary hypotheses
H1
Customer dependence on a 3PL is positively related to a customer’s partnering
behavior.
H2
Perceived dependence of a 3PL on a customer is negatively related to a customer’s
partnering behavior.
H3
A customer’s trust in a 3PL’s credibility is positively related to a customer’s
partnering behavior.
H4
A customer’s trust in a 3PL’s benevolence is positively related to a customer’s
partnering behavior.
H5
Prior partnering experience with 3PLs is positively related to customer’s
partnering behavior with the focal 3PL.
H6
A customer’s relationship marketing orientation is positively related to a
customer’s partnering behavior with a 3PL.
H7
A customer’s satisfaction with past outcomes of the relationship with a 3PL is
positively related to the customer’s partnering behavior with that 3PL.
Antecedents of dependence
H8
A customer’s logistics capabilities are negatively related to a customer’s
dependence on a 3PL.
H9
Environmental diversity in the market for 3PL services is negatively related to a
customer’s dependence on a 3PL.
H10
Environmental volatility in the market for 3PL services is positively related to a
customer’s dependence on a 3PL.
H11
Environmental diversity in the product market is positively related to a customer’s
dependence on a 3PL.
H12
Environmental volatility in the product market is positively related to a customer’s
dependence on a 3PL.
H13
A customer’s logistics complexity is positively related to a customer’s dependence
on a 3PL.
H14 A customer’s TSIs are positively related to a customer’s dependence on a 3PL.
H15
A customer’s TSIs are negatively related to a customer’s perception of a 3PL
dependence on a customer.
H16
A customer’s perception of a 3PL’s TSIs is negatively related to a customer’s
dependence on a 3PL.
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Table 2 (cont.) List of the hypotheses of customer partnering behavior
Number Hypotheses
H17
A customer’s perception of a 3PL’s TSIs is positively negatively related to a
customer’s perception of a 3PL’s dependence on a customer.
Antecedents of trust
H18
A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s credibility.
H19
A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s benevolence.
H20
The reputation of a 3PL is positively related to its customer’s perception of the
3PL’s credibility.
H21
A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s credibility.
H22
A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s benevolence.
H23
A customer’s satisfaction with past outcomes is positively related to the customer’s
perception of the 3PL’s credibility.
H24
A customer’s satisfaction with past outcomes is positively related to the customer’s
perception of the 3PL’s benevolence.
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Figure 8. A model of the determinants of customer partnering behavior in logistics outsourcing relationships
Env. diversity
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
3PL’s credibility
(trust)
Customer
partnering behavior
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Reputation of
the 3PL
TSI by 3PL
TSI by customer
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Customer
capabilities
Logistics
complexity
Prior experience
3PL partnering
Relationship Marketing
Orientation
Env. diversity
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
+
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
TSI by 3PL TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Perception of 3PL’s
dependence on customer
Customer
capabilities
Customer
capabilities
Logistics
complexity
Logistics
complexity
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
+
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
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3.4. Contrasting the model of customer partnering behavior
with Ganesan’s model of long term orientation
This subsection has the objective to highlight the contributions and extensions
that the present model of customer partnering behavior in logistics outsourcing
relationships add to Ganesan’s (1994) model of the antecedents of long term
orientation in buyer seller relationships (Figure 9). As the following paragraphs
describe, the main contributions are related to: 1) the nature of the dependent
variable, 2) consideration of firm-specific factors as primary antecedents of the
dependent variable (i.e. customer partnering behavior), and 3) consideration of firm’s
internal capabilities and firm-specific competitive and operational environments as
antecedents of dependence. Figure 10, next, highlights these elements in the overall
model.
In Ganesan’s (1994) model, the dependent variable is “a retailer long term
orientation” in the relationship with its vendor, which is the expectation that the
relationship will last a long time. In the case of the present model, the dependent
variable is “customer partnering behavior”, which is composed of five dimensions:
extendedness, operational information exchange, operating controls, sharing benefits
and burdens of the relationship, and joint planning. Ganesan’s “long-term orientation”
is conceptually equivalent to “extendedness” in the present model. The dependent
variable “customer partnering behavior” is, thus, a broader representation of relational
behavior, whereas Ganesan’s dependent variable focuses on a single dimension of
relational behavior.
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Figure 9. Ganesan’s (1994) model of long term orientation
The rationale of Ganesan’s (1994) model was primarily based on the premises
of social exchange theory, i.e., interorganizational conditions (trust, dependence, and
satisfaction) are the primary antecedents of relational behavior (long term orientation
in his case). In the model presented in this dissertation, in addition to
interorganizational conditions, firm-specific factors (i.e., experience with partnering
and relationship marketing orientation) are also considered as key antecedents of
relational behavior. The rationale for including firm-specific factors as antecedents of
partnering behavior is drawn from network theory and the strategic orientation
perspective.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
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Figure 10. New variables introduced in the model of customer partnering behavior
Finally, based on the premises of resource dependence theory and transaction
costs economics, the antecedents of dependence in Ganesan’s model include the
effects of environmental diversity and volatility in the vendor’s market and
transaction-specific investments by retailer and vendor. The direct equivalents of
these variables in the present model are environmental diversity and volatility in the
3PL market (the 3PL is the vendor) and transaction specific investments by the
customer and 3PL. In the model presented in this dissertation the competitive and
operational environments of the customer are also considered. Specifically,
environmental volatility and diversity in the market in which the customer operates
(i.e., the product market) are also hypothesized to impact dependence. In addition, the
Perception of 3PL’s
dependence on customer
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
Logistics
complexity
Prior experience
3PL partnering
Relationship Mktg
Orientation
Env. diversity
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
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present model borrows the rationale of the capabilities perspective to introduce a
customer’s internal capabilities as an additional antecedent of customer dependence.
In conclusion, the model presented in this dissertation is a more complete
representation of the antecedents of relational behavior (more specifically, partnering
behavior). Moreover, it provides a combination of key theoretical perspectives which
have been shown to explain relational behavior.
3.5. Conclusions
This chapter presented the rationale for the conceptual model of customer
partnering behavior in logistics outsourcing relationships and for the hypotheses that
compose the model. The model was developed in light of relationship marketing,
especially social exchange theory and relationship marketing orientation. It included
interorganizational conditions and firm specific factors as well. The proposed main
antecedents of a 3PL customer’s partnering behavior are: 1) a customer’s perceived
dependence on a 3PL; 2) a customer’s trust in a 3PL’s credibility and benevolence; 3)
a customer’s prior experience with partnering with other 3PLs and; 4) a customer’s
relationship marketing orientation. The antecedents of dependence are hypothesized
to be: environmental volatility and diversity in the 3PL and product markets,
transaction-specific investments by the customer and the 3PL, complexity of logistics
operations, and a customer’s internal logistics capabilities. The proposed antecedents
of trust are: 3PL reputation, experience with the 3PL, satisfaction with previous
outcomes, and transaction-specific investments undertaken by the customer.
In Chapter 4, the methodological steps that were followed in order to test the
above hypotheses are described.
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Chapter 4: Methodology
This chapter details the methodology that was followed in order to address the
research questions discussed in the previous chapter. First, the selected research
design for the study is presented. The research structure has shaped the choice of
measures for the variables, as well as the methods of data collection and analysis.
A survey instrument was used in this study. The survey design and
implementation followed the steps described under the survey procedure by Dillman
(2000) - The Tailored Design Method. Following the research design subsection, in
accordance with Dillman’s method, this chapter details the operationalization and
measurement of the constructs and variables, as well as the survey design and
implementation.
A detailed description of all steps used in the data analysis, including the
treatment of possible non-response bias and the quantitative methods adopted, is
presented in Chapter 5.
4.1. Research design
This study utilized a non-experimental design
8
, testing a cross-sectional model
through a survey instrument, which is a standard procedure in the marketing
literature. The survey was conducted with the customer firms of a large Brazilian 3PL
provider called Rapidão Cometa (www.rapidaocometa.com.br). It is an asset-based
company that has been operating for over 60 years. Originating as a traditional
carrier, this firm has transformed itself into a logistics provider, offering a wide
8
i.e., no treatments are given: naturally occurring variation in the independent and dependent
variables without any intervention (by the researcher or anyone else) are used to conduct the research.
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variety of services ranging from traditional transportation and warehousing to supply
chain solutions. This 3PL has wide geographic coverage in Brazil, and has access to
numerous international locations through an operational partnership with a major
global 3PL. Its customer base comprises firms from various industries, sizes and
markets, thus offering a good picture of the Brazilian logistics outsourcing industry.
The utilization of a survey instrument is necessary given that the majority of
the variables in the model are perceptual measures of behavior that cannot be
captured by secondary/archival data. In addition, one of the purposes of this research
is to adapt and test Ganesan’s (1994) model of determinants of long-term orientation
in buyer-seller relationships to the context of logistics outsourcing. Therefore,
utilizing the same type of methodology is appropriate.
Given that the study was to be conducted with Brazilian firms, performing a
traditional mail survey from Maryland was not feasible. In addition, electronic
surveys present certain advantages, such as faster delivery, faster data collection,
higher response rates, and low cost (Griffis, Goldsby, and Cooper 2003). Therefore, a
web-based survey instrument was considered to be the most efficient means to
acquire information from the 3PL customers.
Rapidão Cometa sent this researcher its list of customers comprising 4,523
firms. The list contained the names of the individuals who manage their companys’
accounts with Rapidão Cometa. It also contained the following information: company
name, industry, position of the contact, e-mail address of the contact, city and state of
company location. The unit of analysis is the firm, with one key informant.
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4.2. Measurement of the constructs
This section describes the measurement items of the constructs to be tested.
Figure 11 depicts the model. The dependent construct is the customer partnering
behavior in its relationship with a 3PL. The main predictors are the customer’s
dependence on the 3PL, the perceived 3PL dependence on the customer, the
customer’s trust in the 3PL (decomposed into two parts – 3PL credibility and
benevolence), the customer satisfaction with previous outcomes of the relationship,
the customer’s prior experience with outsourcing, and the customer’s relationship
marketing orientation.
Dependence and trust are hypothesized to have specific antecedents. The
antecedents of 3PL and customer dependence are the customer’s perception of:
environmental volatility and complexity in the 3PL market and in the market of the
products its ships with the 3PL (i.e., the product market), transaction-specific
investments (TSI) by both customer and 3PL, the customer’s internal logistical
capabilities, and the logistics complexity of the customer’s operations. The
antecedents of both components of trust – credibility and benevolence - are the
customer’s perception of: transaction specific investments, reputation of the 3PL,
customer experience with the 3PL, and customer satisfaction with previous outcomes
of the relationship.
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Figure 11. Model of customer partnering behavior in logistics outsourcing relationships.
The measures for most constructs were adapted from existing research and
have been previously tested for validity and reliability. Since one objective of this
study is test for the reliability of Ganesan (1994)’s study in the 3PL context, all items
for identical constructs were adapted from his study. It is relevant to point out that
Ganesan’s measures have been extensively adopted in subsequent articles, generally
presenting strong convergent validity. The remaining measures were adapted from
studies in relationship marketing (e.g., Sin et al 2005) and logistics (e.g., Rao and
Young 1994, Morash 1996, Gardner et al 1994). For two constructs - logistics
complexity and logistical capabilities - items were created rather than adapted.
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In addition, a number of the variables not directly associated with the study
were included in the questionnaire in order to provide demographic information and
data for future research. These variables include: position of respondent, professional
experience of respondent, number of functions outsourced, various measures of
performance, number of logistics providers currently used by the respondent’s firm,
and demographics of the respondents’ firm (e.g. number of employees, annual sales).
In the paragraphs that follow, the measures for the constructs directly included
in the model are presented.
4.2.1. Dependent construct: customer partnering behavior
Customer partnering behavior. The measures for customer partnering
behavior were adapted from Gardner et al (1994). Parterning is a behavior style that
occurs along the continuum between arm’s-length and vertical integration, and is
composed of five dimensions: extendedness, operational information exchange,
operating controls, sharing benefits and burdens, and planning. The fifteen-item,
seven-point Likert scales (anchored by strongly disagree (1) and strongly agree (7))
are as follows.
Extendedness (EXT)
We expect our relationship with Rapidão Cometa to last a long time.
We are very loyal to Rapidão Cometa.
Maintaining a long-term relationship with Rapidão Cometa is important to us.
Operational Information Exchange (OIE)
We conduct many transactions via computers with Rapidão Cometa.
We exchange operational information with Rapidão Cometa.
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We use software dedicated to our relationship with Rapidão Cometa. (i.e., EDI)
Operating controls (OCL)
We require shipment tracking ability.
We frequently request delivery control reports.
We request damage/lost control reports.
Sharing of benefits and burdens (SSB)
We are willing to help Rapidão Cometa in difficult situations.
We share risks with Rapidão Cometa.
We have a high willingness to handle unexpected situations by negotiation.
Planning
Rapidão Cometa and our company interact in the activities planning.
We and Rapidão Cometa exchange information that helps establishment of business
planning.
We regularly study Rapidão Cometa's operations for planning.
4.2.2. Primary antecedents
Dependence on 3PL. Dependence items assess the customer’s need to
maintain the relationship with the 3PL in order to achieve desired goals (Frazier
1983). The two measures for customer dependence were adapted from Ganesan
(1994). The first is composed of six item, 7-point Likert scale measures (anchored by
strongly disagree (1) and strongly agree (7)). The second measure refers to the
percentage of the customer’s business for which the 3PL is responsible. The items
are:
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Measure 1
1) Rapidão Cometa is crucial to our performance.
2) Rapidão Cometa is important to our business.
3) If our relationship with Rapidão Cometa were discontinued, we would have
difficulty in performing its services.
4) It would be difficult for us to replace Rapidão Cometa.
5) We are dependent on Rapidão Cometa.
6) We do not have a good alternative to Rapidão Cometa.
Measure 2
What is Rapidão Cometa’s approximate share of your outsourced logistics
expenditures? ___%
Perception of 3PL provider’s dependence on customer. This construct was also
adapted from Ganesan (1994). The three-item, 7-point Likert-scale measures
(anchored by strongly disagree (1) and strongly agree (7)) are:
1) We are important to Rapidão Cometa.
2) We are a major customer for Rapidão Cometa in our trading area.
3) We are not a major customer for Rapidão Cometa (R).
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Trust. Following Ganesan (1994), trust is decomposed into two major
components: credibility and benevolence. Credibility is based on the extent to which
the customer believes that the 3PL has the required expertise to perform the job
effectively and efficiently. Benevolence is related to the customer’s beliefs in the
3PL’s good intentions and motives towards the customer. Therefore, two latent
constructs are tested. Credibility is composed of 7 items, whereas benevolence is
composed of 5 items. All items are measured by Likert scales (anchored by strongly
disagree (1) and strongly agree (7)).
Credibility
Rapidão Cometa's representative …
1) … has been frank in dealing with us.
2) … makes reliable promises.
3) … is knowledgeable regarding his services.
4) … does not make false claims.
5) … is not open in dealing with us. (R)
6) … is honest about the problems may they arise.
7) … has difficulties answering our questions. (R)
Benevolence
Rapidão Cometa's representative …
1) … has made sacrifices for us in the past.
2) … cares for us.
3) … has supported us in times of shortages.
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4) … is like a friend.
5) … has been on our side.
Satisfaction with previous outcomes. As social exchange theory emphasizes,
firms engage in relationships because they expect the outcomes to be rewarding.
Therefore, firms that are satisfied with their 3PLs are more likely to exhibit partnering
behavior in the relationships with their 3PLs. The seven measures are adapted from
Ganesan (1994) and are measured by Likert scales (anchored by strongly disagree (1)
and strongly agree (7)).
• Are you satisfied with the services provided by Rapidão Cometa? Please
describe your opinion with respect to the outcomes with Rapidão Cometa in
the past year:
Last year…
1) … we were pleased with the outcomes.
2) … working with Rapidão Cometa was very useful.
3) … Rapidão Cometa was ineffective. (R)
4) … we were dissatisfied. (R)
5) … the outcomes were outstanding.
6) … the outcomes were of bad value for our company (R)
7) … we were comfortable in working with Rapidão Cometa.
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Prior experience with partnering with 3PLs. This variable measures the number
of years that the firm has been partnering with 3PLs in general, not necessarily with
Rapidão Cometa. It is a continuous variable.
Has your company ever partnered with logistics providers? ___ Yes ___ No
If yes, how many years has your company partnered with other logistics providers (in
general, not necessarily with Rapidão Cometa)? ____ years.
Relationship Marketing Orientation. According to Sin et al (2005), RMO is
considered to be composed of six dimensions: trust, bonding, communication, shared
value, empathy, reciprocity. The 22-item, 7-point Likert-scale measures (anchored by
strongly disagree (1) and strongly agree (7)) are:
• The following sentences describe the relationship between your company and
your company’s major customers (attention: NOT Rapidão Cometa). Please
indicate your level of agreement.
Trust
1. We trust each other
2. They are trustworthy on important things.
3. According to our past business relationship, my company thinks that they are
trustworthy persons.
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4. My company trusts them.
Bonding
5. We rely on each other.
6. We both try very hard to establish a long-term relationship.
7. We work in close cooperation.
8. We keep in touch constantly.
Communication
9. We communicate and express our opinions to each other frequently.
10. We can show our discontent towards each other through communication.
11. We can communicate honestly.
Shared value
12. We share the same worldview.
13. We share the same opinion about most things.
14. We share the same perspectives toward things around us.
15. We share the same values.
Empathy
16. We always see things from each other’s view.
17. We know how each other feels.
18. We understand each other’s values and goals.
19. We care about each other’s feelings.
Reciprocity
20. My company regards “never forget a good turn” as our business motto.
21. We keep our promises to each other in any situation.
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22. If our customers gave assistance when my company had difficulties, then I
would repay their kindness.
4.2.3. Antecedents of dependence
Customer Transaction-specific investments. The customer specific investments
are tangible and intangible assets that are particular to the relationship and cannot be
easily redeployable. Examples of specific assets in the logistics setting undertaken by
3PL customers are dedicated software, personnel training, etc. The items are adapted
from Ganesan (1994) and are measured by a 7-point Likert scale (anchored by
strongly disagree (1) and strongly agree (7)).
1) We have made significant investments (e.g., technology, training etc.)
dedicated to our relationship with Rapidão Cometa.
2) If we switched to a competing logistics provider, we would lose a lot of the
investment we have made in this relationship.
3) We have invested substantially in personnel dedicated to this relationship.
4) If we decided to stop working with Rapidão Cometa, we would be wasting a
lot of knowledge regarding its method of operation.
Perception of 3PL’s specific investments. The items for this construct were
adapted from Ganesan (1994) and are measured by a 7-point Likert scale (anchored
by strongly disagree (1) and strongly agree (7)).
1) Rapidão Cometa has gone out of its way to link us with its business.
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2) Rapidão Cometa has tailored its services and procedures to meet the specific
needs of our company.
3) Rapidão Cometa would find it difficult to recoup its investments in us if our
relationship were to end.
Environmental diversity in the product market. Environmental diversity or
complexity is related to the heterogeneity and concentration of resources in an
environment. The measurement of the construct is borrowed from Ganesan (1994)
and the items are measured by a 7-point Likert scale (anchored by strongly disagree
(1) and strongly agree (7))
• How would you describe the market for the product you ship with Rapidão
Cometa?
1) There are many new products.
2) There are many new competitors.
3) The market is very complex.
Environmental volatility in the product market. Environmental volatility (or
dynamism) represents the level of turbulence or instability facing an environment,
and is related to unpredictable changes and fluctuations in demand in an industry. The
measurement of the construct is borrowed from Ganesan (1994). The items are
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measured by a 7-point Likert scale (anchored by strongly disagree (1) and strongly
agree (7))
• How would you describe the market for the product you ship with Rapidão
Cometa?
1) The demand is unpredictable.
2) Sales forecasts are accurate. (R)
3) The industry production is stable. (R)
4) The demand trends are easy to monitor. (R)
Environmental diversity in the market for 3PL services. 3PL environmental
diversity is related to the alternatives that customers have to the focal 3PL (i.e.,
competition in the 3PL industry). The scales are the same here as they are for the
customer’s environmental diversity, only with modifications to suit the 3PL industry.
• How would you describe the market for logistics services in Brazil?
The market for logistics services in Brazil…
1) … has many service offerings.
2) … has many carriers/logistics providers.
3) … is very complex.
Environmental volatility in the market for 3PL services. 3PL environmental
volatility is related to the instability in the availability of services in the 3PL industry.
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In the current situation of carrier and port capacity constraints, for example, the
availability of services cannot be taken for granted. The scales are the same here as
they are for the customer’s environmental volatility, only with modifications for the
3PL industry.
• How would you describe the market for logistics services in Brazil?
The market for logistics services in Brazil…
4) … has an unpredictable demand.
5) … has a stable service availability. (R)
6) … is easy to monitor. (R)
Logistics complexity. Rao and Young (1994) suggest that logistics complexity is
composed of three components that affect the difficulty of coordinating material and
information: 1) network complexity (e.g., geographic dispersion and intensiveness of
transactions); 2) process complexity (e.g., time and task compression in operations);
3) product complexity (i.e., special handling and transporting requirements). The
measures adopted here follow these three dimensions. The items are measured by a 7-
point Likert scale (anchored by strongly disagree (1) and strongly agree (7)).
• The following items describe the complexity of your company’s logistics
operations. Please indicate your level of agreement.
1) We have a complex network of trading partners.
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2) The timeliness of the transactions in our supply chain is crucial in our
business.
3) We must accomplish very short order cycle times for customer orders.
4) We have a complex network of origin/destination (OD) pairs.
5) Our products require specialized transportation, storage, or handling
(eg. temperature, humidity, etc.).
Internal logistics competencies. The following items aim to capture the extent to
which the firm dedicates human resources to the management of logistics operations
and to what extent these professionals possess knowledge to manage the operations
and overcome problems. The items are measured by a 7-point Likert scale (anchored
by strongly disagree (1) and strongly agree (7)).
• The following items describe the logistics personnel of your company.
Please indicate your level of agreement.
1) Relative to the size of our firm, we have a large group of upper-level
managers dedicated to logistics.
2) Relative to the size of our firm, we have a large group of employees
across all levels dedicated to logistics.
3) Our logistics personnel have a deep understanding of our logistics
operations.
4) Our logistics personnel know where problems and bottlenecks might
exist in our logistics operations.
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5) Our logistics personnel are capable of finding effective solutions when
problems arise.
4.2.4. Antecedents of trust
3PL Reputation. The items for the construct measure the extent to which the
customer perceives the 3PL to enhance the welfare of its customers. The four items
were adapted from Ganesan (1994) and are measured by a 7-point Likert scale
(anchored by strongly disagree (1) and strongly agree (7)).
1) Rapidão Cometa has a reputation for being honest
2) Rapidão Cometa has a reputation for being concerned about its customers
3) Rapidão Cometa has a bad reputation in the market (R)
4) Most customers think that Rapidão Cometa has a reputation for being fair.
Customer experience with 3PL. Following Ganesan (1994), customer
experience with the 3PL is measured by the number of years the customer has been
associated with the 3PL.
How many years has your company worked with Rapidão Cometa? ____ years. (e.g.,
2.5)
Transaction-specific investments by 3PL. (presented under “Antecedents of
dependence,” above)
Satisfaction with previous outcomes. (presented under “Primary
antecedents,” above)
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4.3. Survey design
After selecting the items for the constructs of interest, the next step was to
design the questionnaire, which involved not only the appropriate arrangement of
questions, but also the presentation letters, conduction of pretests to guarantee the
quality of content, ease of understanding and visual appeal of the questionnaire and
computer interface to the respondent, among other factors. According to Dillman
(2000), the questionnaire design has two main objectives: to reduce non-response and
to reduce or eliminate measurement error. Structure and visual appeal can be equally
important. Dillman (2000) points out that while a respondent-friendly appearance and
a good structure can improve response rates, a poor questionnaire layout can cause
questions to be overlooked. Therefore, it is important to keep the wording and visual
appearance of questions simple.
In terms of survey structure, Dillman (2000) emphasizes that the order of the
questions is crucial. The questions should be grouped in a general way from the most
salient to the least salient to the respondent. Moreover, the order must be logical to
the respondent, as if a conversation were taking place. Therefore, before each group
of questions, a general explanation has been included in order to clarify the logic flow
of the questionnaire to the respondent. Also, special attention should be paid to the
first question, which can impact the desirability of the respondent to complete the
questionnaire. It should be appealing to the respondent. The question regarding the
degree of partnering was selected to be first, while the demographic information was
positioned at the end of the questionnaire.
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Once the survey instrument (questionnaire plus letters) was created, several
pretests were implemented. The pretests involved four steps:
Review by experts. The survey instrument was refined with the aid of
feedback provided by logistics experts (professors with knowledge of logistics
outsourcing research and doctoral students experienced in survey research) in order to
finalize substantive content. Experts in logistics with experience in survey research
can identify problematic questions in terms of response rate or understandability. The
objective in this phase was to assure that all necessary questions were included and
that they were consistent with prior studies.
Think-aloud interviews. A think-aloud interview is a common technique in
which the respondent answers the questionnaire in the presence of the interviewer and
is asked to tell the interviewer whatever he/she is thinking from the moment he/she
opens the email until the questionnaire is finished and sent. After reviewing the
survey for substantive content, a first series of think-aloud interviews was conducted
with three doctoral students to assess possible inconsistencies in wording and
structure. In other words, the objective was to evaluate whether the respondents could
understand and answer all questions, and whether the e-mails and questionnaire on
the website created a positive impression. After this first series of interviews, the
survey was revised and translated into Portuguese. In order to prevent possible
translation bias, a Brazilian marketing scholar who works in the U.S., thus being
fluent in both languages and in marketing, reviewed the original and translated the
survey instrument. Other professionals, who are knowledgeable in both Portuguese
125
and English, as well as in transportation and logistics, kindly agreed to review the
survey translation. Minor modifications were needed.
Final check. Next, the website was created with special attention to the ease
and comfort of the user. The online survey instrument was designed in a way that
groups of questions were presented together. Therefore it resembled the experience of
using the Internet. It was possible to interrupt and return to the survey website for
completion at any time. Once the website was ready, it was completed by industry
professionals not involved in any phase of the development or review of the
questionnaire or website. Minor adjustments were made.
Pretest with a reduced sample. The final phase of the pretest involved
conducting the survey online with a reduced sample. The objective was to identify
operational problems in the software utilization by the respondents, as well as in the
implementation of the survey itself. Four hundred customers were randomly selected
from the customer base. Rapidão Cometa sent them an e-mail in which they were
invited to visit a website and provide their names and e-mails if willing to participate
in the survey. One hundred, eighteen emails were returned due to non-existent e-mail
addresses, implying that 282 firms received the invitation. Forty-three e-mail
respondents agreed to participate in the project and received the link to the website.
Sixteen respondents completed the questionnaire, corresponding to a response rate of
5.67%. No problems were encountered and no modifications were made to any part
the survey instrument.
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4.4. Survey implementation
A major objective of carefully planning the survey implementation is to
reduce the non-response rate. According to Dillman (2000), repeated contacts with
potential respondents have been shown to be the most effective strategy in increasing
the response rate. His “tailored design” method of implementation includes: a
“respondent-friendly” questionnaire, up to five contacts with the recipient, plus a
financial incentive sent with the survey request (in the present case, the chance of
“winning” an iPod).
Given that in the pretest no problems were encountered in administering the
survey, the next step was to follow the same process with the remaining customers in
the database. An important point was to make each contact with respondents unique,
since it has been shown that a variety of stimuli are generally more powerful than a
repetition of previously used techniques in increasing response rate (Dillman 2000). It
is also relevant to point out that during the contact period, attention was given to
sending individualized messages (not showing multiple recipient addresses or a
listserv origin).
The survey implementation activities can be summarized as follows:
First contact: pre-notification e-mail. According to Dillman, this is important
for Internet surveys, given the ease in discarding e-mail messages. Following
Dillman’s recommendations, the email was aimed at building anticipation rather than
providing the details and conditions for participation in the survey. In the study, the
first contact began with a pre-notification e-mail sent by Rapidão Cometa in order to
guarantee that our source was trustworthy, to emphasize the confidentiality of the
127
responses, and to express support for the study. In this e-mail, Rapidão Cometa
invited the firms to access a website (created by this researcher) and to provide their
e-mail addresses in order to participate in the study. The e-mail also included a brief
description of the study and its purpose. The e-mail was sent to a total of 2,649
customer firms
9
. Three hundred, thirty-five customer firms accepted Rapidão
Cometa’s invitation, provided their contact information, and received the link to the
website.
Second contact: e-mail with link to website. This e-mail was sent to the 335
firms that responded to Rapidão Cometa’s invitation. It was sent few days after the
pre-notification e-mail. The e-mail contained a letter describing the objective and
importance of the study, emphasizing the confidentiality of the responses. In addition,
the possibility of receiving a gift was indicated.
Follow-up contacts: thank you/reminder e-mails. Thank you e-mails were
sent to all firms that completely filled out the questionnaire. Reminder e-mails and
announcements of the gift winners were sent once a week during a four week period
to all contacts on the e-mail list. It is interesting to note that once winners were
selected and announced to the entire contact list, a temporary increase in the number
of respondent replies was observed.
In total, 265 firms filled out the survey completely, representing a response
rate of 79.1% of those that accepted Rapidão Cometa’s invitation (or 10.0 %, of the
entire customer base that received Rapidão Cometa’s invitation net of the emails that
bounced back).
9
In reality 4,123 e-mails were sent, of which 1,474 “bounced back” as being unknown e-mail
addresses.
128
Short version of the survey for non-respondents. Finally, in order to test for
non-response bias, a short version of the survey composed of 13 theoretically
meaningful items was sent to two groups of non-respondents: 1) those who accepted
the invitation but did not fill out the survey completely, and 2) those who did not
accept Rapidao Cometa’s invitation. In total, 5 customers from the first group and 93
customers from the second group filled out the short version of the survey.
4.5. Conclusions
This chapter presented the research methodology used to test the hypotheses.
The measurement of the variables was defined. A web-based survey instrument was
developed and pre-tested prior to its final implementation. A short version of the
survey was also implemented with the objective to test for non-response bias. All
steps followed in the data analysis, along with the model results, are presented in
Chapter 5.
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Chapter 5: Data Analysis and Results
This chapter presents a detailed description of all steps followed to analyze the
data and test the hypotheses proposed in this study. First, the characteristics of the
respondents are examined, followed by the descriptive statistics of the variables and
constructs. Next, the tests for non-response bias are presented. Finally, all quantitative
procedures and tests conducted during the structural equation modeling process are
discussed, along with the model results.
5.1. Final sample and respondents characteristics
As outlined in the previous chapter, 16 firms completed the survey during the
pretest phase and 265 firms completed the survey during the survey implementation
phase. Given that no modifications were made in the survey instrument between the
phases, and given that the pretest and survey were implemented consecutively (i.e.,
during the months of August and September), the combination of both response
groups was considered as the final sample. In total, a final sample size of 281
observations was used.
The position profile of the respondents was fairly diverse. The respondents
were mostly logistics supervisors, logistics managers, general managers, CEOs, and
partners (see Table 3). Considering that these individuals were Rapidão Cometa’s
contacts for coordination of their activities, and they were professionals in the
management level, this might indicate that the respondents were knowledgeable about
their company’s relationship with Rapidão Cometa. This implies that key informant
130
bias may not be a concern in this study. These firms belonged to a variety of
industries, such as (Table 4): apparel (18.5%), health care (6.4%), automotive and
auto parts (5.7%), electronics (5.7%), cosmetics (5.3%), telecommunications (4.3%),
food and beverage (5.0%), and others.
Table 3. Position profile of the respondents
Position Count %
President/CEO/COO 18 6.41%
Owner/Partner 19 6.76%
Logistics director 8 2.85%
Logistics manager 53 18.86%
Logistics supervisor 35 12.46%
Logistics employee 23 8.19%
Logistics Analyst 14 4.98%
General manager 29 10.32%
Procurement manager 16 5.69%
Director 7 2.49%
Sales supervisor 5 1.78%
Sales manager 2 0.71%
Other 24 8.54%
Not informed 28 9.96%
Total 281 100.00%
Table 4. Respondents’ Industries
Industry Count %
Apparel 52 18.51%
Health care/Pharmaceutical 18 6.41%
Auto/Auto Parts 16 5.69%
Electronics 16 5.69%
Cosmetics 15 5.34%
Food and Beverage 14 4.98%
Chemicals and Plastics 14 4.98%
Telecommunications 11 3.91%
Retail 11 3.91%
Service 11 3.91%
Other 77 27.40%
Not informed 26 9.25%
Total 281 100.00%
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Almost 75% of the sample was composed of small firms with fewer than 250
employees. Larger firms with more than 1,000 employees composed less than 10% of
the sample. The complete distribution is found in Table 5. The small size of the firms
in the sample can be also seen by observing their annual sales distribution
10
. Of the
respondent firms, 18.5% had annual sales of less then US$ 0.5 million, 31.3% of the
firms had annual sales that ranged from US$ 0.5 to US$ 4.3 million, and 10.3% of the
firms had annual sales ranging between US$ 4.3 to 11.4 million. The remaining
respondents had annual sales greater than US$ 11.5 million, of which only 6% had
annual sales greater than US$ 120 million.
Table 5. Number of employees of the respondent firms
Number of Employees Quantity %
Fewer than 100 144 51.25%
100 - 249 53 18.86%
250 - 499 19 6.76%
500 - 999 15 5.34%
1,000 - 2,499 13 4.63%
2,500 - 4,999 5 1.78%
5,000 - 9,999 3 1.07%
more than 10,000 1 0.36%
Not informed 28 9.96%
Total 281 100.00%
10
The “unusual” breakdown of sales categories is a result of converting from the Brazilian currency
“Real” (R$) to U.S. dollars.
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Regarding the respondents’ logistics outsourcing practices, more than half of
the sample (52.7%) outsourced only one logistics function, while about 5% of the
firms outsourced 6 or more functions. The remaining firms outsourced from 2 to 5
functions (Table 6). The vast majority of firms outsourced transportation operations,
which seems to be the strongest capability of the 3PL. Other outsourced functions
were transportation planning, freight consolidation, and distribution to final customer
(see details on Table 7).
Table 6. Number of functions outsourced
Number of
functions
# Firms %
1 148 52.67%
2 38 13.52%
3 27 9.61%
4 12 4.27%
5 9 3.20%
6 6 2.14%
7 3 1.07%
8 2 0.71%
9 1 0.36%
10 0 0.00%
11 1 0.36%
Not informed 34 12.10%
Total 281 100.00%
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Table 7. Respondents’ logistics functions outsourced
Activity # Firms
Transportation operations 216
Freight consolidation 48
Final consumer distribution 43
Freight bill payment 34
Warehousing 26
Reverse logistics 22
IT systems 14
EDI capability 14
Traffic control (distribution) 14
Transportation planning 13
Network/route optimization 8
Inventory management/control 7
Order management 7
Cross-docking 6
Traffic control (supply) 5
Packaging 3
After-sale service distribution 3
Pick & pack operations 2
Lead logistics management 1
5.2. Descriptive statistics of the constructs
Table 8 provides the means and standard deviations of the constructs
11
. It can
be noted that, in general, the variable averages were slightly above the central point of
the Likert scale (i.e., 4) and presented good variability. Customer Transaction
Specific Investments (Customer TSI) presented the highest standard deviation
(1.684). Relationship Marketing Orientation (RMO), however, presented a smaller
standard deviation compared to the other constructs. Since some of the variable
means were located to the right of the central point of the 7-point Likert scale, there is
11
The value of each construct for each observation was calculated as the average of the scale items.
134
an indication that some distributions might be skewed (to be tested in the following
sections). For this reason, the robust estimation technique might need to be employed
in order to correct for skewed data.
Table 8. Descriptive statistics of the constructs
Construct Mean
Std.
Dev.
Customer partnering behavior
behabehavior
4.587 1.131
Satisfaction 5.179 1.375
Credibility 5.867 1.210
Benevolence 4.990 1.556
Reputation 5.966 1.074
Customer dependence 3.772 1.358
Customer TSI 2.797 1.684
3PL TSI 3.436 1.623
3PL dependence 4.983 1.485
Volatility product market 3.198 1.491
Diversity product market 5.083 1.334
Volatility 3PL market 3.886 1.253
Diversity 3PL market 4.241 1.160
Logistics complexity 5.237 1.323
Logistics capabilities 4.787 1.648
RMO 5.892 0.803
Table 9 presents the correlation table between the constructs. It should be
noted that most of the statistically significant correlations had small values. Many
other correlations were not statistically significant, which should have implications
for the model fit to be tested later in this chapter.
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Table 9. Correlation matrix for the averages of the constructs
Construct
L
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A
P
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L
P
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P
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3
P
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3
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X
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R
M
O
Customer logistics capabilities
Volatility product market -0.033
Diversity product market 0.052 0.154
Volatility 3PL market 0.043 0.219 0.144
Diversity 3PL market 0.032 0.078 0.234 -0.089
Logistics complexity 0.448 0.079 0.115 0.022 -0.009
Customer TSI 0.261 -0.114 -0.142 -0.171 -0.039 0.290
3PL TSI 0.282 -0.109 -0.084 -0.124 -0.054 0.377 0.716
3PL reputation 0.072 0.092 0.057 -0.045 0.173 0.125 0.105 0.208
Experience with 3PL 0.104 -0.058 0.047 -0.010 -0.034 0.162 0.156 0.159 0.081
Satisfaction 0.132 -0.030 0.030 -0.187 0.117 0.071 0.218 0.299 0.457 0.058
Dependence 0.169 -0.067 -0.025 -0.129 0.050 0.290 0.686 0.636 0.298 0.196 0.422
3PL dependence 0.315 -0.021 0.080 -0.055 0.150 0.360 0.235 0.413 0.260 0.086 0.332 0.357
3PL credibility 0.101 0.010 0.082 -0.148 0.138 0.177 0.151 0.247 0.573 0.077 0.460 0.313 0.231
3PL benevolence 0.263 -0.031 -0.096 -0.188 0.113 0.262 0.372 0.478 0.426 0.149 0.411 0.489 0.372 0.583
Experience partnering 0.203 0.000 0.044 0.010 0.030 0.069 -0.156 -0.145 0.076 0.096 0.008 -0.117 0.078 -0.019 -0.021
RMO 0.281 -0.021 0.076 -0.157 -0.068 0.320 0.178 0.127 -0.013 0.128 0.081 0.054 0.159 0.007 0.096 0.070
Customer partnering behavior 0.383 -0.039 -0.003 -0.127 0.122 0.318 0.508 0.533 0.388 0.168 0.352 0.565 0.360 0.370 0.522 0.125 0.128
Observation: The figures in bold are statistically significant at the 0.05 level.
136
5.3. Tests for non-response bias
Before any analysis can be performed, a test for non-response bias must be
conducted. Non-response bias occurs when the answers of the respondents are
statistically different from the answers of the non-respondents (Lambert and
Harrington 1990). Testing for non-response bias is critical to the generalizability of
the research findings.
Two standard methods were used to test for non-response bias in this study:
1
st
method: Comparison of early and late respondents. From the software it was
possible to identify the date when each respondent had finalized the survey
instrument. Figure 12 presents a graph with the counts of respondents per day. A first
wave of respondents was identified from September 2
nd
to 5
th
, comprising 94
respondents. This first group was considered as the early respondents group. The last
94 respondents were considered as the late respondents. The thirteen key non-
demographic questions provided in the short version of the questionnaire were
compared through a Two Group Hotelling T-Squared Test - Manova (Table 10). The
test showed no statistical significance between the vector of early and late
respondents. This result indicated that the null hypothesis that the vectors are equal
could not be rejected. Therefore an absence of response bias between early and late
respondents was inferred.
137
Figure 12. Daily counts of survey completion.
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Table 10. Comparison of vector means between early vs. late respondents
Item
Mean
(Early)
Mean
(Late)
Extendedness 6.28 6.42
Operational Information
Exchange
4.42 4.11
Operating Controls 5.24 5.04
Sharing Benefits and Burdens 6.03 5.69
Planning 4.08 3.60
Dependence 5.43 5.65
3PL Dependence 5.65 5.35
3PL Credibility 6.09 6.22
3PL Benevolence 5.48 5.32
RMO 6.49 6.10
Customer TSI 2.37 2.63
3PL reputation 2.02 1.79
Satisfaction 5.15 5.40
2-group Hotelling’s T-squared = 19.056
F test statistic = 1.3612, p = 0.1841.
2
nd
method: According to Lambert and Harrington’s (1990) method of testing for
non-response bias, a random sample of the non respondents should be selected and
contacted to answer the same set of questions used when examining the early and late
138
respondents. Their results then should be generalized to the non-respondent
population.
An e-mail with the link to the short version of the survey was sent to the
customers who did not respond to Rapidão Cometa’s initial invitation to participate in
the survey. Seventy-five responses were collected. Again, the Two Group Hotelling t-
squared test was used to compare the vector means between the respondent and non-
respondent groups (Table 11). The null hypothesis that the vectors of means are equal
for the two groups could not be rejected. Therefore, the absence of non-response bias
was inferred.
Table 11. Manova comparison of vector means (respondents vs. non-respondents)
Item
Mean
(Respondents)
Mean (Non-
respondents)
Extendedness 6.31 6.37
Operational Information
Exchange
4.39 4.70
Operating Controls 5.18 4.89
Sharing Benefits and Burdens 5.89 5.67
Planning 4.02 4.03
Dependence 5.56 5.86
3PL Dependence 5.57 5.60
3PL Credibility 6.15 6.17
3PL Benevolence 5.53 5.59
RMO 6.32 6.14
Customer TSI 2.59 2.90
3PL reputation 1.85 2.00
Satisfaction 5.32 5.60
2-group Hotelling’s t-squared = 12.741
F-test statistic = 0.9439, p = 0.5075
139
5.4. Structural equation modeling
Following Ganesan (1994) and a substantial group of relationship marketing
researchers (e.g., Morgan and Hunt 1994, Hewett and Bearden 2001, Knemeyer 2000,
2004), structural equation modeling (SEM) was the statistical technique employed in
this study. SEM is a powerful multivariate technique that can be used to investigate a
priori specified, theory-derived, hypothesized correlations or causal relations among
latent, unobserved variables. SEM is a largely confirmatory, rather than exploratory,
technique.
The underlying logic of SEM is as follows: A structural equation model
implies a structure of the covariance matrix of the variables that are used as
measurement items
12
for the latent variables, or constructs (hence an alternative name
for this field, "analysis of covariance structures"). Once the model's parameters have
been estimated, the resulting model-implied covariance matrix can then be compared
to an empirical or data-based covariance matrix. If the two matrices are consistent,
then the structural equation model can be considered a plausible explanation for the
hypothesized relations between the measurement items.
Overview of the SEM process. The SEM process centers around two steps:
validating the measurement model and fitting the structural model. The former is
accomplished primarily through confirmatory factor analysis, while the latter is
accomplished primarily through path analysis with latent variables.
In the first phase, the measurement phase, a confirmatory model allowing
covariances among all construct and stand-alone variables (not intended as indicators)
12
The terms “measurement items,” “items,” and “variables” are used interchangeably.
140
is tested. The objective of the measurement model is to assess how well the indicators
serve as a measurement instrument for the latent constructs (Garver and Mentzer
1999). Thus, the objective is to identify and correct measurement error, ensuring the
correct interpretation of the results of the structural model (phase 2). In the
measurement phase, the constructs are tested for reliability, convergent validity, and
discriminant validity. Theoretically meaningful respecifications in the measurement
model might be necessary in order to obtain an adequate model fit.
After a reasonable model fit is achieved in the measurement model, and it is
shown that the constructs are reliable and valid, the second phase of SEM process, the
structural phase, can be initiated. Here the hypothesized path model is tested and the
model fit and structural paths are examined.
5.4.1. Data preparation and preliminary analysis
Before starting the SEM process, it is necessary to follow a few pre-steps that
involve an overview of the quality of the data and data preparation (i.e., assessment of
unidimensionality and item cleaning).
Quality of data evaluation
Before utilizing the SEM software (EQS in this dissertation), a preliminary evaluation
of the quality of the data was conducted.
Outliers. All variables, which correspond to the measurement items, were checked for
obvious univariate outliers using box plots. No outliers were found.
Normality. The univariate distributions for each variable were checked for symmetry
through histograms. Many of the variables were shown to be skewed to the left (i.e.,
141
most observations fell on the right side of the scale). For this reason, the robust
estimation procedure in EQS, which accounts for non-normal data, was utilized. The
robust procedure corrects the maximum likelihood model ?
2
statistic and the standard
error to adjust for non-normal data. This was preferred to transforming the data, given
that a disadvantage of data transformation is that the new variable is no longer a
direct representation of the underlying construct.
Missing data. The data set was checked for the presence of missing data. There were
a few instances of missing data, which was expected given that the original
questionnaire had more than 100 questions. Although the cases and variables in
which the missing data occurred were random, it was decided to run the model with
the complete observations only. Substituting the missing data with the mean values of
the variables in question can lead to under-representation of the variance of the
population. Also, using pairwise deletion to generate variances and covariances can
lead to convergence problems and bias in the results.
Undimensionality
Before testing the model fit and the hypothesized relationships in the
structural phase, the set of variables (i.e., measurement items) for each of the
constructs in the model need to be tested for unidimensionality, reliability and
validity (Garver and Menzer 1999). Once unidimensionality has been established,
construct validity and reliability can be investigated.
Unidimensionality is “the degree to which items represent one and only one
underlying latent variable” (Garver and Mentzer 1999). Unidimensionality was
assessed with the aid of exploratory factor analysis. Reliability, convergent and
142
discriminant validity were assessed in the measurement phase of the structural
equation modeling process.
As most constructs in this dissertation were adapted from prior research, it
was expected that the measurement items would have high reliability and validity.
However, many of the constructs were originally used in fields other than logistics
(e.g. marketing and strategy). Therefore, it was still necessary to closely examine the
items comprising the constructs.
Initially, exploratory factor analysis (principal component analysis with
Varimax rotation,) was conducted for each construct to assess unidimensionality.
Varimax rotation maximizes the variance of squared loadings in the columns of the
structure matrix. Therefore it provides a simpler and clearer structure of loadings.
Those items that loaded weakly (e.g. less than 0.4) were removed from the scale,
while still ensuring content validity of the construct. Of the 88 items comprising 18
constructs, 79 loaded highly on their factors while 7 items were removed from the
scales due to low loadings.
Partial disaggregation
SEM might encounter convergence problems in models in which constructs
have many indicators (Knemeyer 2000). In models with factors with many items,
employing the traditional structural equation approach “can be unwieldy because of
likely high levels of random error in typical items and the many parameters that must
be estimated” (Bagozzi and Heartherton 1994, pp. 42-43). This can be especially true
in the case of the present model which is fairly complex, with many of the constructs
having six, seven, or more items.
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In order to correct for this potential problem, partial disaggregation was
adopted in this dissertation. Partial disaggregation is operationally accomplished by
randomly assigning items of a construct into composites. These composites then
become the new measurement items. The process is conducted in a way that each
factor has no more that 3 combined indicators instead of many indicators. The
rationale of partial disaggregation is that all items related to a factor should
correspond in the same way to that latent factor; therefore any combination of these
items should yield the same model fit (Dabholkar et al 1996). The advantage of
partial disaggregation is that the multivariate aspect of the model tested is maintained
while the model is simplified and the levels of random error are reduced. In this
study, the items were randomly allocated to composites for the constructs that had
more than four items after the initial item cleaning.
5.4.2. Measurement phase
The objective of the measurement phase is to isolate model misspecification
and to verify that the measures adopted appropriately represent the latent constructs in
the model. Syntax was written for the confirmatory model allowing covariances
among all constructs and stand-alone variables (not intended as indicators). By
allowing all factors to co-vary, the structural portion became just identified (thus with
a perfect fit), and the measurement part of the model could be assessed.
The robust fit indices obtained for the measurement model were: ?2 =
1883.013 (df = 1279), CFI = .878, RMSEA = .045 and SRMR = .070. The ?2 statistic
was statistically significant. It is noted that RMSEA and SRMR indices presented
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good fits. The CFI index, however, was marginally significant (threshold is .90). The
fit indices indicate that the data covariance matrix has a relatively good fit.
Convergent validity
Convergent validity refers to the extent that the items of the factor capture the
content of the construct. Two standard means of assessing convergent validity are: 1)
by examining whether the factor loadings of the measurement equations (that explain
all variables as a function of the factor) are positive and statistically significant, and;
2) by calculating the “variance extracted” by the construct, which corresponds to the
mean squared standardized loading. Ideally it should exceed .50 (Garver and Mentzer
1999).
Convergent validity was checked by both methods. By examining the
software output, it was noted that all loadings were positive and statistically
significant. It was thus inferred that convergent validity exists. In addition, the
“variance extracted” was calculated for all constructs (see Table 12). Out of 18
constructs, four fell significantly below the desired 0.50 threshold. Another four also
fell below the threshold but were very close to 0.5. Given that the PCA results
showed that these items did load on a single factor, it was decided not to eliminate or
rearrange the items used for these four constructs with low convergent validity.
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Table 12 Variance extracted of the constructs
Construct
Variance
extracted
Logistics capabilities 0.685
Volatility product market 0.401
Diversity product market 0.582
Volatility 3PL market 0.342
Diversity 3PL market 0.651
Logistics complexity 0.498
Customer TSI 0.643
3PL TSI 0.451
3PL reputation 0.371
Experience with 3PL 1.000
Satisfaction 0.804
Customer dependence 0.542
3PL dependence 0.463
3PL credibility 0.737
3PL benevolence 0.721
Experience partnering 1.000
RMO 0.469
Partnering behavior 0.391
* The variance extracted for “Experience with 3PL”
and “Experience partnering” are 1 given that they
were measured by a single indicator.
Discriminant validity
Another test conducted in the measurement phase consisted of examining the
discriminant validity of the constructs; i.e., verifying that the items loaded on the
construct of interest and not on other constructs. According to several authors (Shook
et al 2005, Kline 2005, p. 182), achieving a good fit for the model in which each
indicator loads on only one factor provides a precise test of discriminant validity.
The measurement model presented reasonable fit indices; thus it was inferred
that discriminant validity existed. In addition, the factor covariances were fairly small
in the vast majority of cases and non-significant in many cases as well. This fact also
146
diminished concerns that factors assumed as independent were in reality a single
factor (i.e., not discriminant).
Shook et al (2005) indicate that an alternative method for testing for
discriminant validity is to calculate the shared variance between constructs and verify
that it is lower than the average variance extracted for each individual construct. This
procedure was conducted for all pairs of constructs. All but three pairs (TSI –
3PLTSI, TSI – DEP, 3PLTSI – DEP) did pass this test. Therefore, for these three
pairs, a fit comparison of nested models was conducted. Models with correlations
between the two factors set equal to 1 (i.e., where the two factors are considered a
single, unique factor) were compared to models where the two factors were free to
correlate. Given that the difference in ?
2
was statistically significant for all three pairs
(see Table 13), the existence of discriminant validity was inferred.
Table 13. Test for discriminant validity for construct pairs with high covariance
Single factor
model
Two factor
model
Fit difference
Construct pairs
? ?? ?
2
df ? ?? ?
2
df ? ?? ?? ?? ?
2
? ?? ?df
Stat. sig
at 5%
level?
Discrim.
Validity
?
TSI - 3PL TSI 62.765 14 51.178 13 11.587 1 Yes Yes
TSI - DEP 143.134 20 63.352 19 79.782 1 Yes Yes
3PL TSI - DEP 61.199 14 16.372 13 44.827 1 Yes Yes
Scale reliability
Scale reliability refers to the internal consistency of a particular scale to
measure a latent variable (Garver and Mentzer 1999); i.e., indicates whether a factor
is expected to be stable and replicable. Garver and Mentzer (1999) point out that the
coefficient alpha, the traditionally adopted measure of reliability, has some
147
limitations. In some cases, it tends to underestimate the scale reliability or become
inflated when the construct has a larger number of items. They suggest the use of
SEM reliability measures, such as the variance extraction measure and the SEM
“Reliability of the Construct” measure. Following their recommendations, SEM
measures of reliability were taken into consideration.
The “variance extracted” was calculated for all constructs (Table 12 above)
and most constructs had values above the recommended figure of 0.5. In addition, the
coefficient “Maximal Reliability”, Coefficient H developed by Hancock and Mueller
(2001), a measure of construct reliability, was calculated. Hancock and Mueller
(2001) argue that the traditional “Reliability of the Construct,” RC, developed by
Fornell and Larcker (1981) has some limitations: 1) its value is affected by loading
signs; 2) it is decreased by additional indicators if those have small loadings; 3) it can
be smaller than the reliability (squared loading) of the best indicator. Table 14, below,
presents the coefficient H for each construct. All values were found to be above the
0.7 threshold.
Given that the measurement model has been assessed in terms of fit and
convergent and discriminant validity, the next step was to test the structural model
where the theoretical links are investigated.
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Table 14. Construct reliability results
Construct
Construct
Reliability
(H)
LCAP 0.921
VOLPM 0.816
DIVPM 0.737
VOL3PL 0.515
DIV3PL 0.830
LCOMP 0.797
TSI 0.892
3PLTSI 0.730
REP 0.781
SAT 0.938
DEP 0.876
3PLDEP 0.875
CRED 0.919
BENEV 0.948
RMO 0.848
PART 0.801
5.4.3. Structural phase
In the second phase, a new EQS program was written for the confirmatory
model. All independent constructs were allowed to correlate. The disturbances of the
construct pairs credibility/benevolence and dependence/3PL dependence were
allowed to correlate as well.
The following steps were followed:
Check of goodness-of-fit information. There are a dozen fit indices that are used to
assess the fit of structural equation models. Because there are so many options,
different articles report different indices and reviewers may request different fit
indices that they know or prefer (Kline 2005). Kline (2005) recommends the
149
following set of indices that reflect “the current state of practice and
recommendations about what to report in written summary of the analysis” (p. 134):
1) The model chi-square (?
2
): The chi-square statistic compares the observed and
the model-implied covariance matrices. Since the objective is that these two
matrices are similar, a non-significant chi-square is desired. However, it is a
very powerful test that can detect small discrepancies in the data. Therefore, it
is likely that the this statistic will be significant; i.e., will predict that the
model does not fit the data;
2) The Steiger-Lind root mean square error of approximation (RMSEA):
RMSEA is a fit index that evaluates the overall discrepancy between observed
and model implied (co)variances, while taking into account the model’s
simplicity. It improves as more parameters are added to the model, as long as
those parameters are making a useful contribution. It is a “badness-of-fit”
index, which means that a value of zero indicates the best fit. Values less than
0.06 are considered acceptable;
3) The Bentler comparative fit index (CFI): CFI is a data-model fit index that
evaluates the improvement in the model’s fit relative to a baseline model,
usually the independence model (also called null model). The independence
model is the worst possible model, in which there are no relationships in the
data (i.e., population covariances among observed variables are zero). A rule
of thumb is that CFI values greater than roughly 0.90 are considered
acceptable (Kline 2005);
150
4) The standardized root mean squared residual (SRMR): SRMR is a measure of
the mean absolute squared residual; i.e., the overall difference between
observed and predicted correlations. Values of SRMR less than 0.10 are
considered acceptable (Kline 2005).
This study follows Kline’s (2005) recommendations and uses these four
indices (?
2
, SRMR, RMSEA, and CFI) to assess the model fit. As expected, the ?
2
was statistically significant, which indicates that the model does not have a good fit.
This does not undermine the fit evaluation. As with the measurement model, the
values for RMSEA and SRMR fit indices fell within the desired range (see Table 15).
The CFI index, however, was marginally below the 0.90 threshold. The model fit was
considered to be marginally acceptable.
Table 15. Summary of fit indices for the full model
Chi-square CFI RMSEA SRMR
Desirable range > 0.9 < 0.06 < 0.10
Full model 1994.388 (df = 1328) 0.865 0.047 0.075
Check of inter-factor path coefficients. With the model presenting a marginal level
of acceptance, the structural paths were examined for theoretical and practical
implications. Table 16 provides an overview of the standardized solution of the
structural model. The first part of the table presents the primary antecedents of
customer partnering behavior, followed by the antecedents of dependence and
antecedents of trust.
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Table 16. Standardized path coefficients.
Hypothesis Relation Full
model
Primary Hypotheses
1 Customer dependence ? Partnering 0.374*
2 3PL dependence ? Partnering 0.095
3 3PL credibility ? Partnering 0.027
4 3PL benevolence ? Partnering 0.245*
5 Partnering experience ? Partnering 0.206*
6 RMO ? Partnering 0.216*
7 Satisfaction ? Partnering 0.06
Antecedents of dependence
8 Customer capabilities ? Dependence -0.140*
9 Environmental diversity 3PL ? Dependence -0.043
10 Environmental volatility 3PL ? Dependence -0.056
11 Environmental diversity product market. ? Dependence 0.056
12 Environmental volatility in product market ? Dependence 0.025
13 Logistics Complexity ? Dependence -0.041
14 TSI by customer ? Dependence 0.234
15 TSI by customer ? 3PL dependence -0.639*
16 3PL TSI ? Dependence 0.668*
17 3PL TSI ? 3PL dependence 1.114*
Antecedents of trust
18 3PL TSI ? Credibility 0.217*
19 3PL TSI ? Benevolence 0.454*
20 Reputation ? Credibility 0.273*
21 Experience with 3PL ? Credibility -0.049
22 Experience with 3PL ? Benevolence 0.028
23 Satisfaction ? Credibility 0.271*
24 Satisfaction ? Benevolence 0.248*
Obs.: The figures indicated by * are significant at the 5% level.
5.5. Results
In this subsection, the model results are discussed in light of the hypotheses
proposed. Figure 13, below, presents a diagram with the statistically significant paths
and Table 17 presents the model results and support for the hypotheses. A more
detailed discussion of the implications of the results is found in Chapter 6.
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Figure 13. Statistically significant path coefficients.
Customer
partnering behavior
Customer
partnering behavior
Perception of 3PL’s
dependence on customer
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation
Env. diversity
3PL market
Env .volatility
3PL market
Customer’s
experience with 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
TSI by customer
Satisfaction with
previous outcomes
Customer
capabilities
Logistics
complexity
Env. diversity
product market
Env. volatility
product market
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
Customer
capabilities
Logistics
complexity
Logistics
complexity
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
0.374*
0.216*
0.206*
0.245*
- 0.140*
- 0.639*
0.668*
1.114*
0.217*
0.454*
0.271*
0.248*
R
2
= 0.517
R
2
= 0.385
R
2
= 0.352
R
2
= 0.467
R
2
= 719
0.273*
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Table 17. Summary of Results
Hypothesis Relation Full model
Support/
Nonsupport
Primary Hypotheses
1 Customer dependence ? Partnering positive, significant supported
2 3PL dependence ? Partnering nonsignificant not supported
3 3PL credibility ? Partnering nonsignificant not supported
4 3PL benevolence ? Partnering positive, significant supported
5 Partnering experience ? Partnering positive, significant supported
6 RMO ? Partnering positive, significant supported
7 Satisfaction ? Partnering nonsignificant not supported
Antecedents of dependence
8 Customer capabilities ? Dependence
negative,
significant
supported
9
Environmental diversity 3PL ?
Dependence
nonsignificant not supported
10
Environmental volatility 3PL ?
Dependence
nonsignificant not supported
11
Environmental diversity product
market. ? Dependence
nonsignificant not supported
12
Environmental volatility in product
market ? Dependence
nonsignificant not supported
13 Logistics Complexity ? Dependence nonsignificant not supported
14 TSI by customer ? Dependence nonsignificant not supported
15 TSI by customer ? 3PL dependence
negative,
significant
supported
16 3PL TSI ? Dependence positive, significant not supported
17 3PL TSI ? 3PL dependence positive, significant supported
Antecedents of trust
18 3PL TSI ? Credibility positive, significant supported
19 3PL TSI ? Benevolence positive, significant supported
20 Reputation ? Credibility positive, significant supported
21 Experience with 3PL ? Credibility nonsignificant not supported
22 Experience with 3PL ? Benevolence nonsignificant not supported
23 Satisfaction ? Credibility positive, significant supported
24 Satisfaction ? Benevolence positive, significant supported
Antecedents of customer partnering behavior. The primary hypotheses
proposed that, with the exception of perceived 3PL dependence, all primary
antecedents (i.e., customer dependence on a 3PL, 3PL credibility, 3PL benevolence,
154
customer experience with partnering, customer relationship marketing orientation,
and satisfaction with previous outcomes) have a positive effect on customer
partnering behavior. In other words, it was proposed that a customer with higher
levels of dependence on a 3PL, trust in a 3PL’s credibility and benevolence,
satisfaction with a 3PL, relationship marketing orientation, and satisfaction with the
relationship, will exhibit higher levels of partnering behavior.
Examining the signs and statistical significance of the structural paths linking
these constructs provides information on whether the hypotheses are supported. It was
found that there is a statistically significant positive relationship between a
customer’s dependence on a 3PL (H1), a customer’s trust in a 3PL’s benevolence
(H4), a customer’s experience with partnering (H5), a customer’s relationship
marketing orientation (H6), and a customer’s partnering behavior. Hypotheses H1,
H4, H5, and H6 were supported.
The paths linking a 3PL’s dependence (H2), a 3PL’s credibility (H3) and
satisfaction with previous outcomes (H7), to a customer’s partnership behavior were
not statistically significant. Therefore, these hypotheses were not supported.
These findings indicate that both interorganizational conditions (i.e., customer
dependence and customer’s trust in a 3PL’s benevolence) and firm specific factors
(i.e., customer partnering experience and a customer’s relationship marketing
orientation) play a role in shaping a customer’s perceived partnering behavior with a
3PL. It can also be observed from the magnitude of the standardized path coefficients
that interorganizational factors, especially dependence, have a stronger influence than
firm specific factors (i.e, customer’s partnering experience and relationship marketing
155
orientation). The surprising finding that 3PL credibility had no significant influence
on customer partnering behavior might indicate that the interpersonal relationships
between a 3PL representative and his/her customer cannot be underestimated and is
crucial in shaping a customer’s trust. These four constructs explained almost 52% of
partnering behavior’s variance.
Antecedents of customer dependence. The hypothesized antecedents of
customer dependence are related to a customer’s internal logistics capabilities, its
competitive and operational environment, a 3PL’s competitive environment, and
transaction specific investments (TSI) by both customers and the 3PL. It has been
proposed that a customer with higher levels of internal logistics capabilities (H8)
perceives itself to be less dependent on a 3PL. It was also hypothesized that a
customer will perceive itself to be less dependent on a 3PL if the 3PL is immersed in
a diverse environment (H9) and invests in their relationship (H16).
It was also proposed that a customer’s dependence on a 3PL increases if a
firm is immersed in a diverse (H11) and volatile market (H12), with complex logistics
operations (H13), and when the customer invests in its relationship with the 3PL
(H14). Volatility in the 3PL market (H10) was also hypothesized to have a positive
relationship with customer dependence.
Surprisingly, for the sample under study, competitive pressures, operations
complexity or lack of alternatives (i.e., H9, H10, H11, H12, and H13) and TSI by
customer (H14) do not have a statistically significant effect on a customer’s perceived
dependence on a 3PL. Only a customer’s logistics capabilities (H8) and TSI by 3PL
(H16) had statistically significant results.
156
It was found that there is a negative relationship between a customer’s
logistics capabilities and a customer’s dependence on a 3PL (H8 supported). This
means that when a customer has a greater understanding of the management of its
logistics operations, a customer will perceive itself to be less dependent on a 3PL. A
(strong) positive effect was found between a TSI done by a 3PL and a customer
perceived dependence on a 3PL, which is the opposite effect that was hypothesized
(H16 not supported). This finding means that when a 3PL invests in a relationship
with a customer, this customer perceives itself to be more dependent on the 3PL. This
point is very important. The traditional resource dependence rationale is based on an
adversarial point of view – dependence asymmetry. If a firm perceives its partner to
be dependent, a firm’s level of dependence is reduced. The unexpected findings might
suggest that a customer that perceived the 3PL to be investing in their relationship
perceives itself to be more dependent on the partner. This might suggest that they are
more loyal to trade partners that invest in a relationship, or may be an indication that
they perceive that no other 3PLs would be willing to make such investment on their
behalf.
Antecedents of 3PL dependence. Two hypotheses were presented for the
antecedents of 3PL dependence. First, it was hypothesized that a customer will
perceive the 3PL to be more dependent on it when the 3PL invests in the relationship
(H17). Second, it was hypothesized that a customer will perceive the 3PL to be less
dependent on it when the customer invests in the relationship (H15). Both hypotheses
were found to be statistically significant and in the expected direction. It was found
that there is a negative relationship between TSI by a customer and 3PL dependence
157
(H15 supported) and a positive relationship between 3PL TSI and 3PL dependence
(H17 supported).
Antecedents of trust. TSI by the 3PL have a significantly positive impact on
both credibility and benevolence (H18 and H19 supported). Reputation has a
significantly positive impact on credibility (H20 supported). There was no
statistically significant link found between experience with 3PL and credibility and
benevolence (H21 and H22). Finally, satisfaction significantly impacts both
credibility and benevolence (H23 and H24 supported). Therefore, given that the direct
link between satisfaction and customer partnering behavior satisfaction (H7) was not
found, this implies that satisfaction does not lead directly to customer’s partnering
behavior, but indirectly through the building of trust.
Contrasting the results with Ganesan’s model of long term orientation.
Eventhough Ganesan’s (1994) model has a different dependent variable (i.e., retailer
long term orientation in its relationship with a vendor) than the one adopted in this
dissertation, it is useful to identify which results were consistent (or inconsistent) to
Ganesan’s findings. Regarding the antecedents of the long-term orientation, the
present model was consistent to Ganesan’s solely regarding the customer’s
dependence (equivalent to retailer’s dependence on Ganesan’s model). Satisfaction,
credibility, and 3PL dependence, which were significant in Ganesan’s model, were
not found to be significant in the present model.
Regarding the antecedents of dependence, only the effects of transaction
specific investments by the customer and 3PL on customer dependence were
consistent to Ganesan’s findings. There is an interesting point to highlight here. In
158
both models, it was hypothesized that transaction specific investments by the vendor
(or 3PL) would have a negative effect on a firm’s dependence. In both models, the
results were the opposite as expected (i.e., transaction specific investments by a
vendor have a positive effect on a firm’s dependence) and significant.
Regarding the antecedents of trust, both models presented very similar findings.
It was found a positive effect of reputation on credibility in both models. Also in both
models, prior experience with the vendor (or 3PL) was not found to have a significant
effect on credibility and benevolence. Transaction specific investments by a vendor
(or 3PL) had a positive effect on both credibility and benevolence. However,
satisfaction was found to directly impact credibility and benevolence in the present
model, but not in Ganesan’s model. In his case, satisfaction directly impacted the
dependent variable. In the model of this dissertation, satisfaction impacts the
dependent variable mediated by trust.
As a conclusion, it can be said that Ganesan’s contentions were generally
validated although the dependent variable of this dissertation (partnering behavior) is
a broader description of relational behavior, as opposed to Ganesan’s long term
orientation, which is a single dimension of relational behavior. The model presented
in this dissertation contributes to the previous model by providing evidence that other
firm specific characteristics, such as prior experience with partnering, relationship
marketing orientation, and capabilities, do impact relational behavior as well.
5.6. Conclusions
This chapter presented the procedures followed in order to analyze the data
and the results from the formal tests of the hypotheses. The process started with
159
testing for non-response bias and a preliminary analysis of the data. The steps
following included the analysis of the measurement model, including construct
reliability, discriminant validity, and convergent validity. Finally the structural model
was analyzed and the results presented. The next chapter presents the conclusions and
discussion of the results, along with an overview of the contributions of the study,
limitations, and directions for future research.
160
Chapter 6: Discussion and Concluding Remarks
This chapter comprises four main topics. First, an overall discussion of the
model results is presented. Second, the contributions of this dissertation to the
academic literature and managerial implications are examined. Third, the limitations
of this study are addressed. Next, the directions for future research are outlined.
Concluding remarks finalize the chapter.
6.1. Discussion of model results
The objective of this dissertation was to develop a model of the determinants
of customer partnering behavior in logistics outsourcing relationships. Customer
partnering behavior in the relationship with a 3PL is defined as the customer’s
perception that this relationship presents five key behavioral elements (Gardner et al
1994): planning, sharing benefits and burdens of the relationship, systematic
operational information exchange, and mutual operating controls. Developing close
relationships between 3PLs and customers has been shown to bring them many
benefits, such as: 1) increased customer’s performance (Knemeyer and Murphy 2004)
and market share (Stank et al 2003), and 2) greater levels of customer retention,
service recovery, and referrals to new customers (Knemeyer and Murphy 2005).
The hypotheses that compose the model were developed based on theories and
empirical evidence in the marketing, logistics, and strategy literatures. The model was
tested following established statistical procedures and Figure 14 depicts the simplified
model comprising solely the statistically significant structural paths. Overall, the
model findings support the contention that interorganizational conditions created
161
through the relationship interactions (i.e. trust, dependence, and satisfaction)
combined with firm specific factors (i.e. experience with partnering and customer
relationship marketing orientation) influence a customer partnering behavior with a
3PL. The interorganizational conditions are influenced also by both firms specific
characteristics (e.g., customer’s logistics capabilities, 3PL reputation), and both firms
actions towards the relationship (i.e., transaction specific investments). In the
paragraphs that follow, the results obtained from the data analysis are discussed in
detail.
Figure 14. A simplified model of customer partnering behavior in logistics outsourcing
relationships
13
Antecedents of customer partnering behavior. The antecedents of customer
partnering behavior in its relationship with a 3PL are related to interorganizational
13
The shaded constructs represent the validated extensions to Ganesan’s (1994) model of the
antecedents long term orientation in buyer-seller relationships.
Customer
partnering behavior
Perception of 3PL’s
dependence on customer
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Prior experience
3PL partnering
Relationship Marketing
Orientation
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
0.374*
0.216*
0.206*
0.245*
- 0.140*
- 0.639*
0.668*
1.114*
0.217*
0.454*
0.271* 0.248*
R
2
= 0.517
R
2
= 0.385
R
2
= 0.352
R
2
= 0.467
R
2
= 719
0.273*
162
conditions and customer specific characteristics. The model identified four main
antecedents of customer partnering behavior:
- The perception of a customer’s dependence on a 3PL (H1);
- A customer’s trust in a 3PL’s benevolence (H4);
- A customer’s prior partnering experience with other 3PLs (H5), and;
- A customer’s relationship marketing orientation (H6).
The model provided support for the contention that higher levels of customer
dependence lead to higher levels of customer partnering behavior (H1). It was also
found that the perception that a 3PL depends on the customer does not influence a
customer partnering behavior (H2 not supported). In other words, a customer will be
willing to exchange information, engage in joint planning, and share benefits and
burdens of the relationship, when it perceives itself to be dependent on the 3PL’s
expertise in providing logistics services. This occurs regardless of whether the
customer perceives itself to be a major customer of the 3PL (i.e., when the customer
perceives the 3PL to be dependent on its business relationship).
The model also supported the hypothesis that a customer’s trust in a 3PL’s
benevolence positively affects a customer’s partnering behavior (H4). This means
that when a customer perceives the 3PL to care for the relationship and to be willing
to make sacrifices for the relationship, a customer will be more likely to exhibit a
partnering behavior with a 3PL. Indeed, during semi-structured interviews conducted
in December of 2006 with customers of Rapidão Cometa, it was evident that
customers very much appreciated the weekly visits conducted by Rapidão Cometa’s
representatives and the personal and close relationship developed between them. In
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the event of operational problems and difficulties, all interviewed customers agreed
that Rapidão Cometa’s representatives were very active in assisting them.
Surprisingly, support was not found for the contention that a customer’s
perception of a 3PL’s credibility, i.e., reliability and consistency of behavior,
positively impacts a customer’s partnering behavior (H3). This result implies that the
belief in a 3PL ability to efficiently perform does not directly impact the customer’s
partnering behavior. To some extent, however, this dimension is captured by
customer satisfaction (discussed later in the antecedents of trust).
The model found strong support for the contention that customer specific
characteristics play an important role in shaping a customer’s partnering behavior
with a 3PL. It was found that a customer’s prior experience with other 3PLs (H5) and
a customer’s relationship marketing orientation (H6) positively affect a customer’s
partnering behavior. The first result indicates that firms that are more experienced in
partnering with a logistics provider organization may be better at implementing and
maintaining close and interactive relationships. In addition, the strategy a firm
embraces with regards to its own customers will influence the nature of the
relationship with the 3PL. Therefore, it is crucial that 3PLs investigate the history and
relationship practices of potential customers before incurring investment costs to
build relationships.
Antecedents of dependence. This sub-model presented the most surprising
results. Out of the eight hypothesized antecedents of customer dependence on a 3PL,
only two paths were statistically significant: the perception of a customer’s internal
logistics capabilities (H8) and the transaction-specific investments (TSI) performed
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by the 3PL (H16). The model found a negative relationship between a customer’s
logistics capabilities and the perceived dependence on a 3PL, providing support for
H8. The result supported the contention that when a firm perceives itself to be
knowledgeable about its logistics processes, it may believe itself to be less dependent
on a 3PL. An unexpected finding was related to the effect of TSI by 3PL on customer
dependence. It was hypothesized that when a customer perceives that a 3PL has
invested in their relationship, the customer would believe itself to be less dependent
on a 3PL. The rationale was that the 3PL would have incurred relationship costs, thus
creating exit barriers for the 3PL. Interestingly, the result was the opposite. It was
found that a customer that believes a 3PL has invested in a relationship feels that it is
more dependent on the 3PL. This might indicate that the customer has become more
loyal to the 3PL, or that the customer perceives that it would have difficulties finding
another 3PL that would make the same investments. During the semi-structured
interviews conducted with customers of Rapidão Cometa, anecdotal evidence was
found for this contention. One motorcycle manufacturer indicated that Rapidão
Cometa built and attached racks in their trucks to load motorcycles. Due to Rapidão
Cometa’s initiative and willingness to assume the costs of the racks, the manufacturer
reduced costs by not requiring heavy and expensive packaging. Another example is
related to Rapidão Cometa’s investments in its relationship with a large cosmetics
company. Rapidão Cometa assumed the costs for the “kits assembly” (packaging)
equipment that was installed in the manufacturer’s distribution center.
Interestingly, none of the factors related to environmental pressures in the
product market or in the market for 3PL services (H9, H10, H11, and H12) had an
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impact on the perceived customer dependence. As well, the complexities of logistics
operations showed no influence on customer dependence (H13).
Both hypothesized antecedents of perceived 3PL dependence on a customer
were supported. It was found that TSI by customer had a negative impact on
perceived 3PL dependence (H15) and that TSI by the 3PL had a positive effect on the
perceived 3PL dependence on a customer (H17). These results have limited interest in
the context of the overall model, since 3PL dependence had no significant effect on
customer partnering behavior (see main antecedents of partnering, above).
Antecedents of trust. With the exception of customer experience with 3PLs,
all proposed antecedents of both dimensions of trust, i.e., credibility and benevolence,
were supported. Transaction-specific investments (TSI) by the 3PL positively impact
a customer’s perception of a 3PL’s credibility and benevolence. The crucial
importance of 3PL investments are noted in that they ultimately influence a
customer’s partnering behavior. Not only does a customer perceive itself to be more
dependent on the 3PL, but it also believes the 3PL to be efficient and to care for the
relationship. As noted in the antecedents of dependence subsection above, customers
greatly appreciate Rapidão Cometa’s investments in their relationships. These
investments constitute, therefore, tangible demonstrations of benevolence. A 3PL’s
reputation in the market was also found to have a positive effect on a 3PL’s
credibility. Therefore it is crucial for 3PLs not only to invest in advertising, but,
especially, to build strong reputations through excellence of service. Reputation may
be disseminated via word-of-mouth communication. Satisfaction with previous
outcomes of the relationship also had a positive effect on credibility (H23) and
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benevolence (H24). Given that the direct link between satisfaction and customer
partnering behavior was not found to be statistically significant (H7), the results of
the model indicates that the effect of satisfaction on partnering behavior is indirect
through the building of trust.
6.2. Contributions
This research provides contributions to both academics and practitioners. As
the following paragraphs describe, contributions have been made to the logistics and
marketing fields, as well as to managers.
Contributions to the logistics literature: The main contribution of this
dissertation to the logistics literature is the development and testing of a theory-based
model. Most logistics outsourcing literature has been exploratory in nature and there
have been few examples of theory testing (Maloni and Carter 2005). This dissertation
provides and tests a theoretical framework of the conditions under which partnerships
between 3PLs and customers will more likely occur. A second contribution to the
logistics literature related to the integrative nature of the model that combines theories
and findings from other disciplines, such as marketing and strategy. More
specifically, rationales borrowed from network theory, the capabilities perspective,
and the strategic orientation perspective were combined with social exchange theory
in the model.
Another contribution of this dissertation to the logistics outsourcing literature
is its focus on the antecedents of partnering behavior. As noted in the literature
review, the few examples of theory testing in the logistics outsourcing literature have
focused on other aspects of these relationships, e.g., the positive effects of logistics
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outsourcing relationships on customer and 3PL performance (e.g. Sinkovics and
Roath 2004, Panayides and So 2005, Knemeyer and Murphy 2005).
Aside from identifying the antecedents of customer partnering behavior, an
important contribution of the model is to provide an understanding of how the
interplay among various factors occurs, leading to a customer’s partnering behavior
with its 3PL. The factors that composed the model were related to environmental
forces, interorganizational conditions and firm-specific factors. Since it has been
shown that these factors contribute positively to performance, understanding the
mechanisms through which these close relationships occur is very relevant.
Collecting data from Brazilian 3PL customers is a final contribution. The
majority of studies in the logistics outsourcing literature have focused on U.S. firms.
Other studies have focused on surveys and case studies in countries such as New
Zealand, Saudi Arabia, China, and Mexico. However, to the best of this author’s
knowledge, no study has used Brazilian data. Given the importance of the Brazilian
market to world trade, understanding the dynamics of logistics outsourcing
partnerships in that market is relevant. Moreover, since many of the constructs tested
here were first developed and used with U.S. data, future cross-cultural comparisons
can be undertaken.
Contributions to the marketing literature: An important contribution of this
dissertation to the marketing literature is extending the seminal marketing study
developed by Ganesan (1994). Although written more than ten years ago, this study
continues to be cited by marketing researchers. His model identified the antecedents
of long-term orientation in buyer-seller relationships and was tested with retailers and
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their vendors. The constructs adopted by Ganesan (1994) focused primarily on
interorganizational conditions (e.g. trust in the partner, dependence on the partner)
and elements of environmental conditions (e.g., environmental uncertainty). The
present model contributes to the extension of Ganesan’s model by combining firm-
specific characteristics with interorganizational factors in the explanation of a firm
partnering behavior. The results of the model indicate that Ganesan’s rationale also
holds in the case of partnering behavior in logistics outsourcing relationships, but
provides statistical validation that firm-specific factors also play an important role in
shaping partnering behavior as well.
Another contribution to the marketing and partnering literatures relates to the
multidimensional nature of the dependent variable: customer partnering behavior.
Ganesan’s (1994) study focuses on long-term orientation, which is one dimension of
partnering. To the best of its author’s knowledge, there has been no study in which
partnering behavior itself is the dependent variable. A final contribution to the
marketing literature is testing the model in an industry that is not commonly
investigated by marketing researchers, the logistics outsourcing industry.
Contributions to managers: This study brings relevant contributions to 3PL
managers. It has been consistently shown in the logistics literature that developing
and nurturing close relationships between 3PLs and customer firms results in benefits
for 3PLs and customers (e.g., higher performance, higher levels of customer retention
and referrals, increased market share, etc). It is thus in the 3PL’s interest to identify
the factors that are important or effective in stimulating their customers to engage in
close relationships; i.e., to exhibit partnering behavior. The present model identified
169
these factors and their relative effects on shaping customer partnering behavior.
Identifying the factors that have a strong influence on customer partnering behavior
provides guidance to 3PLs on how to best nurture partnerships with their customers.
This could assist 3PLs in maintaining and expanding their customer base.
6.3. Managerial implications
This research has identified several major factors that influence a customer’s
partnering behavior in its relationship with a 3PL. Based on the results of the
research, several recommendations can be made to 3PL managers:
Increasing a customer dependence on a 3PL. A 3PL can increase the depth
of its partnerships with its customers by increasing customer dependence on its
services. It was shown that when a customer perceives to be dependent on a 3PL, a
customer exhibits higher levels of partnering behavior (H1). The level of a customer
dependence on a 3PL will be a function of two main factors: a customer’s logistics
capabilities and the degree to which a 3PL invests in the relationship. The results of
the model indicated that there is a negative relationship between a customer’s internal
logistics capabilities and customer dependence on a 3PL. The key idea is that a 3PL
should carefully protect its core competencies. If a customer perceives to fully
understand how to perform those activities outsourced to the 3PL, it will perceive to
be less dependent and exhibit a partnering behavior to a less degree. This increases
the likelihood that a customer will quit the relationship for an alternative 3PL and
contract less of the focal 3PL services.
Secondly, as counterintuitive as it may sound, the results from this dissertation
suggest that a 3PL should invest in a relationship (H16) in order to increase customer
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dependence. This might be either because the investments increase customer loyalty,
or because the customer perceives that no other 3PL may be willing to invest in the
relationship. A 3PL that invests in a relationship may feel appreciated by its
customer. These investments do not necessarily need to be in physical assets. They
can be related to training of transactional activities related to the operations between
3PL and customer, or processes developed exclusively for that particular relationship.
It is relevant to note that there was no evidence found that competitive pressures in
the customer industry, or that complexity of customer operations, or availability of
other 3PLs, impacts a customer’s perceived dependence on a 3PL. Therefore, in order
to increase a customer’s perceived dependence, a 3PL should focus on its capabilities
and investments in the customer relationship.
Increasing customer trust in a 3PL. The model results indicate that there is a
positive relationship between a customer’s perception of a 3PL’s benevolence and a
customer’s partnering behavior (H4). Therefore, it is crucial for a 3PL representative
to make an effort to develop personal and interactive relationships with its customers.
Personal relationships may be emphasized in many areas of a 3PL’s activities,
including those that deal with customer issues or complaints (e.g., the marketing
department and call-center). Several semi-structured interviews were conducted with
Rapidão Cometa’s customers. During the interviews, it was emphasized how
important the weekly visits from Rapidão Cometa’s representatives were for the
customers. The customers argued that Rapidão Cometa was responsive when
problems arose, such as late shipments. They pointed out that it was important to
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know that someone from Rapidão Cometa was paying attention to their problems and
working to solve them.
Aside from working on interpersonal interactions with customers, one way to
increase a customer’s perception of a 3PL’s benevolence is by investing in the
relationship. It was shown that there is a positive relationship between 3PL
transaction-specific investments and 3PL benevolence. Transaction specific
investments are a demonstration of concern and care for the relationship. Therefore, if
a 3PL invests in the relationship with a customer, the customer will not only perceive
itself to be dependent on the 3PL, but will also trust the 3PL.
An important means of increasing a customer’s perception of a 3PL’s
credibility is by increasing the 3PL’s reputation. A positive relationship was found
between a 3PL’s reputation and its customer’s perception of the 3PL’s credibility.
Reputational advertising may help here. In addition, reputation may spread through
word-of-mouth. One of the customers interviewed said that his company chose
Rapidão Cometa based on conversations with managers from other firms who already
worked with Rapidão Cometa.
Finally, it is crucial that a customer is satisfied with the services provided. The
model results show that satisfaction with outcomes of the relationship build trust that
in turn shapes a customer’s partnering behavior with a 3PL. During the interviews,
on-time performance, freight visibility through a satellite tracking system, and cargo
integrity (i.e., absence of damage and spoilage) were clearly the main factors that
customers used to evaluate Rapidão Cometa’s performance.
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Knowing a 3PL customer. The model results showed that a customer
relationship marketing orientation and a customer’s prior experience with partnering
had strong positive impacts on partnering behavior. These findings are particularly
important for a 3PL when deciding whether to start working a new customer. A
customer’s own marketing strategies and philosophies of relating with business
partners will influence the quality and dynamics of its relationship with the 3PL. A
3PL should try to understand how a customer relates to its own customers. If the
customer firm’s strategy focuses on nurturing mutually beneficial relationships with
its own customers, it is more likely that the customer will do the same with the 3PL.
In addition, a customer’s experience with other 3PLs will shape its
expectations in the present relationship. Therefore, it is recommended that 3PLs
investigate a customer’s prior experiences with other 3PLs. If a customer has any
prior experience partnering with other 3PLs, it may have more realistic expectations
with the current service level.
6.4. Limitations
There are key limitations of this study. The first set of limitations is related to
the nature of the model, variable measurement, and data collected. First, this study
examined customer partnering behavior in a relationship with a 3PL from the
customer’s perspective. The perception of the 3PL provider was not captured in the
data. Second, all constructs were measured by perceptual scales. Ideally objective
measures should be utilized to match the perceptual measures, especially those that
are related to operational activities (e.g., information exchange, planning, operating
controls, logistics operations). Third, the sample respondent firms are customers of a
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single 3PL. These customers might not represent the population profile of Brazilian
firms, in general.
Another concern is related to the comparability of the findings from this study
to those of other studies. The fact that the sample is composed of Brazilian firms may
make the findings difficult to compare to those from other studies, given that most of
the studies have been conducted with U.S. firms. In addition, given that the model
focused on a single industry (i.e. logistics outsourcing industry) the findings may not
be generalizabe to other industries.
The second set of limitations is related to methodological issues. Many
variables were skewed, which violates the normality assumption of structural
equation models. This research tried to overcome this problem by using robust
estimation techniques. In addition, despite concerted efforts to increase survey
responses, there were still a fairly small number of observations to test a complex
model.
6.5. Future research
Several avenues for future research can be identified:
First, the model could be enhanced by testing the effects of customer
partnering behavior on performance. Performance measures could include perceptual
measures from the customer’s perspective, or objective measures, such as on-time
performance, or sales. It would be interesting to contrast the results of models
estimated using perceptual versus objective measures.
Second, an alternative model without trust or dependence as mediating
variables could be tested. As explained in the literature review section, according to
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social exchange theory, environmental and firm-specific factors contribute to the
creation of interorganizational conditions (i.e. dependence and trust) that, in turn,
influence relational behavior (customer partnering behavior in this dissertation).
Studies that follow other theories, such as resource dependency and transaction costs
economics, link environmental and firm-specific factors directly to relational
behavior variables. Comparing alternative models would be a good extension of this
paper.
The model could be tested linking the independent variables separately to
each of the dimensions of partnering behavior. It might be the case that some
dimensions of partnering behavior, such as operating controls, might be highly
influenced by the partnering antecedents, while others might not be influenced to the
same extent. Similarly, a model where performance is the dependent variable could
be tested with separate dimensions of partnering as the independent variables. It
might be the case that some components of partnering have a greater effect on
performance than do others.
Simpler models could be tested well. For example, the effect of RMO on
partnering, moderated by environmental uncertainty could be tested. Customer
demographics may also play a role in shaping partnering behavior. For example, the
customer partnering behavior of small and large firms could be compared. As well,
different types of customers could be compared; e.g., partnership behavior could be
compared between those with few and many functions outsourced.
Finally, previous work on marketing and transaction costs economics has
shown that factors, such as contractual issues, legal issues and relationship
175
measurement issues, could also affect partnering behavior. A future model could
encompass these additional variables.
6.6. Summary and concluding remarks
Although the logistics literature has reinforced the importance of relationship
building between 3PLs and their customers, a theoretical and testable model that
identifies the factors that lead customers to exhibit partnering behavior is still lacking.
This dissertation fills this gap by identifying the factors that lead to customer
partnering behavior in a relationship with a 3PL. In addition, the interplay between
environmental forces, interorganizational conditions, and firm-specific factors in the
shaping of such behavior is described.
Interorganizational conditions of trust and dependence were found to be key
drivers of a customer’s partnering behavior, and correspond to the factors 3PLs must
focus on to improve partnering. It is relevant to note that these interorganizational
influences are stronger than other factors, such as a customer’s experience with
partnering or the strategic orientation of the customer. In order to increase levels of
dependence and trust, transaction-specific investments may be made by the 3PL. This
single element has a strong influence on both trust and dependence. An important
point is that competitive and operational environments do not seem to have a
significant influence on a customer‘s dependence.
Aside from investing in the relationship with customers, levels of trust can be
increased by building reputation for excellence and fairness and, especially, by
demonstrating concern for the relationship with customers. The interpersonal side of a
relationship should not be underestimated.
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This research shows that a customer’s experience with partnering and strategic
orientation (i.e., relationship marketing orientation) also play important roles in
explaining a customer’s partnering behavior. This means that before investing in a
relationship with a specific customer, the 3PL could closely investigate how the
potential customer behaves in its relationships with its own customers. It is important
to identify the nature of a customer’s relationship with its own customers, since this
relationship might mirror the customer-3PL partnership.
In conclusion, maintaining interorganizational relationships requires a broad
knowledge of a partner’s strategic profile and expectations. It requires creating
conditions of satisfaction, trust, and dependence on the relationship. Partnerships are
hard to build and maintain. This study may shed some light on effective actions that
3PLs can undertake in order to build strong partnerships with their customers.
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Appendices
1. E-mails
2. Survey instrument
178
Initial E-mailing cover letter
Dear ,
With the emphasis on cost reduction and service improvement, more companies have
engaged in partnerships with one or more third party logistics providers (3PLs). To gain a
better understanding of what factors trigger that decision and what are the performance
effects, Prof. Martin Dresner and Adriana Rossiter at the University of Maryland, with the
support of Rapidao Cometa, are conducting a research study that examines the roles of
dependence, trust, and strategic orientation in partnering relationships with logistics
providers, and what effects these relationships have on customer performance.
You are one of a small group of individuals selected as being particularly knowledgeable
about these types of relationships. We are asking you to provide input on your experience in
working with Rapidão Cometa. To ensure that the results of this research represent the
opinion of firms that are involved in these relationships, it is important that the web-based
survey be fully completed. The survey should take approximately 20 minutes to complete.
Your participation involves the completion of the survey at the link provided below. Sending
the questionnaire is your consent to participate in this study and the acknowledgement that
you are 18 years or over. Your responses will be completely confidential and combined with
data others provide for presentation purposes. Individual email addresses will be kept on file
for about six weeks after the launch of the survey for potential follow-up e-mails, but will be
removed from the database afterwards. Rapidao Cometa will not have access to individual
responses. As an incentive for you fully completing the survey, we offer you a summary
report of the research and the chance to win an iPod. You may withdraw from participation in
this survey at any time and your data will be removed from the study results. If you want to
withdraw from participation in this survey, please send an e-mail to Adriana Rossiter.
The overall results from this research may be very helpful to you in identifying the key
factors that lead to 3PL-customer partnering, and how that affects a customer’s performance.
If you would like to receive a summary copy of the results, please include your contact
information at the end of the survey. Some examples of questions in the survey include your
agreement with sentences such as: “The relationship with Rapidao Cometa has improved our
information technology”, or “We require shipment tracking ability.”
If you have any questions about this project, please call Adriana Rossiter at 1.301. 456.9163
or e-mail to [email protected]. Thank you very much for your support in this
important study about the 3PL industry. If you have questions about your right as a research
subject, please contact: Institutional Review Board Office, University of Maryland, College
Park, MD 20742, [email protected] or 1.301.405.4212.
Sincerely,
Prof. Martin Dresner
The R. H. Smith School of Business
University of Maryland
Adriana Rossiter
The R. H. Smith School of Business
University of Maryland
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Follow-up e-mail
Dear ,
I recently e-mailed you seeking your input on your relationship with Rapidão
Cometa. If you already completed the survey, thank you for your participation. If you
have not yet had the chance to complete the survey, could you please take a few
minutes now and complete the survey found at the link below?
The results of this study should be very helpful to you in identifying the key factors
that led you to engage in a relationship with Rapidão Cometa. If you would like to
receive a summary copy of the results, simply include your contact information at the
end of the survey.
If you have any questions, please call me at 1.301. 456.9163 or send me an email at
[email protected]. Again, thank you for your cooperation. I greatly
appreciate your time and effort in completing the survey.
Sincerely,
Adriana Rossiter
University of Maryland
180
3PL-customer partnerhips:
A study about your relationship with Rapidão Cometa
The success of this research is depends on your participation. Thank you in advance for your time and support!
We would like to thank you for participating and offer you:
- an opportunity to win a digital camera/iPod (retail value $ 150.00)
- a summary report so you can identify the drivers of your relationship with Rapidão Cometa and how it
affects your performance.
Instructions
Please read the following instructions carefully before beginning the survey:
- Your responses to the questions will be strictly confidential and accessible only to the researchers.
Rapidão Cometa will not have access to your individual responses. Your responses will be used along
with responses from other participating customers to create summary reports.
- Please answer all the questions as well as you can, even if some questions may appear similar. If you
do not know the exact answer, please provide your best estimate.
- Please refer all questions to your business unit or the unit of your company responsible for managing
logistics.
- You can suspend the completion of the survey after each page that you have submitted and
continue later using the hyperlink and password that were included in your invitation email. Your entries
are saved by clicking on the “submit” button at the end of each page. Please note that the chances to
win gifts are available only to those respondents who complete the entire survey.
- Please use the “submit” and “back” buttons within the survey. Using the “back” and “next” buttons
of your browser may result in data loss.
Please contact Adriana Rossiter for questions:
Email: [email protected]
Phone: 1.301.314.9170
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A. Relationship with Rapidão Cometa
This first set of statements describes the relationship between Rapidão Cometa
and your company. Please indicate your level of agreement.
Strongly
disagree
Neither
agree
nor
disagree
Strongly
agree
We expect our relationship with Rapidão Cometa to last a
long time ? ? ? ? ? ? ?
We are very loyal to Rapidão Cometa ? ? ? ? ? ? ?
Maintaining a long-term relationship with Rapidão
Cometa is important to us. ? ? ? ? ? ? ?
We have many direct computer to computer links with
Rapidão Cometa (i.e., EDI) ? ? ? ? ? ? ?
We use software compatible with Rapidão Cometa ? ? ? ? ? ? ?
We are linked to Rapidão Cometa through computers ? ? ? ? ? ? ?
We and Rapidão Cometa exchange information that
helps establishment of our business planning
We require shipment tracking ability ? ? ? ? ? ? ?
We require frequent fleet status reports ? ? ? ? ? ? ?
We require on-time performance reports
We are willing to help Rapidão Cometa in difficult
situations ? ? ? ? ? ? ?
We share risk with Rapidão Cometa ? ? ? ? ? ? ?
We have a high willingness to handle exceptions by
negotiation ? ? ? ? ? ? ?
Rapidão Cometa and our company have joint
committees/task forces ? ? ? ? ? ? ?
We heavily exchange technical information with Rapidão
Cometa ? ? ? ? ? ? ?
We regularly study Rapidão Cometa's operations for our
planning ? ? ? ? ? ? ?
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The next set of statements is related to how your relationship with Rapidão
Cometa has helped improve your company performance. Please indicate your
level of agreement.
Strongly
disagree Neutral
Strongly
agree
This relationship has….
improved our logistics system responsiveness ? ? ? ? ? ? ?
improved our logistics system information ? ? ? ? ? ? ?
reduced our operational risk ? ? ? ? ? ? ?
improved our product/service availability ? ? ? ? ? ? ?
allowed us to achieve logistics costs reductions ? ? ? ? ? ? ?
improved our information technology ? ? ? ? ? ? ?
enabled us to implement changes faster/better ? ? ? ? ? ? ?
provided us more specialized logistics expertise. ? ? ? ? ? ? ?
enabled us to move from a "push" to a "pull" system ? ? ? ? ? ? ?
reduced our order cycle time ? ? ? ? ? ? ?
improved our post-sale customer support ? ? ? ? ? ? ?
helped us integrate our supply chain ? ? ? ? ? ? ?
183
Are you satisfied with the services provided by Rapidão Cometa? Please
describe your opinions with respect to the outcomes with Rapidão Cometa in the
past year:
Last year…
Strongly
disagree
Neither
agree
nor
disagree
Strongly
agree
…we were pleased with the outcomes ? ? ? ? ? ? ?
… working with Rapidao was very useful ? ? ? ? ? ? ?
… Rapidao Cometa was ineffective ? ? ? ? ? ? ?
… we were dissatisfied ? ? ? ? ? ? ?
… the outcomes were outstanding ? ? ? ? ? ? ?
… the outcomes were of bad value for our company ? ? ? ? ? ? ?
… we were comfortable in working with Rapidao
Cometa
How many years has your company worked with Rapidão Cometa? ____ years. (e.g.,
2.5)
Has your company ever partnered with logistics providers? ___ Yes ___ No
If yes, how many years has your company partnered with other logistics providers (in
general, not necessarily with Rapidão Cometa)? ____ years (e.g., 2.5)
.
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The following statements describe your relationship with Rapidao Cometa’s
representative. Please indicate the level of agreement.
Strongly disagree Strongly agree
Rapidão Cometa’s representative…
… has been frank in dealing with us ? ? ? ? ? ? ?
… makes reliable promises ? ? ? ? ? ? ?
… is knowledgeable regarding his services ? ? ? ? ? ? ?
… does not make false claims ? ? ? ? ? ? ?
… is not open in dealing with us ? ? ? ? ? ? ?
… is honest about the problems may them arise ? ? ? ? ? ? ?
… has difficulties answering our questions ? ? ? ? ? ? ?
… has made sacrifices for us in the past ? ? ? ? ? ? ?
… cares about us ? ? ? ? ? ? ?
… has supported us in times of shortages ? ? ? ? ? ? ?
… is like a friend ? ? ? ? ? ? ?
… has has been on our side ? ? ? ? ? ? ?
Rapidão Cometa…
… has a reputation for being honest ? ? ? ? ? ? ?
… has a reputation for being concerned about its customers ? ? ? ? ? ? ?
… has a bad reputation in the market ? ? ? ? ? ? ?
… has a reputation for being fair according to most customers ? ? ? ? ? ? ?
185
How important is Rapidão Cometa to your company? Please indicate your level
of agreement with the following statements.
Strongly
disagree
Strongly
agree
Rapidão Cometa is crucial to our performance ? ? ? ? ? ? ?
Rapidão Cometa is important to our business ? ? ? ? ? ? ?
If our relationship with Rapidão Cometa were discontinued, we
would have difficulty in performing its services. ? ? ? ? ? ? ?
It would be difficult for us to replace Rapidão Cometa ? ? ? ? ? ? ?
We are dependent on Rapidão Cometa ? ? ? ? ? ? ?
We do not have a good alternative to Rapidão Cometa. ? ? ? ? ? ? ?
We are important to Rapidão Cometa. ? ? ? ? ? ? ?
We are a major customer for Rapidão Cometa in our trading
area. ? ? ? ? ? ? ?
We are not a major customer for Rapidão Cometa. ? ? ? ? ? ? ?
We have made significant investments (e.g., technology,
training etc.) dedicated to our relationship with Rapidão
Cometa ? ? ? ? ? ? ?
If we switched to a competing logistics provider, we would lose
a lot of the investment we have made in this relationship. ? ? ? ? ? ? ?
We have invested substantially in personnel dedicated to this
relationship ? ? ? ? ? ? ?
If we decided to stop working with Rapidão Cometa, we would
be wasting a lot of knowledge regarding its methods of
operation. ? ? ? ? ? ? ?
Rapidão Cometa has gone out of its way to link us with its
business ? ? ? ? ? ? ?
Rapidão Cometa has tailored its services and procedures to
meet the specific needs of our company ? ? ? ? ? ? ?
Rapidão Cometa would find it difficult to recoup its investments
in us if our relationship were to end. ? ? ? ? ? ? ?
186
How would you describe the market for the product you ship with Rapidão
Cometa ?
Strongly
disagree Neutral
Strongly
agree
The demand is unpredictable ? ? ? ? ? ? ?
Sales forecasts are accurate ? ? ? ? ? ? ?
The industry production is stable ? ? ? ? ? ? ?
The demand trends are easy to monitor ? ? ? ? ? ? ?
The market is very complex ? ? ? ? ? ? ?
There are many new products ? ? ? ? ? ? ?
There are many competitors ? ? ? ? ? ? ?
How would you describe the market for logistics services in Brazil?
Strongly
disagree Neutral
Strongly
agree
The market for logistics services in Brazil
…. has an unpredictable demand ? ? ? ? ? ? ?
…. has a stable of service availability ? ? ? ? ? ? ?
… is easy to monitor ? ? ? ? ? ? ?
… is very complex ? ? ? ? ? ? ?
… has many service offerings ? ? ? ? ? ? ?
… has many logistics providers ? ? ? ? ? ? ?
187
B. Questions on the operational and competitive profiles of your company
The following items describe the complexity of the logistics operations of your
company. Please indicate your level of agreement.
Strongly
disagree Neutral Strongly agree
We have a complex network of trading partners. ? ? ? ? ? ? ?
The timeliness of the transactions in our supply chain is
crucial in our business. ? ? ? ? ? ? ?
We must accomplish very short order cycle times for
customer orders. ? ? ? ? ? ? ?
We have a complex network of origin/destination (OD)
pairs. ? ? ? ? ? ? ?
Our products require specialized transportation,
storage, or handling (e.g. temperature, humidity, etc.) ? ? ? ? ? ? ?
The following items describe the logistics personnel of your company. Please
indicate your level of agreement.
Strongly
disagree Neutral
Strongly
agree
Relative to the size of our firm, we have a large group of
upper-level managers dedicated to logistics ? ? ? ? ? ? ?
Relative to the size of our firm, we have a large group of
employees across all levels dedicated to logistics ? ? ? ? ? ? ?
Our logistics personnel have a deep understanding of our
logistics operations ? ? ? ? ? ? ?
Our logistics personnel know where problems and
bottlenecks might exist in our logistics operations ? ? ? ? ? ? ?
Our logistics personnel are capable of finding effective
solutions when problems arise ? ? ? ? ? ? ?
188
The following sentences describe the relationship between your company and
your company’s major customers (attention: NOT Rapidao Cometa). Please
indicate your level of agreement.
Strongly
disagree
Strongly
agree
We trust each other ? ? ? ? ? ? ?
They are trustworthy on important things. ? ? ? ? ? ? ?
According to our past business relationship, my company thinks
that they are trustworthy persons. ? ? ? ? ? ? ?
My company trusts them. ? ? ? ? ? ? ?
We rely on each other. ? ? ? ? ? ? ?
We both try very hard to establish a long-term relationship. ? ? ? ? ? ? ?
We work in close cooperation.
We keep in touch constantly. ? ? ? ? ? ? ?
We communicate and express our opinions to each other
frequently. ? ? ? ? ? ? ?
We can show our discontent towards each other through
communication. ? ? ? ? ? ? ?
We can communicate honestly. ? ? ? ? ? ? ?
We share the same worldview. ? ? ? ? ? ? ?
We share the same opinion about most things. ? ? ? ? ? ? ?
We share the same perspectives toward things around us. ? ? ? ? ? ? ?
We share the same values. ? ? ? ? ? ? ?
We always see things from each other’s perspective. ? ? ? ? ? ? ?
We know how each other thinks. ? ? ? ? ? ? ?
We understand each other’s values and goals. ? ? ? ? ? ? ?
We care about each other’s feelings. ? ? ? ? ? ? ?
My company regards “never forget a good turn” as our business
motto. ? ? ? ? ? ? ?
We keep our promises to each other in any situation. ? ? ? ? ? ? ?
If our customers gave assistance when my company had
difficulties, then I would repay their kindness. ? ? ? ? ? ? ?
189
Thank you for completing the survey to this point. We appreciate the time you have
taken to complete this survey!
We would now like to ask you to complete a few background questions. As with the
rest of the survey, we guarantee strict confidentiality!
What is your position?
President/CEO/COO
Vice president, logistics, transportation, or distribution
Director, Logistics, transportation, or distribution
Manager, Logistics, transportation, or distribution
Supervisor, Logistics, transportation, or distribution
Employee, Logistics, transportation, or distribution
Logistics analyst
Other, please specify:
For how many years has your company been operating?
For ___ years (e.g. 2.5)
For how many years have you been working in this position?
For ___ years (e.g. 2.5)
For how many years have you been working for this company?
For ____ years (e.g. 2.5)
What category better describe your industry?
Please select only one industry.
Food and beverage
Automotive
Consumer goods
Industrial equipment
Electronics and related instruments
Computer hardware and peripheral equipment
Chemicals and plastics
Retailing
Healthcare
Other __
190
What are the current monthly revenues of your company (in R$
thousand/month)
Up to 9 10-100 101-
1000
1001-
5000
5001-
10000
10001 -
100000
100.001
-499.000
More
than
500.000
What is the approximate number of employees in your business unit?
…. Employees
How many logistics providers/carriers does your business unit use?
…. logistics providers.
Please complete the following questions having Rapidao Cometa in mind.
Which services does Rapidão Cometa provide to your company?
Please mark all applicable services.
Transportation planning ?
Transportation operations ?
International freight forwarding ?
Cross-docking ?
Warehousing ?
Inventory control/management ?
Pick/pack operations ?
Assembly ?
Reverse logistics ?
Logistics information systems ?
Lead logistics management ?
EDI capability ?
Order fulfillment ?
Freight forwarding ?
Route and network optimization ?
Freight consolidation ?
Outbound traffic control ?
Inbound traffic control ?
Other: _____
What is Rapidão Cometa’s approximate share of your total outsourced logistics
expenditures?
…. Percent (e.g., 2.5)
For how long has your unit been working together with Rapidao Cometa in a
way that you would call a “close relationship”?
For …. Years (e.g., 2.5)
What is the total duration of the current contract with Rapidao Cometa?
….. years (Zero – 0 – if no contract)
191
Thank you for taking part in this survey!
Please provide us with the address to which we may forward your summary
report:
Last name
First name
Email address
Company
Street
Zip
City
Phone number
Can we contact you in order to get further information? ___ Yes ___ No
Please use the space below for comments and suggestions:
192
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doc_646385652.pdf
Developing close relationships with third-party logistics providers (3PLs) has been acknowledged in the literature as a beneficial strategy for 3PLs and customer firms. It has been shown that customers embedded in close relationships with 3PLs achieve higher levels of operational and financial performance.
ABSTRACT
Title of Document: DETERMINANTS OF CUSTOMER
PARTNERING BEHAVIOR IN LOGISTICS
OUTSOURCING RELATIONSHIPS:
A RELATIONSHIP MARKETING
PERSPECTIVE
Adriana Rossiter Hofer, PhD, 2007
Directed By: Professor Martin E. Dresner
Department of Logistics, Business, and Public
Policy
Developing close relationships with third-party logistics providers (3PLs) has
been acknowledged in the literature as a beneficial strategy for 3PLs and customer
firms. It has been shown that customers embedded in close relationships with 3PLs
achieve higher levels of operational and financial performance. 3PLs also benefit
from engaging in these relationships by generating higher levels of customer
satisfaction, customer retention, and referrals to new customers. In order to
complement these findings, this study integrates theories and empirical evidence
drawn primarily from relationship marketing to develop a model of the antecedents of
customer partnering behavior in logistics outsourcing relationships.
It is proposed that a combination of key interorganizational conditions and
customer characteristics directly impacts a customer’s partnering behavior with a
3PL. More specifically, a customer embedded in a relationship with a 3PL in which
there are high levels of dependence, trust, and satisfaction, is more likely to exhibit
higher levels of partnering behavior with a 3PL. In addition, a customer’s prior
experiences with partnering, and policy of engaging in interactive relationships with
customers, will also positively impact its partnering behavior with a 3PL.
Antecedents of dependence and trust are also identified in the model.
Data are collected through a web-based survey with customers of a large
Brazilian 3PL and the model tested using structural equation modeling. The results
support several of the hypotheses proposed in the model. In particular, evidence is
found that customer-specific characteristics, such as a customer relationship
marketing orientation and prior experience with 3PL partnering, have a positive effect
on a customer partnering behavior with a 3PL, above and beyond the effect of
interorganizational conditions, as advocated in traditional behavioral models.
Contributions of this research include the depiction of the interplay between
environmental forces, interorganizational conditions, and firm-specific factors that are
hypothesized to impact a customer’s partnering behavior with its 3PL. With an
understanding of the mechanisms on which a customer’s partnering behavior is built,
3PLs can take effective action in the pursuit of the development of closer
relationships with their customers, contributing to the maintenance and expansion of
their customer base.
DETERMINANTS OF CUSTOMER PARTNERING BEHAVIOR IN LOGISTICS
OUTSOURCING RELATIONSHIPS:
A RELATIONSHIP MARKETING PERSPECTIVE
By
Adriana Rossiter Hofer
Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, College Park, in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2007
Advisory Committee:
Professor Martin E. Dresner, Chair
Professor Curtis M. Grimm
Professor William J. DeWitt
Professor Donna B. Hamilton
Professor A. Michael Knemeyer
© Copyright by
Adriana Rossiter Hofer
2007
ii
Dedication
To the memory of my mom Glaucia who always encouraged me to pursue my dreams
and
to two wonderful and inspirational women:
grandmas “Vovó” Tildes and “Vovó” Teca.
iii
Acknowledgements
I would like to thank, first and foremost, my husband Christian, my sister Gal,
and my brother Nel, for their endless love and support throughout these five years of
doctoral studies.
I would like to acknowledge that the completion of my PhD and this project
would not have been possible without the guidance and friendship of Professor
Martin Dresner, who has been an inspirational example of a mentor and scholar to
me.
I would like to thank Dr. Donna B. Hamilton, Dr. Bill DeWitt, and Dr. Curt
Grimm for kindly agreeing to serve on my dissertation committee and to share their
expertise. I would also like to express my gratitude to Dr. Mike Knemeyer, whose
support and encouragement have been crucial in the growth of my understanding of
the logistics outsourcing research stream.
I would like acknowledge that this study was only possible thanks to the
support of Rapidão Cometa by providing access to their customer base and assistance
during the survey design and implementation. I would like to thank Rapidão
Cometa’s executives Américo Pereira Filho, Celso Queiroz, Vanessa Ramos, and
Fernanda Rogrigues, for their consistent collaboration and assistance on the
realization of this study.
I would like to thank Dr. Gregory Hancock for kindly and patiently guiding
me through the structural equation modeling process. I would also like to thank him
for his unforgettable statistics lectures.
iv
I would like to thank special staff members of the R. H. Smith School of
Business, Mary Slye, Ann Stevens, and Hazel Wentt, for always kindly assisting me
with administrative issues.
Finally, I would like to thank all my professors, friends and colleagues for
their friendship and support during the doctoral studies.
v
Table of Contents
Dedication..................................................................................................................... ii
Acknowledgements...................................................................................................... iii
Table of Contents.......................................................................................................... v
List of Tables .............................................................................................................. vii
List of Figures ............................................................................................................ viii
Chapter 1: Introduction................................................................................................. 1
Chapter 2: Literature Review...................................................................................... 11
2.1. Logistics outsourcing....................................................................................... 11
2.1.1. Third-party logistics (3PL) providers defined .......................................... 12
2.1.2. An overview of the 3PL industry.............................................................. 13
2.1.3. Logistics outsourcing in Brazil ................................................................. 15
2.1.4. Logistics outsourcing research.................................................................. 16
2.2. Partnering......................................................................................................... 26
2.2.1. 3PL-customer partnership defined............................................................ 26
2.2.2. Distinguishing between partnerships and other interorganizational
relationships ........................................................................................................ 31
2.2.3. Previous research on the antecedents and outcomes of partnering........... 34
2.3. Relationship Marketing.................................................................................... 39
2.3.1. Relationship marketing defined ................................................................ 40
2.3.2. Theoretical foundations of relationship marketing................................... 42
2.3.3. A brief introduction to social exchange theory......................................... 44
2.3.4. Relationship marketing literature focused on business-to-business
relational exchange ............................................................................................. 47
2.3.5. Relationship marketing orientation........................................................... 51
2.4. Ganesan’s (1994) model of long-term orientation........................................... 53
2.5. Cultural differences and logistics outsourcing................................................. 63
2.6. Conclusion ....................................................................................................... 66
Chapter 3: Model Development and Hypotheses ....................................................... 67
3.1. Conceptual model ............................................................................................ 67
3.2. Hypotheses development ................................................................................. 71
3.2.1. Primary antecedents.................................................................................. 71
3.2.2. Antecedents of dependence....................................................................... 82
3.2.3. Antecedents of trust .................................................................................. 93
3.3. Hypothesized model......................................................................................... 98
3.4. Contrasting the model of customer partnering behavior with Ganesan’s model
of long term orientation ........................................................................................ 102
3.5. Conclusions.................................................................................................... 105
Chapter 4: Methodology ........................................................................................... 106
4.1. Research design ............................................................................................. 106
4.2. Measurement of the constructs ...................................................................... 108
4.2.1. Dependent construct: customer partnering behavior .............................. 110
4.2.2. Primary antecedents................................................................................ 111
vi
4.2.3. Antecedents of dependence..................................................................... 117
4.2.4. Antecedents of trust ................................................................................ 122
4.3. Survey design................................................................................................. 123
4.4. Survey implementation.................................................................................. 126
4.5. Conclusions.................................................................................................... 128
Chapter 5: Data Analysis and Results....................................................................... 129
5.1. Final sample and respondents characteristics ................................................ 129
5.2. Descriptive statistics of the constructs........................................................... 133
5.3. Tests for non-response bias............................................................................ 136
5.4. Structural equation modeling......................................................................... 139
5.4.1. Data preparation and preliminary analysis ............................................. 140
5.4.2. Measurement phase................................................................................. 143
5.4.3. Structural phase....................................................................................... 148
5.5. Results............................................................................................................ 151
5.6. Conclusions.................................................................................................... 158
Chapter 6: Discussion and Concluding Remarks...................................................... 160
6.1. Discussion of model results ........................................................................... 160
6.2. Contributions.................................................................................................. 166
6.3. Managerial implications................................................................................. 169
6.4. Limitations ..................................................................................................... 172
6.5. Future research............................................................................................... 173
6.6. Summary and concluding remarks................................................................. 175
Appendices................................................................................................................ 177
Bibliography ............................................................................................................. 192
vii
List of Tables
Table 1. Summary of the findings of Ganesan’s (1994) model .................................. 60
Table 2. List of hypotheses of the determinants of customer partnering behavior..... 99
Table 3. Position profile of the respondents ............................................................. 130
Table 4. Respondents’ Industries.............................................................................. 130
Table 5. Number of employees of the respondent firms........................................... 131
Table 6. Number of functions outsourced................................................................. 132
Table 7. Respondents’ logistics functions outsourced.............................................. 133
Table 8. Descriptive statistics of the constructs........................................................ 134
Table 9. Correlation matrix for the averages of the constructs................................. 135
Table 10. Comparison of vector means between early vs. late respondents............. 137
Table 11. Manova comparison of vector means (respondents vs. non-respondents) 138
Table 12 Variance extracted of the constructs.......................................................... 145
Table 13. Test for discriminant validity for construct pairs with high covariance... 146
Table 14. Construct reliability results....................................................................... 148
Table 15. Summary of fit indices for the full model................................................. 150
Table 16. Standardized path coefficients. ................................................................. 151
Table 17. Summary of Results.................................................................................. 153
viii
List of Figures
Figure 1. Continuum of relationship styles (extracted from Gardner et al 1994)....... 31
Figure 2. Ganesan’s (1994) model.............................................................................. 55
Figure 3. Ganesan’s (1994) model with results .......................................................... 59
Figure 4. Conceptual model of customer partnering behavior in logistics outsourcing
relationships ................................................................................................................ 70
Figure 5. Primary antecedents of customer partnering behavior in logistics
outsourcing relationships. ........................................................................................... 72
Figure 6. Sub-model of antecedents of dependence. .................................................. 84
Figure 7. Sub-model of the antecedents of trust ......................................................... 93
Figure 8. A model of the determinants of customer partnering behavior in logistics
outsourcing relationships .......................................................................................... 101
Figure 9. Ganesan’s (1994) model of long term orientation..................................... 103
Figure 10. New variables introduced in the model of customer partnering behavior104
Figure 11. Model of customer partnering behavior in logistics outsourcing
relationships. ............................................................................................................. 109
Figure 12. Daily counts of survey completion.......................................................... 137
Figure 13. Statistically significant path coefficients................................................. 152
Figure 14. A simplified model of customer partnering behavior in logistics
outsourcing relationships .......................................................................................... 161
1
Chapter 1: Introduction
“Companies will be looking for more flexibility and service
partners who can allow them to focus on their core business and
spend less time managing the supply chain.” (Dan Colleran,
President Suddath Logistics Group, Inbound Logistics, July
2003, p. 102)
“Shippers who work with their carriers will get the trucks. Those
who don’t will pay much more for transportation.” (Lance
Craig, Inbound Logistics, January 2006, p. 166)
Outsourcing logistics functions to third-party logistics providers (3PLs) -
independent firms that perform single or multiple logistics services on behalf of a
shipper (Sink et al 1996) - is not a new phenomenon. For decades, firms have
outsourced transportation and warehousing activities, and more recently have started
to purchase complex and customized services, such as consulting for supply chain
solutions and customer service management. Many advantages accrue from logistics
outsourcing. 3PLs can provide logistics expertise and cost benefits to their customers,
since firms that outsource logistics services do not have to spend large amounts of
capital to own and manage expensive assets, such as trucks and warehouses
(Bolumole 2003). In addition, 3PLs can also offer advantages of economies of scale,
since they may use the assets more efficiently by sharing them among many
customers.
A strong trend in the logistics outsourcing industry refers to the change in the
nature of the relationship between 3PLs and their customers; i.e., the buyers of their
services (Leahy et al 1995). Due to globalization, many 3PL customers face greater
competition and more rapidly changing customer needs. These factors strengthen the
2
pressures for cost-reduction and increased customer service levels through the pursuit
of operational efficiencies, introduction of new products, and improved product
quality. In addition, expanding their business geographically through sourcing,
manufacturing and distributing overseas – which means longer distances, language
barriers, different regulations, etc – has brought increased complexity to logistics
operations and coordination of supply chains. Moreover, recent capacity constraints,
in terms of port congestion and restricted transportation supply, have imposed extra
burdens on logistics managers. This challenging reality has brought the need for firms
to change the nature of their relationships with their 3PLs in order to focus on their
core competencies and compete in today’s global markets. But 3PLs, as well, face
many challenges. The 3PL industry has already grown to a considerable size
(Berglund et al 1999). In the U.S., for example, it was estimated that payments for
3PLs services exceeded US$ 103 billion in 2005
(http://www.3plogistics.com/3PLmarket.htm).The landscape of their market has
continuously changed. Larger 3PLs have merged and have expanded their operations
geographically (Lieb and Bentz 2005). New 3PLs have entered the market, with
origins from the most unexpected areas, such as information technology and
consulting (Berglund et al 1999). Due to capacity constraints, efficiently coordinating
the operations of customers has become increasingly complex. While encountering
continuous pressures to reduce prices, offer supplementary services, and expand
geographic coverage, 3PLs face rising fuel prices and operating costs. 3PLs, then,
also understand the need to collaborate with their customers. This is reflected in 3PL
advertising campaigns, in which they emphasize their role as reliable partners. In
3
short, both 3PLs and customers of their services have realized the need to adapt to a
new business environment. One way they both can do this is by developing long-
term, collaborative relationships.
Accordingly, the changing nature of logistics outsourcing relationships has
received much attention in the academic logistics literature, including the redefinition
of “3PL.” The “modern” definition of 3PL follows Murphy and Poist (1998), who
define third-party logistics as “a relationship between a shipper and third party which,
compared to basic services, has more customized offerings, encompasses a broader
number of service functions, and is characterized by a longer-term, more mutually
beneficial relationship.” The 3PL literature, based mainly on case studies and surveys
with 3PLs and their customers, has largely emphasized the importance of nurturing
close relationships between 3PLs and their customers. For example, Leahy et al
(1995) surveyed 3PLs and considered customer orientation and dependability as the
most important determinants of successful relationships. Larson and Gammelgaard
(2001), based on a survey with logistics providers and with case studies, found
evidence that close collaboration between buyers, suppliers, and 3PLs provided
benefits, such as greater flexibility, higher inventory availability, and more on-time
pick up and delivery.
The few studies with theory-testing in the 3PL literature have also emphasized
that relational elements are important for satisfactory logistics outsourcing
relationships, and ultimately for achieving higher performance. Knemeyer et al
(2003), for example, found that customers whose relationships with 3PLs involve
higher operational and strategic integration, exhibit higher levels of key relationship
4
marketing elements, such as trust in the partner, commitment with the relationship,
and dependence on the partner. These “closer relationships” also exhibited higher
levels of relationship marketing outcomes, such as customer retention, and referrals
of the 3PLs to other potential customers. Knemeyer and Murphy (2004)
complemented their previous findings, showing that relationship marketing elements
had a stronger impact on marketing outcomes than the effects of firm characteristics,
such as size and number of functions outsourced. Positive effects from engaging in
relationships with 3PLs were also found by Panayides and So (2005). They found that
firms that engaged in relationships with 3PLs with higher levels of trust, bonding,
communication, shared value, empathy, and reciprocity, developed higher levels of
key organizational capabilities, such as organizational learning and innovation,
promoting an improvement in supply chain effectiveness and performance. Sinkovics
and Roath (2004) also found a positive effect of customer collaboration with 3PLs on
a customer’s market and logistics performance. In the same manner, Stank et al
(2003) showed a positive impact of relational performance between a 3PL and its
customer on the customer’s market share. From the examples above, it can be noted
that the relationships between 3PLs and their customers differ in terms of operational
and strategic integration, and that, in general, closer relationships lead to greater
benefits to the parties involved.
It is relevant to note that, although the measures adopted to describe the nature
of the relationships between 3PLs and their customers differ across studies, they all
convey the same concept: a “close relationship.” All of the measures capture, to some
extent, dimensions of a relational exchange. A relational exchange differs from a
5
discrete exchange in the sense that it extends over time, and the participants can be
expected to engage in social exchange and derive non-economic, personal
satisfaction. Relational exchanges, however, can be translated into patterns of
behavior. One construct that captures these facets of relational behavior is partnering
behavior. Partnering can be defined as an “on-going relationship between two firms
that involves a commitment over an extended time period, and mutual sharing of
information and the risks and rewards of the relationship” (Ellram and Hendrick
1995). Partnerships are a “hybrid” governance mechanism in which the coordinative
forces include trust and commitment (Rese 2006). Partnering behavior exhibits the
characteristics of joint-planning, sharing benefits and burdens, extendedness,
systematic operational exchange, and mutual operating controls (Gardner et al 1994).
Using the concept of partnering, the objective of this dissertation is to
complement the existing theory-based 3PL literature (that focuses on the outcomes of
close relationships) and, with a theoretical framework, answer the following research
questions:
1) For firms already outsourcing logistics services, under what
conditions will they be more likely to exhibit a partnering behavior
with their 3PLs?
2) What is the interplay between environmental forces,
interorganizational conditions and firm specific factors in shaping
such behavior?
6
3) Which factors have a stronger effect on shaping this behavior? Are
interorganizational conditions created in the relationship stronger
predictors than customer-specific factors?
In order to answer these questions, a relationship marketing perspective is
adopted. Relationship marketing, often referred to as a “major shift in marketing
theory and practice” (Rao and Perry 2002), is a widely used perspective in marketing
research that investigates the creation, development, and maintenance of committed,
interactive, and profitable relationships with selected partners over time (Harker
1999). Given that the objective of relationship marketing is to establish, develop and
maintain successful, mutually beneficial relational exchanges (Morgan and Hunt
1994, Hewett and Bearden 2001), bringing relationship marketing to 3PL research is
an appropriate theoretical perspective to investigate the determinants of partnering.
The relationship marketing perspective draws on many theories and schools of
thought, among which social exchange theory (SET) is used to investigate the
formation and dynamics of relational exchanges (Rao and Perry 2002). According to
social exchange theory, relationships are developed when exchange partners perceive
that they accrue higher rewards from the relationship than would be possible outside
the relationship (Thibaut and Kelley 1959). Dependence on the partner and trust in
the partner are considered the main antecedents of relational exchanges (Lambe et al
2001). In addition, satisfaction with previous outcomes of a relationship has been
shown to impact a relationship’s continuity and development (Ganesan 1994, Dwyer,
Schurr and Oh 1987). These three factors are captured in the seminal marketing piece
of Ganesan (1994), who investigated the determinants of long-term orientation in
7
buyer-seller relationships; i.e., the perception that the relationship outcomes are
expected to benefit the exchange partners in the long run.
Following the foundational premises of relationship marketing and social
exchange theory, this dissertation builds upon and extends Ganesan’s (1994) model
into the context of logistics outsourcing relationships. More specifically, following
Ganesan, it is hypothesized that a 3PL customer’s partnering behavior is positively
influenced by: 1) a customer’s dependence on a 3PL, 2) a customer’s trust in a 3PL,
and 3) a customer’s satisfaction with a 3PL. However, it is hypothesized that these
three factors are not sufficient to explain the customer’s partnering behavior. SET
predicts that the relationship dynamics are the major forces in explaining relationship
development, but a partner’s particular history (e.g., Uzzi 1996, Ho et al 2003) and
internal orientation (e.g., Bolumole 2001, Sin et al 2005a) may affect relationship
behavior as well. Firms that have had earlier partnership-type outsourcing
relationships may have developed a capability that facilitates partnering with the
current 3PL. Moreover, according to the relationship marketing literature, firms may
have unique strategic orientations towards engaging in relationships with main
stakeholders (e.g., customers, partners), or a relationship marketing orientation, that
might also influence the decision to engage in partnerships with the 3PL.
In order to test the model briefly described above, the customers of a large
Brazilian 3PL provider called Rapidão Cometa are surveyed. Rapidão Cometa
(www.rapidaocometa.com.br) is an asset based company with broad geographical
coverage both in Brazil and overseas through an operational alliance with a global
logistics provider (i.e., FedEx). The firm has been in business for over 60 years, and
8
has over 3,000 employees and 7,000 active customers. It provides an entire array of
logistics services, ranging from simple transportation and warehouse management to
customized consulting for supply chain solutions. It also utilizes information
technology in the provision of services, such as electronic data interchange - EDI and
warehouse management systems – WMS. Its customer base is composed of small and
large firms from a variety of industries, such as apparel, auto, and electronics, among
others. Given its strong reputation, logistics capabilities, and its diverse customer-
base, Rapidão Cometa is an appropriate source of information to address the research
questions proposed in this dissertation.
This dissertation contributes to the logistics and marketing literatures and to
managers as well. Contributions of this dissertation include:
1) Contributing to the 3PL literature by developing and testing a
theoretically-driven model. As noted above, few examples in the
literature on logistics outsourcing relationships have used hypotheses
testing, and even fewer articles have been built on theory;
2) Extending Ganesan’s model by: 1) including new explanatory
variables using different theoretical perspectives; 2) including other
dimensions of relational exchange in the dependent variable (long-
term orientation in Ganesan’s (1994) model is one dimension of
partnering behavior);
3) Identifying whether a firm’s particular experience with partnering or
its specific orientation towards partnering with stakeholders is a
stronger predictor of its partnering behavior than interorganizational
9
factors, such as trust in the partner, satisfaction with the partner, and
dependence on the partner;
4) Using structural equation modeling (SEM), a powerful multivariate
technique that can be used to investigate relationships among latent,
unobserved variables. SEM is a more advanced analytical approach
than those commonly in use in 3PL research, such as percentages or
means testing. This provides a contribution to the 3PL literature, in
which many papers lack a “formalized, advanced methodological
approach” (Maloni and Carter 2005);
5) Expanding the geographical coverage of 3PL research by collecting
data from Brazil, an important market with strong growth potential. As
Maloni and Carter (2005) point out, “much of the existing 3PL
research assessed one geographical region, generally the United
States.” Other studies, however, have focused on Western Europe, the
United Kingdom, the Netherlands, Australia, China, Singapore and
Malaysia. An extended geographical scope in 3PL research can be
beneficial, especially for practitioners given the importance of Brazil
as an important U.S. trade partner. Also, since many constructs have
been already tested with U.S. firms, there is an opportunity for future
cross-cultural comparison studies;
6) And for managers, considering the performance benefits of close
relationships for 3PLs and customers, the identification of what factors
have a stronger effect on a customer partnering behavior can guide
10
3PL managers on the nurturing of partnerships with their customers,
thus helping them maintain and develop their customer base.
The structure of this dissertation is as follows. Chapter 2 presents a literature
review of the main research streams that are related to this dissertation, including
logistics outsourcing, partnerships, relationship marketing, and a brief description of
Ganesan’s model of long-term orientation in buyer-seller relationships. Chapter 3
presents the conceptual model and describes the rationale for the hypotheses in detail.
Chapter 4 describes the methodology to be undertaken in order to measure the
constructs, survey design and implementation, and data collection. Chapter 5 presents
the steps followed in the data analysis and the model results. Finally, Chapter 6
presents a discussion of the findings of this dissertation, contributions, limitations,
and avenues for future research.
11
Chapter 2: Literature Review
In order to understand in greater detail the model proposed in this dissertation,
this chapter provides an overview of the various areas related to the research question,
as well as a theoretical background for the hypotheses development, the subject of
Chapter 3. The first section describes the literature in logistics outsourcing, with a
focus on the relationships between 3PLs and their customers. The second section
provides the definition of customer partnering behavior and a brief overview of the
various research streams in the logistics, marketing, and strategy literatures that have
investigated the formation of “hybrid governance structures”, of which partnerships is
one type. The third section presents an introduction to relationship marketing, with
special attention to social exchange theory, the theoretical perspective that serves as
the basis for the development of the model. Next, Ganesan’s (1994) model is briefly
described, along with the literature that has extended his work. Finally, some
comments on the effects of cultural differences in logistics outsourcing are addressed.
2.1. Logistics outsourcing
This section provides an introductory overview of logistics outsourcing
concepts, industry trends, and academic research. First, due to the various
terminologies used in the literature, the definition of third-party logistics providers
(3PL) is provided, along with a brief characterization of the 3PL industry and its main
trends both in North America and in Brazil, where the data were collected. Finally, an
overview of the main research questions that have been addressed in the 3PL
literature is presented with a focus on the empirical work dedicated to 3PL-customer
12
relationships (see Razzaque and Sheng 1998, or Maloni and Carter 2005 for
comprehensive literature reviews on 3PL research).
2.1.1. Third-party logistics (3PL) providers defined
The involvement of 3PLs in the supply chain is becoming increasingly
necessary for a firm’s survival in the global and competitive environment (Bask
2001). Increased competition and globalization, and the need to reduce cycle times
and inventory levels, have created a need for more responsive processes based on
efficient supply chain partnerships. These pressures have encouraged management to
re-examine a firm’s individual and collective positions within the supply chain, and
have increased the interest in outsourcing a broad array of logistics services.
Outsourcing logistics services to 3PLs has become not only a means to cost-
efficiency, but also a strategic tool for creating competitive advantage through
increased service and flexibility (Skjoett-Larsen 2000).
3PLs are independent firms that provide single or multiple logistics services
on behalf of a shipper (Sink et al 1996, Berglund et al 1999). For example, they can
just provide transportation services, or, conversely, a broad array of logistics services,
such as customs clearance, information technology (IT) based services for inventory
and customer management, and consulting for supply chain solutions. The concept of
3PL, however, is often not well-defined, either in the academic or the industry
literature. The earlier definitions of 3PL do not consider a crucial element of the
current state of logistics outsourcing: the nature of the relationship between the
provider and the customer (Murphy and Poist 2000). The clear trend in the literature
is towards the notion that “modern” 3PL logistics involves long-term, mutually
13
beneficial relationships (Leahy et al 1995, Papadoupoulou and Macbeth 2001).
Therefore, the definition of 3PL adopted for this dissertation follows Murphy and
Poist (1998) and considers that third-party logistics involves “a relationship between
a shipper and a third-party which, compared to basic services, has more customized
offerings, encompasses a broader number of service functions and is characterized by
a longer-term, more mutually beneficial relationship” (p. 26). The characteristics of
long-term and mutual benefits are in line with the concept of partnering, the focus of
this dissertation.
2.1.2. An overview of the 3PL industry
The main firms in the 3PL industry come from a variety of backgrounds, but
can be categorized into three groups (Berglund et al 1999): 1) traditional
transportation companies that have expanded their services into logistics; 2) parcel
and express companies (e.g., DHL, TNT, UPS), that entered the logistics market
based on their worldwide networks and experience with expediting freight; and
finally 3) players from other areas, such as information technology, management
consulting, and financial services.
The 3PL industry has achieved significant growth over the past several years
(Berglund et al 1999). Although no official statistics are available, it is estimated that
the U.S. 3PL/contract logistics market has grown from approximately US$ 31 billion
in revenues in 1996 to US$ 85 billion in 2004 (Capgemini et al. 2005). This trend is
mirrored by the number of firms outsourcing logistics services. Lieb and colleagues
(1999, 2003, 2005) performed annual surveys of large U.S. manufacturers. The
results show that the percentage of firms using 3PL services has grown from
14
approximately 65% in 2003 to 80% in 2004. This finding is consistent with the
findings of the annual surveys conducted by Langley Jr. with industry partners (e.g.,
Capgemini et al. 2003, 2004, 2005). They have found that the percentage of 3PL
users has increased from 72% in 2000 to 80% in 2005. Concurrent with its growth,
the 3PL industry has also experienced fundamental changes. There are more
competitors in the market. The array of services provided by 3PLs has increased in
response to customer desires for one-stop shopping. Aside from the traditional service
offerings of warehousing and outbound and inbound transportation services, other
frequently outsourced activities are customs brokerage, customs clearance, and
freight forwarding. Other more complex activities are also outsourced, including
those directly related to customers (e.g., order fulfillment, customer service and order
entry/processing), information technology (IT), and strategic services, such as
consulting, procurement of logistics, and 4PL services
1
(Capgemini et al. 2005). In
addition, pressures for reducing prices, providing multiple services, and expanding
geographical coverage, have forced 3PL providers to engage in mergers, acquisitions,
and/or strategic alliances (Foster 1999, Lieb 2003, 2005
. As a result, 3PLs face thechallenges of working closely with their partners. Moreover, major 3PLs have
become more selective about customers and have shifted their focus towards longer-
term relationships (Lieb 2005), with greater emphasis on the overall logistics
processes rather than on isolated task-based operations (Eyefortransport 2006). In this
dynamic and challenging environment, the 3PL industry offers relevant research
opportunities in the logistics and supply chain management arenas.
1
4PL can be defined as an integrator that combines its own resources with other organizations’
resources in order to design, build, and run comprehensive supply chain solutions.
15
2.1.3. Logistics outsourcing in Brazil
The state of logistics outsourcing in Brazil is, in many ways, similar to what is
found in the U.S. Although Brazil is a smaller market, there are similarities in terms
of challenges 3PLs face and in industry trends. Local 3PLs have merged and allied
with global 3PLs in their search for larger market shares and broader geographical
coverage. Although no official statistics are available, according to the Brazilian
magazine Tecnologística
2
, there are about 200 3PLs operating in Brazil, realizing in
2001 approximately US$ 2.36 billion in total revenues (www.guiadelogistica.com.br).
In Brazil, a great variety of industries outsource logistics services (e.g., chemical,
pharmaceutical, electronics, furniture, apparel, wholesaling, and retailing), reflecting
a diversity in terms of logistics complexity (COPPEAD and Booz-Allen, 2001).
About 90% of the Brazilian 3PLs have roots as companies that provide basic
transportation and warehousing services. Although some of these firms have
increased their portfolios of services offered, many still offer only the basic services;
i.e. transportation and warehousing. More recently, large American and European
providers have entered the Brazilian market. About 70% of the logistics providers are
asset-based firms and have grown, in part, due to the absence of a good public
warehouse infrastructure, and their willingness to provide reliable transportation
services.
In 2001, the consulting firm Booz Allen and the Brazilian academic institution
CEL/COPPEAD (Center for Logistics Studies at the Business Graduate Studies and
Research Institute at the Federal University of Rio de Janeiro) conducted a study of
2
According to Tecnologística magazine, a 3PL “provides services related to the logistics area.”
16
the contract logistics market in Brazil. From a survey of 67 3PLs and additional in-
depth interviews, they identified many challenges facing the 3PL industry in Brazil
(Booz Allen 2001): First, the substantial differences in taxes among the different
regions and states of Brazil hinder the optimization of logistics networks. Another
major problem refers to the poor transportation (both in terms of physical conditions
and security) and public warehousing infrastructures, which diminishes the ability of
3PLs to operate efficiently. Another barrier to the expansion of the industry is the
lack of qualified human resources in logistics. More importantly, Brazilian 3PLs
complain about the lack of customer maturity; i.e., customers not being able to
specify expectations and needs. Customers, on the other hand, argue that 3PLs are not
able to meet their expectations. This disagreement between 3PLs and their customers
is an indicator that the “culture of customer-3PL collaboration” may not be mature;
i.e., that there is room for 3PL-customer relationships to develop. As such, compared
to the U.S. 3PL industry, one might expect fewer long-term relationships, and fewer
activities outsourced in Brazil.
2.1.4. Logistics outsourcing research
The academic 3PL literature is primarily based on surveys and case studies
that capture customer and 3PL perspectives on the following topics (Razzaque and
Sheng 1998): the current and future state of the 3PL industry (Murphy and Poist
1998, Berglund et al 1999, Sum and Teo 1999); identification of drivers for
outsourcing, the extent of logistics outsourcing, enablers and hinderers of logistics
relationships (Wilding and Juriado 2004); and the investigation of the dynamics of
logistics outsourcing relationships; i.e., how relationships grow and what factors
17
affect their evolution and decline (Knemeyer 2003, 2004, 2005). Overall, the articles
emphasize the growing potential of the industry and the benefits to supply chains
through logistics outsourcing, not only as a means to cost-efficiency, but also as a
strategic tool for creating competitive advantage through increased service and
flexibility (Skjoett-Larsen, 2000).
As the following paragraphs show, much of the academic 3PL literature has
been exploratory in nature. There have been few examples of theory testing (Maloni
and Carter 2005), indicating an opportunity gap for advancement of theory and status
of academic work in this field. At this point, it is relevant to note that empirical work
in the 3PL industry presents many challenges. First, the size of the industry is difficult
to estimate, since governmental statistics are often not available. Also, many
providers are part of larger companies that do not break out data on subsidiaries
(Berglund et al 1999). Another problem relates to the confusion regarding
terminology. As Skjoett-Larsen (2000) states, new concepts, such as third-party
logistics, are characterized by multiple definitions (i.e., some researchers consider any
transportation carrier as a logistics provider whereas others include only providers
that offer a larger array of services). Berglund et al (1999), for example, mention that
many transportation companies call themselves logistics companies, or even supply
chain partners.
The current and future state of the industry. A number of articles focus on
describing the current practices and trends in logistics outsourcing from both the 3PL
and the customer perspectives. Longitudinal studies conducted by Lieb et al (e.g.,
1999, 2003, 2005) and Langley (Capgemini et al 2003, 2004, 2005), with the support
18
of industry partners, reveal relevant information. The industry has grown
continuously over the past several years, and the number and complexity of the
functions outsourced has increased. 3PL CEOs consider supply chain integration as
the most significant opportunity for 3PL providers (Lieb and Kendrick, 2003). Users
are generally satisfied with their relationships with 3PLs, but point out that some
areas should be improved, especially those related to advanced services, such as
technology innovation (Capgemini et al 2005). In this sense, some observers predict
that the 3PL entrants that emerged from the information technology and consulting
areas may be more likely to have greater competitive advantage due to their skills in
assisting supply chain optimization and integration activities (Berglund et al 1999).
Overall, the 3PL industry may be reaching maturity as 3PLs start to focus activities
on market segmentation (Lieb 2005).
Drivers and extent of logistics outsourcing. Frequently cited primary reasons
for outsourcing logistics functions include (Boyson et al 1999, Maloni and Carter
2005): cost reduction, service improvements and efficiency, and focus on core
competencies. In order to achieve these objectives, logistics outsourcing can occur at
different levels, both in terms of scope of logistics activities to be outsourced and
degree of integration between the 3PL and the buyer of the service. In this matter, a
common research stream in the 3PL literature comprises the investigation of the types
of relationships between 3PL providers and customers (Knemeyer et al 2003), and
normative frameworks regarding the make or buy decision related to logistics
activities; e.g., the steps and factors relating to the decision to outsource (Sink and
19
Langley, Jr , 1997, Maltz and Ellram, 1997), or the decision as to what kinds of
services 3PLs should provide (Hanna and Maltz, 1998).
The variety of existing outsourcing relationships was captured by Knemeyer
et al (2003). From a survey of logistics managers across the U.S. compiled from a
trade magazine subscriber list, the authors found that more developed partnerships (in
which more operational and/or strategic integration is in place) exhibit higher levels
of relationship marketing elements (commitment, investment, dependence,
communication, attachment, reciprocity) and outcomes (retention, referrals,
recovery). Overall, many factors may impact the role that 3PL providers have on
customer operations and strategies. Bolumole (2001), for example, examined 3PL
relationships in the UK petrol industry. She identified four factors that determine the
supply chain role of 3PL providers: 1) the competitive strategic orientation of the
outsourcing organization, which influences the firm’s logistics strategy; 2) the focal
firm’s perception of the 3PL role within the logistics strategy; 3) the nature of the
3PL-customer relationship (adversarial versus collaborative), and; 4) the extent to
which logistics functions are outsourced. Rabinovich et al (1999) surveyed 372
logistics managers and their results clarified different patterns of choice of logistics
functions to be outsourced. They found that firms commonly bundle transactional and
physical functions within inventory and customer service areas, with the purpose of
achieving economies of scale (efficiency) and improving customer service levels
without committing significant amounts of financial resources.
Regarding the process of outsourcing, Sink and Langley, Jr (1997), for
instance, provided a framework to guide industrial buyers in the purchasing process
20
of third-party logistics services. The proposed process contains five steps: to identify
need to outsource logistics, to develop feasible alternatives, to evaluate and select the
supplier, to implement the service, and to assess the ongoing service. Maltz and
Ellram (1997) proposed an analytical framework for the logistics outsourcing
decision based on the concept of “total cost of relationship”, an adaptation of the total
cost of ownership (TCO) procedures, traditionally used by manufacturers to
incorporate non-price considerations into the make or buy decision. Meade and Sarkis
(2002) developed a methodology to select and evaluate third-party reverse logistics
providers. It consists of a decision network hierarchy, in which elements related to the
product life cycle, the reverse logistics functions, organizational performance criteria,
and the organizational role of reverse logistics, along with their relative importance,
are simultaneously considered. Bask (2001) provided a strategic perspective on the
relationships among 3PL providers and members of supply chains. Translated into a
normative framework, she proposes that the purchased logistics services should
match the supply chain strategies employed by 3PL customers. She argues that if, for
example, a firm has a full speculation supply chain strategy
3
, it will be better off
purchasing routine logistics services since it requires less coordination with a 3PL.
Conversely, firms that employ manufacturing postponement
4
, which is operationally
more challenging, should purchase customized logistics services. Hanna and Maltz
(1998), focusing on the 3PL provider perspective, used transaction costs economics to
investigate the specific decision of Class I less-than-truckload (LTL) carriers to
3
In a full speculation strategy, manufacturing is centralized, goods are produced to inventory, and
finished products are stocked close to customers.
4
In a manufacturing postponement strategy, final manufacturing operations occur after the customer’s
order is placed. Early stages of manufacturing are centralized, and final manufacturing operations
occur in locations close to the customer.
21
expand into warehousing. They found that increased asset specificity (e.g., warehouse
with special store equipment, or with a strategic location) is associated with the
probability of warehouse ownership, and that larger carriers are more likely to own
warehousing assets used to expand their business.
Success factors / Barriers. Of great interest are the factors that contribute to
the success (or lack thereof) of logistics outsourcing relationships. Surveys and case
studies are the common methods utilized in such studies and the results obtained are
usually descriptive. Success factors are found to be present not only during, but
before the initiation of the outsourcing relationship. Factors that are cited as
determinants of successful relationships relate to the importance of the customer in
clearly specifying expectations prior to the relationship and in developing and
monitoring performance metrics (Boyson et al 1999, van Laarhoven et al 2000).
Boyson et al (1999), for example, in a survey of logistics managers across the U.S.,
emphasized the crucial importance of the contracting agreements and the need to have
in-house knowledgeable managers to audit and monitor 3PLs. Sink et al (1996) made
use of a focus group of experienced customers to capture observations of the U.S.
third party logistics market. They highlighted the importance of understanding the
various interests in contracting logistics in order to implement an efficient and
effective marketing strategy. Developing and monitoring performance metrics are
indeed very important. Through a telephone survey of third party logistics providers,
van Hoek (2001) found empirical support for the contention that performance
measurement contributes to the expansion of third party logistics alliances in terms of
offering supplementary services (e.g., product configuration, packaging, etc).
22
Aside from performance monitoring, the willingness to collaborate and
communicate with the 3PL is mentioned as a key element for relationship success.
Leahy et al (1995) surveyed fifty-one 3PLs and found that customer orientation and
dependability are the most important determinants of successful logistics outsourcing
relationships. Murphy and Poist (2000) investigated perspectives of both 3PL
providers and users. Although both providers and users expressed high levels of
satisfaction with 3PL-customer relationships, the authors noted that there is room for
improvement. They came to the conclusion that there is an apparent mismatch
between the services offered by the 3PLs and the logistics services required by 3PL
customers. This result reinforced the importance of ongoing communication between
both parties. In this respect, effective and ongoing communication is key for
anticipating customer needs and delivering solutions to problems when they emerge.
The role of information technologies (IT), including hardware, databases,
software, and other devices that support any information systems, has also been
presented as a crucial capability that enhances the 3PL-customer relationship. Lewis
and Talalayevsky (2000), for example, emphasized that global competition and the
rapid evolution of IT have contributed to the significant trend toward outsourcing of
logistics services among major U.S. firms. They highlighted how information
technology has allowed users of logistics services to focus on their core competencies
(e.g., manufacturing, marketing, etc). Using case studies with three logistics
providers, van Hoek (2002) demonstrated that technology impacts operational
relations in the supply chain and helps 3PLs improve their operations offerings.
Sauvage (2003), in a survey of French logistics service providers, showed that the
23
success of logistics outsourcing relationships is enhanced by the 3PL’s technological
ability to improve supply chain reactivity in industries immersed in a competitive
context characterized by “time compression” (i.e., shorter product life cycle times,
shorter order cycle times, etc).
Dynamics of logistics relationships. Some researchers have relied on theory
to develop models and test propositions related to the dynamics of logistics
outsourcing relationships; i.e., how these relationships evolve and what factors
influence their development. One example of a conceptual, theory-based article is
Skjoett-Larsen (2000), who viewed third party logistics from an interorganizational
point of view, using network theory to develop (but not test) propositions about the
dynamics in third party cooperation. From three case studies, Skjoett-Larsen
emphasized the importance of both exchange (e.g., technical, information, and social)
and adaptation processes (e.g., mutual modification of systems and operations) in
developing a relationship, since past and present experiences play a major part in the
development of third party cooperation. Another example of a theory-based research
can be found in Hertz and Alfredsson (2003). Adopting a social network perspective,
and using three case studies on new entrant 3PL providers and their customers, they
showed that 3PLs are influenced by customers’ customers in the development of their
business.
Empirical tests of propositions derived from theory are relatively rare in 3PL
research. van Hoek (2000) built a transaction cost economics (TCE) framework to
test propositions related to the governance structure of 3PL-customer relationships,
including the types of services, contracts, frequency of communication at different
24
organizational levels, frequency of reports, and content of coordination and
communication. He found that the offering of supplementary services, relationship
coordination, and frequency of contact are positively associated with detailed
contracts. In later research, through a telephone survey of third party logistics
providers, van Hoek (2001) found empirical support for the hypothesis that
performance measurement contributes to the expansion of third party logistics
partnerships. Another example of empirical tests of propositions can be found in
Moore (1998), who tested a model of logistics alliances from a 3PL customer
perspective. His results indicated that 3PL customers who perceive 3PLs to be
trustworthy were committed to maintaining the alliance relationships, thus decreasing
the risk of opportunism.
Knemeyer and Murphy (2004) adopted relationship marketing as a theoretical
basis and found linkages between relationship marketing activities and the perceived
performance of 3PL arrangements. More specifically, the levels of trust and
communication were found to influence customer perspectives of various 3PL
performance factors, such as operations performance and channel performance. In a
later study, Knemeyer and Murphy (2005) investigated the impact of select
relationship characteristics (e.g. communication, reputation) and customer attributes
(e.g. size, number of functions outsourced) on 3PL relationship outcomes (e.g.
customer retention, service recovery). They found that relationship characteristics
have a stronger impact than customer attributes on relationship outcomes, reinforcing
the importance of nurturing relationships regardless of the type and size of customer.
25
Sinkovics and Roath (2004), adapting the structure-conduct-performance
paradigm, found that internal capabilities (operational flexibility and cooperation)
mediate the relationship between two dimensions of firm strategic orientation
(competitor and customer orientation) and customer market performance. Although
they obtained mixed results for their hypotheses, operational flexibility was the most
salient capability, and it augments competitor orientation to impact logistics and
market performance. Positive effects of engaging in relationships with 3PLs were also
found in Panayides and So (2005). They found that relationship orientation, measured
in terms of trust, bonding, communication, shared values, empathy and reciprocity,
had a positive influence on key organizational capabilities, such as organizational
learning and innovation, thereby promoting an improvement in supply chain
effectiveness and performance. A similar result was found by Stank et al (2003), who
showed a positive impact from the relational performance between a 3PL and its
customers on the customer’s market share.
From the examples presented above, it can be seen that most work on 3PLs
has focused on exploratory surveys, case studies, or conceptual frameworks to guide
users and providers on the processes of the decision to outsource, what functions to
outsource, selection of the provider, and maintenance and monitoring of the
relationship. Theory grounded, or empirically tested research, appears with much less
frequency in the literature. It can also be noted that research has shown that
relationships between 3PLs and customers differ in terms of the functions that 3PLs
provide and in terms of operational and strategic integration. It is also shown that, in
26
general, collaborative and interactive relationships exhibit higher satisfaction and
performance.
Although the motivations to outsource seem to be consistent across studies, an
overall theoretical framework of the conditions under which closer, collaborative
relationships between 3PLs and customers will more likely occur remain unexplored.
There is much research in marketing and strategy that has investigated the formation
of interorganizational relationships, and, more specifically, interorganizational
relational exchanges and partnering. Bringing these perspectives and applying them
to the 3PL literature is, therefore, one of the main contributions of this dissertation.
2.2. Partnering
This subsection discusses relevant aspects of the partnering behavior, the
focus of this dissertation. Initially, customer partnering behavior is defined. Next, a
brief discussion of the concept of partnering is provided, distinguishing partnering
from other ‘hybrid’-type relationships. Finally, research on antecedents and outcomes
of partnering is reviewed.
2.2.1. 3PL-customer partnership defined
The pressures of increasing global competition and rapidly changing customer
tastes and preferences have turned the integration and control of the supply chain and
logistics functions into a critical activity for enterprises. In order to achieve supply
chain coordination and integration, scholars and practitioners have emphasized the
strategy of developing and nurturing long-term cooperative partnerships between
supply chain members. The literature generally supports the ability of partnerships to
27
achieve cost savings, and as a result partnerships are increasingly cited as a common
source of efficiency and competitive advantage (Gentry and Vellenga 1996, Mentzer
et al 2000, Duffy and Fearne 2004).
Partnerships are a “hybrid” governance mechanism in which the coordinative
forces include trust and commitment, in addition to price (Rese 2006). The
partnership concept is borrowed from the relational contracting literature (e.g.,
Macneil, 1978, Dwyer et al 1987) and encompasses dimensions of relational
governance (Joshi and Campbell, 2003), in which participants engage in social
exchange and take not only economic, but also non-economic, social benefits into
consideration. In a partnership, as with any relational exchange, each transaction is
viewed in terms of its historical context and its anticipated future prospects (Kim and
Chung 2003); i.e., as opposed to a discrete exchange that is relatively short term with
limited communication. As well, with partnerships, as in relational exchanges,
relational norms or expectations of behavior are developed over time. The expectation
of continuity and/or the relational norms act as controls against possible opportunistic
behaviors. Trust, commitment, and exchange norms complement more formal
mechanisms, such as detailed contracts.
The exact definition of partnership, however, is not trivial, as can be noted in
the academic literature:
- Mohr and Spekman (1994) define partnerships as “purposive strategic
relationships between independent firms who share compatible goals, strive
for mutual benefit and acknowledge a high level of interdependence” (p. 135);
28
- Gardner et al (1994) have a broader perspective on the concept and consider a
partnership as the “relational contract” in Macneil’s (1980) language; i.e., a
relationship style present within the continuum of interorganizational
relationships from arm’s length to vertical integration;
- Ellram and Hendrick (1995) define partnering as an on-going relationship
between two firms that involves a commitment over an extended time period
with a mutual sharing of information, risks, and rewards from the relationship;
- Lambert et al (1996) classify partnerships into three types. In type I
partnerships, firms recognize each other as partners and coordinate activities
and planning on a limited basis. Type II partnerships are related to firms that
have moved from simply coordinating activities to the integration of activities
with a longer term orientation and involving multiple areas within the firm.
Finally, firms involved in type III partnerships share a significant level of
operational and strategic integration;
- Mentzer et al (2000) distinguish between strategic and operational partnering.
While strategic partnering is an “on-going, long-term interfirm relationship for
achieving strategic goals, which delivers value for customers and profitability
to partners,” operational partnering is an “as-needed, shorter term relationship
for obtaining parity with competitors.” An operational partnering orientation
seeks improvements in operational efficiency and effectiveness;
- Rinehart et al (2004) classify partnerships as a “hybrid” system that is
contained within a range of relational governing systems (from informal
agreements to franchising), and is differentiated from mere activity-based or
29
functional systems for its emphasis on relational characteristics that guide the
actions of parties. They argue that there are key distinguishing attributes
among the types of partnerships in the transaction-relationship-ownership
continuum, such as trust, interaction frequency, and commitment;
- Lambert et al (2004) define a partnership as “a tailored business relationship
based on mutual trust, openness, shared risk and shared rewards that result in
business performance greater than would be achieved by the two firms
working together in the absence of partnership” (p. 22). The key point in this
definition is that the relationship is customized and cannot be uniform for all
customers, since the tailoring process consumes managerial time and effort.
From the definitions above, it can be noted that partnership agreements are
unique and possess elements of relational exchange. Gentry (1996) points out that,
although the definitions differ in the literature, partnerships usually share the common
characteristics of:
- long term commitment;
- open communications and information sharing;
- cooperative, continuous improvements in cost reductions and increased
quality;
- sharing of risks and rewards.
These partnering characteristics are among the main elements that exist in a
relational exchange (Gardner et al 1994). Therefore, “partnership” is an appropriate
and relevant construct to be investigated when studying relational exchanges. From a
3PL’s perspective, investigating the relevant antecedents to a customer’s relational
30
behavior is very useful in that a 3PL may be proactive and focus on these antecedents,
enhancing the relationship and fostering the continuity and success of these
relationships. As Ivens (2004) found in his study with members of a German market
research association, a service provider’s relational behavior exerts considerable
influence on a customer’s economic and social satisfaction.
In this dissertation, Gardner et al’s (1994) broader definition of partnerships is
adopted – “partnering behavior” can exist in different degrees at any point on the
continuum between discrete exchanges and vertical integration (see Figure 1). In their
words, “partnership would be any relationship that falls to the right of the continuum,
beyond arm’s length” (p. 122). More specifically, in this dissertation the dependent
variable is the 3PL customer’s partnering behavior which corresponds to the
customer’s perception that its relationship with a 3PL possesses the following
behavioral elements (Gardner et al 1994, p. 127):
- “planning: integration of the operations of the two firms, smoothing the
disturbances from expected and unexpected environmental factors;
- sharing of benefits and burdens: reflects the willingness of both parties to
accept short-term hardships with the expectation that the opposite party will
do the same. In this way both firms win in the long run;
- extendedness: refers to loyalty and long-term expectations of the two parties
involved;
- systematic operational information exchange refers to the systems designed to
provide accurate, concise, and usable day-to-day information transfers. These
31
systems would include automated and non-automated systems; EDI being a
good example;
- mutual operating controls: reflects each party’s willingness to allow managers
of the other party to have a meaningful say in its operations. The goal would
be to build more efficient total systems and to verify optimal performance.”
Figure 1. Continuum of relationship styles (extracted from Gardner et al 1994)
2.2.2. Distinguishing between partnerships and other
interorganizational relationships
An important point to highlight is the unclear distinction between partnerships
and other forms of relational exchanges, especially alliances. There is no agreement
in the literature as to whether these terms are synonymous or two independent
concepts. Although the distinction between partnerships and other forms of relational
behaviors is beyond the scope of this dissertation, this issue is relevant, given that
both partnerships and alliances are relational governed, hybrid systems, and the
understanding of their drivers and consequences follows the same logic. In addition,
No partnership
present
Range of relationship styles
Arm’s Length
Relationship Style, e.g.,
Commodities Markets
Just Short of
Full Vertical Integration,
e.g., Corporate
Vertical Marketing Systems
Many elements of partnership
present
32
the literature on alliances is also used in this dissertation for insights into the
development of the partnering model.
Few scholars have aimed to clarify the behavioral dimensions of partnerships,
as well as to differentiate partnerships from other relationship types. Gardner et al
(1994) identified the five partnership dimensions presented in the previous subsection
– planning, sharing of benefits and burdens, extendedness, systematic operational
exchange, and mutual operating controls - and tested for partnership as a first-order
factor. Although they were able to discriminate the partnership dimensions, their
sample was too small for statistical significance, and relatively few of the potential
influencing factors had clear, significant correlations with the overall measure of
partnership (thus testing the validity and reliability of a second-order factor for
partnerships is a contribution this research aims to make). Mohr and Spekman (1994)
argued that partnerships possess behavioral attributes, such as commitment and trust,
communication behaviors, and conflict resolution techniques.
Empirically distinguishing partnerships from other interorganizational
relationships has not proven to be an easy task. Rinehart et al (2004), for example,
explored whether different types of business relationships (e.g. non-strategic
transactions, administered relationships, contractual relationship, partnerships, joint
venture and alliances) exhibit different attribute levels (trust, interaction frequency,
and commitment) through cluster analysis and concluded that this issue is more
complex than traditional classifications would predict. The authors expected that
closer relationships would exhibit higher levels of the behavioral attributes, but this
was not found. Strategic alliances, for instance, did not exhibit higher levels of all the
33
behavioral dimensions than partnerships. As well, joint ventures exhibited lower
levels of trust, which might be indicative of why greater investments are required for
joint ventures.
In particular, the distinction between partnerships and alliances is not clear in
the literature. Partnerships are a hybrid governance mechanism in which the
coordinative forces include trust and commitment (in contrast to pure market
transactions, in which price is the coordinative force) (Rese 2006). Indeed, as Mohr
and Spekman (1994) point out, “closer, more intimate bonds are what separate these
partnerships from a more transaction-based set of exchanges which are limited in
scope and purpose.” Alliances on the other hand have been defined as “voluntary
arrangements between firms involving exchange, sharing, or co-development of
products, technologies, or services” (Gulati 1998), or “a form of inter-organizational
cooperation involving pooling of skills and resources to achieve common objectives
of alliance partners, but retaining their separate entities” (Xie and Johnston 2004).
Some researchers do not agree that alliances mean keeping separate entities and
consider joint ventures and contracting agreements (e.g., licensing, distribution etc) as
governance forms of alliances (e.g., Osborn and Baughn 1990). Zineldin and
Bredenlow (2003), for example, argue that strategic alliances encompass agreements
between firms needed to achieve some strategic objective, and can range from a
simple handshake agreement to licensing, outsourcing, and equity joint-ventures.
In many instances, partnerships and alliances terms are considered to be the
same concept (e.g., Gentry and Vellenga 1996, Wong et al 2005). However,
partnerships are often distinguished from alliances. Webster (1992), for example,
34
distinguishes partnerships from long-term relationships in the sense that, in
partnerships, cooperation substitutes for arm’s length and adversarial behaviors that
might exist in long-term relationships. Then, he distinguishes strategic alliances from
partnerships arguing that strategic alliances are an entirely new venture where
partners work towards a long-term, strategic goal. In his opinion, this strategic
objective is one distinguishing feature that separates strategic alliances from other
forms of inter-firm cooperation. Gardner et al (1994) view partnerships as a behavior
style with some behavioral elements/characteristics (planning, sharing benefits and
burdens, extendedness, operational information exchange, and mutual operating
controls). Other types of relationships possess elements of partnerships (alliances,
joint-ventures, small account selling) to a different extent. Gardner et al’s (1994) view
is adopted for this dissertation.
2.2.3. Previous research on the antecedents and outcomes of
partnering
As outlined earlier, partnerships are a hybrid form of inter-organizational
governance, in which relational behavior elements are present, and pure market forces
and prices are no longer the only controlling mechanisms. The objective of this
dissertation is to identify the antecedents of partnering behavior in the context of
logistics outsourcing. For this reason, understanding the drivers of partnering requires
a broad overview of the different research streams that have been used to investigate
the drivers, structures and outcomes of interorganizational relationships in general.
Research on interorganizational relationships has been conducted in the
marketing, strategy and logistics literatures. In all fields, researchers have drawn upon
35
various theories, such as: transaction costs economics (e.g., Osborn and Baughn
1990), resource dependency (e.g., Pfeffer and Salancik 1978, Thompson 1967),
contract law and social exchange theory (Anderson and Narus 1984, Dwyer, Schurr
and Oh 1987, Frazier 1983), and social network theory (Gulati 1998, 2000) to
investigate the drivers and selection of governance structures. Aside from specifying
the behavioral dimensions and attributes of partnerships described in the previous
subsection (Gardner et al, 1994, Ellram and Hendrick, 1995, Rinehart et al, 2004), the
main research questions addressed have been related to: partner selection (Ellram
1990, Rese 2006); partnership antecedents (Oliver 1990, Whipple et al 1996, Gulati
1998); partnership satisfaction (Anderson and Narus 1990, Walton, 1996, Mohr and
Spekman 1994 Lambert et al, 1996, 1999, 2004), and partnership performance
(Kleinsorge et al, 1991, Duffy and Fearne, 2004). As the paragraphs below show, the
research on partnership formation focuses on either environmental,
interorganizational, and firm-specific characteristics. The model proposed in this
dissertation contributes to this literature by combining these three elements.
Partnering motivations and formation. Scholars have identified several
factors that motivate firms to engage in close, collaborative relationships with other
organizations. Mohr and Spekman (1994) argue that partnerships are primarily
motivated to gain competitive advantage in the market place. Whipple et al (1996)
cite cost reduction, performance improvement, operating stability, the desire to
become more customer oriented, and access to the partner’s expertise as motivations
to enter alliances (or partnerships). They argued, however, that the list of potential
motivations to engage in alliances is unlimited in scope and many times specific to
36
the position within the marketing channel. Oliver (1990), based on a comprehensive
review of the interorganizational relationship literature, identified six motivations to
establish a wide range of business-to-business relationships: necessity, asymmetry,
reciprocity, efficiency, stability, and legitimacy. Bucklin and Sengupta (1993) pointed
out that, regardless of the motivations, firms expect significant strategic and/or
operational benefits that accrue from relationships to outweigh the costs of
maintaining them.
Some researchers have focused on modeling the conditions that trigger the
formation and shape the development of interorganizational relationships, such as
partnerships and alliances. A traditional perspective is transaction costs economics
(TCE) that aims to balance transaction and production costs in order to achieve an
economically efficient governance structure (e.g., Osborn and Baughn 1990).
Resource dependence theory examines the role of the external environment in
shaping such decisions. Conversely, the resource-based view focuses primarily on the
existing competence (or lack thereof) that may propel firms to ally with other firms
(e.g., White 2000). A fourth perspective is the network theory, which builds on the
notion that firms’ actions are influenced by the social context in which they are
embedded (Gulati 1998). TCE, resource dependency, resource-based view, and social
network theories are widely used in strategy research. Another very common
theoretical perspective, usually applied by marketing researchers, is social exchange
theory (e.g., Dywer, Schurr and Oh 1987). Social exchange theory (SET) is thus an
appropriate lens to investigate 3PL – customer relationships and is the main
37
theoretical perspective adopted in this dissertation. SET is reviewed in more detail in
the next section.
Many researchers have linked theoretical perspectives. Joshi and Campbell
(2003) investigated the effect of manufacturers’ downstream environmental
dynamism on the relational governance between manufacturers and their suppliers.
They found that in dynamic environments, manufacturers adopt relational governance
with suppliers when a manufacturer’s collaborative belief is high and when a
supplier’s knowledge is high. Izquierdo and Cillan (2004) combined resource
dependency theory, transaction cost economics, and relationship marketing. They
found that trust strengthens the effect of interdependence on the relational exchange
between suppliers and manufacturers in the automotive industry. White and Lui
(2005) distinguished sources of costs of cooperation and control in alliances. They
found that cooperation costs and transaction costs affect the level of time and effort a
manager spends in the alliance. In summary, although different theories focus on
firm, environmental, and inter-organizational factors, all factors seem to play a role in
decisions to build and maintain partnerships.
A common ground among researchers is that no one partnership type is
always appropriate. Zinn and Parasuraman (1997), for example, created a typology
that classifies logistical alliances along the dimensions of scope (broad versus
narrow) and intensity (high versus low). They emphasize that an alliance
characterized by a broad scope is not necessarily better or more effective than one
characterized by a narrow scope. Both broad and narrow scope strategic alliances can
be equally cost effective under appropriate conditions. Indeed, as Lambert and
38
Knemeyer (2004) point out, partnerships are costly to implement and are justified
only if the benefits of a partnership exceed those of not partnering. In a conceptual
piece that explored how, why, and when to establish a wide range of possible B2B
relationships, Cooper and Gardner (1993) suggest that firms should concentrate on
developing good business relationships, which may have varying levels of partnership
characteristics. Considering that partnerships may not be appropriate under all
circumstances, Rese (2006) developed a normative decision model for managers to
evaluate whether partnerships as a coordinative form are really the best choice in
given situations. The decision to partner should be taken based on two criteria: the
degree of standardization/individualization of the product purchased, and the
possibilities to allocate revenue to the several partners in the network.
Partnering outcomes. The effects of partnering on performance and
satisfaction have also been investigated. Duffy and Fearne (2004), using a sample of
UK retailers and fresh produce suppliers, found a positive effect of main partnership
dimensions on supplier performance (measured by future growth and current costs
and sales). Walton (1996) found a positive relationship between the five partnership
dimensions of planning, sharing benefits and burdens, interdependence, operational
information exchange and extendedness, and partnership satisfaction. Mohr and
Spekman (1994) showed that partnerships attributes (e.g., commitment, coordination,
interdependence, trust), communication behavior and conflict resolution techniques
do affect partnership success in terms of partner satisfaction and increases in sales.
Gentry and Vellenga (1996), in a conceptual paper, propose that logistics alliances are
a source of competitive advantage in the marketplace in that this allows for access to
39
superior skills and resources. Jonsson and Zineldin (2003) proposed a conceptual
model of dealer satisfaction in long-term working relationships between suppliers and
dealers. They found that reputation and close ties are key elements to achieving
satisfactory relationships when trust and commitment are high, and that it is possible
to achieve satisfactory relationships even if trust and commitment are lacking.
Research has also focused on the development of models that identify the
factors that influence partnership formation and management and provide guidelines
for managers to successfully implement partnerships. Lambert, Emmelhainz and
Gardner’s (1996, 1999) model, for example, provides managers with a series of steps
to be followed in order to identify the drivers, the components, or the activities of the
potential partnership, performance measures, etc. Tuten and Urban (2001) identified
three main factors that make a partnership successful: improved communication in
terms of frequency, characteristics of strong relationships (e.g. trust, reliability,
honesty and fairness), and satisfactory performance indicators (e.g. profitability,
market share, sales) in line with expectations. In a recent article, Lambert, Knemeyer
and Gardner (2004) validated Lambert, Emmenhainz and Gardner’s model based on a
facilitation of 20 partnerships cases.
2.3. Relationship Marketing
This subsection introduces the concept of relationship marketing, and its
theoretical foundations, with a focus on social exchange theory. A brief overview of
the extant literature related to business-to-business exchange is presented. Finally, the
concept of relationship marketing orientation, one of the main constructs of the
proposed model for this dissertation, is discussed.
40
2.3.1. Relationship marketing defined
Although considered by some as a mere restatement of the marketing concept,
thus “redundant and unnecessary” (Gruen 1997), relationship marketing has
undeniably become a “hot topic discipline” (Möller and Halinen 2000), and has been
referred to as “a major shift in marketing theory and practice” (Rao and Perry 2002).
This shift is based on the fact that in the relationship marketing philosophy, the
relationship between buyers and sellers becomes the core of the firm’s operational
and strategic thinking (Tse and Sin 2004). This view is different from transactional
marketing, where the customer remains faceless, and future interactions between
buyers and sellers are not a major concern. Indeed, some researchers believe that
relationship marketing is the opposite of transactional marketing (Rao and Perry
2002).
A comprehensive definition of relationship marketing is provided by Morgan
and Hunt (1994): “Relationship marketing refers to all marketing activities directed
towards establishing, developing, and maintaining successful relationship exchanges”
(p. 22). Although many other definitions of relationship marketing exist in the
literature, recent articles have often followed Harker (1999) who identified as many
as seven conceptual categories and 26 definitions of relationship marketing, arrived at
the following definition: “An organization engaged in proactively creating,
developing and maintaining committed, interactive and profitable exchanges with
selected customers [partners] over time is engaged in relationship marketing”(Harker
1999, p. 16). Note that the word “partners” indicate that the objectives of relationship
marketing are to build, maintain, and when necessary, terminate relationships not
41
only with customers, but with stakeholders as well; i.e., suppliers, partners, and even
competitors (Rao and Perry 2002).
Morgan and Hunt (1994) explain that in order to fully understand the nature of
relationship marketing, the first step is to distinguish between a transactional
exchange and a relational exchange. A discrete transaction involves a single, short-
time exchange, and has a sharp beginning and ending. A relational exchange,
however, encompasses multiple exchanges and usually involves both economic and
social bonds (Rao and Perry 2002). To illustrate the broad range of possible forms of
relationship marketing, Morgan and Hunt (1994) present ten examples: the partnering
involved in relational exchanges between manufacturers and their goods suppliers, as
in JIT procurement; relational exchanges with service providers; strategic alliances
between firms and their “competitors”; co-marketing alliances and global strategic
alliances; alliances with nonprofit organizations; partnerships for joint development;
long-term exchanges with ultimate customers; relational exchanges with working
partners, as in channels of distribution; exchanges involving functional departments;
exchanges between a firm and its employees; within firm exchanges such as among
subsidiaries or business units.
The central idea underlying the relationship marketing concept is, therefore, to
build and nurture lasting and mutually beneficial relationships (Hewett and Bearden
2001). The expected benefit of systematically developing cooperative and
collaborative partnerships is the decrease in exchange uncertainty through customer
collaboration and commitment (Andersen 2002). As a consequence, a higher share of
each customer’s lifetime business is attained (Gruen 1997). This notion was born
42
from the fact that organizations have realized that in today’s competitive
environment, firms need to collaborate in order to compete (Perlmutter and Heenan
1986). Interdependence and cooperation become, therefore, efficient tools to create
value and achieve sustainable competitive advantage (Gruen 1997).
2.3.2. Theoretical foundations of relationship marketing
An important ongoing debate amongst marketing researchers is related to the
scope and theoretical foundations of relationship marketing. Some articles have
discussed the theoretical roots and future directions of the relationship marketing
discipline (e.g., Möller and Halinen 2000, Rao and Perry 2002). Möller and Halinen
(2000), for example, argue that a theory of relationship marketing has not been
developed yet, but only a “variety of partial descriptions and theories focusing on the
broad content of the phenomena researchers have labeled relationship marketing” (p.
34). Indeed, the academic background of relationship marketing contributors is
extremely diverse (Harker 1999). For some researchers, however, this combination of
seemingly unrelated strands of marketing thought makes relationship marketing an
attractive concept and can become, in fact, its biggest strength (Harker 1999, Zinkhan
2002).
There is no agreement on the classification of the various relationship
marketing schools of thought (for examples see Zinkhan 2002, Rao and Perry 2002,
Möller and Halinen 2000). One common ground, however, is that the two major
disciplinary roots of relationship marketing are the Nordic school (Gummerson et al
1997) focusing on services marketing, and the industrial marketing school developed
43
by the international marketing and purchasing group (IMP). The service marketing
school focuses on explaining the management of services with special attention to the
relationship between the consumer and the personnel that provide the service. The
major questions investigated are the management of service encounters and service
quality (e.g, Parasuraman, Zeithman and Berry 1985). The industrial marketing
(marketing channels) school focuses on explaining governance structures and the
modeling of socio-economic behaviors of channel members and draws on socio-
economic theories (Spekman and Carraway 2006). Aside from the service and
industrial marketing schools, database marketing and the network approach are also
cited as strands of thought in the relationship marketing discipline (Möller and
Halinen 2000). Another research stream comes from the work on market-oriented
organizations, in which the culture of the firm places the customer as a primary
stakeholder (e.g., Narver and Slater 1990). Given the broad scope of relationship
marketing studies, a comprehensive literature review of all these schools of thought is
beyond the scope of this dissertation. Therefore, this section focuses on the
application of relationship marketing to business-to-business relationship formation
and development.
A common topic examined in relationship marketing is the effect of
characteristics of exchange relationships (e.g., trust, dependence) on outcomes (e.g.,
retention, referrals) that represent desired behaviors on the part of one or more of the
partners in the exchange (Hewett and Bearden 2001). Other studies, however, focus
on identifying the antecedents of relational behavior, such as trust (e.g., Morgan and
Hunt 1994) and long-term orientation (e.g., Ganesan 1994). In addition, many
44
marketing scholars have developed models in order to explain the development of
relationships between exchange partners (e.g., Dwyer, Schurr and Oh 1987). They are
usually process models that suggest that relationships that facilitate relational
exchanges develop in stages through exchange interactions over time. During the
interactions, trustworthiness of suppliers and buyers are tested and norms of behavior
are developed (Andersen 2002). These models are typically composed of phases that
involve initiation, maintenance and termination (Dwyer, Schurr and Oh 1987, Frazier
1983).
The studies described above have drawn on a variety of theories (Harker
1999), including interorganizational theory (van de Ven 1992, Reve and Stern 1979),
transaction-cost economics, resource dependency theory, and industrial network
theory (Larson 1992, Johanson and Mattson 1987). However, one of the earliest
approaches is social exchange theory (SET), which is the theoretical basis for this
dissertation. For this reason, the next subsection presents a brief description of social
exchange theory and provides a literature review on the development of relationships,
especially from a SET perspective.
2.3.3. A brief introduction to social exchange theory
Marketing scholars have relied widely on social exchange theory (hereafter,
SET) to explain relational governance in business-to-business relational exchanges.
SET focuses on the relationship between partners, and advocates that relational
control in the form of personal relations can be an effective means of governance.
This is opposed to early research that focused solely on power and dependence
45
(Lambe et al 2001). This governance mechanism is built on the foundation of trust,
commitment, and exchange norms that replace or complement more formal
governance mechanisms, such as detailed contracts. In SET, the relationship is the
unit of analysis and the key to relational exchange success.
Continuous interactions are said to build a relationship in stages. Anderson
(1995), for example, explains that relationship development is experienced as a series
of exchange episodes. Each exchange episode is composed of four events: defining
the purpose of a relationship, setting relationship boundaries, creating relationship
value, and evaluating exchange outcomes. Dwyer, Schurr and Oh (1987) stress the
evolution of exchange relationships and propose that relationships develop through
five phases, including awareness, exploration, expansion, commitment, and
dissolution.
According to SET, firms engage in and maintain relationships because they
expect that doing so will be rewarding (Blau 1964). Therefore, parties will remain in
a relationship as long as the parties judge the relationship satisfactory (in other words,
that the benefits of the relationship outweigh the costs). SET acknowledges that these
rewards may come in various forms, such as: economic, information, product or
service, and social rewards (such as emotional satisfaction, view sharing, etc). These
rewards are acquired through a history of interactions; the relationship being the lens
through which firms anticipate future costs and benefits. If previous experiences have
been positive, SET assumes that firms will expect future interactions to have positive
outcomes as well.
46
From a SET perspective, in order to assess whether rewards (i.e., benefits
minus costs) are satisfactory, social and economic outcomes are compared to two
standards that may vary from party to party (Thibaut and Kelley 1959): the benefit
standard one feels is deserved in a given kind of relationship – the comparison level
CL; and the overall benefit that one believes can be obtained from the best possible
alternative exchange relationship – the comparison level of alternatives CL
alt
. Note
that the comparison level CL is based upon present and past experiences with similar
relationships, and knowledge of other firms’ relationships (Anderson and Narus
1984). In other words, firms evaluate the economic and social outcomes from each
transaction and compare them to the level it is felt that the firm deserves (i.e., CL) as
well as to the level of benefits provided by other potential exchange partners (i.e.,
CL
alt
). If the outcomes level is above of what the firm believes is deserved (i.e., CL),
some degree of satisfaction will occur. If rewards acquired from a given exchange
relationship exceed CL
alt
, Thibaut and Kelley (1959) suggest that the party will have a
degree of dependence on the relationship. SET also suggests that, if positive
outcomes (that exceed CL and CL
alt
) and reciprocal beneficial actions occur, trust is
built over time and the process of creating trust creates social obligations. Therefore,
trust contributes significantly to the level of partner commitment to the relationship.
Aside from the creation of trust, with continuous interactions, explicitly and/or tacitly
determined rules of behavior, or relational exchange norms, are created. Relational
exchange norms are very important because they increase the efficiency of a
relationship and reduce the degree of uncertainty.
47
In a nutshell, the above paragraphs describe the four premises of social
exchange theory (Lambe et al 2001, p. 6): “1) exchange interactions result in
economic and/or social outcomes; 2) these outcomes are compared over time to other
exchange alternatives to determine the dependence on the exchange relationship; 3)
positive outcomes over time increase firms’ trust of their trading partner(s) and their
commitment to the exchange relationship; and 4) positive exchange interactions over
time produce relational exchange norms that govern the exchange relationship.”
2.3.4. Relationship marketing literature focused on business-to-
business relational exchange
There is a substantial body of research on business-to-business relational
exchange that uses and operationalizes SET (for a review, see Lambe et al 2001).
This research can be divided into two groups (Lambe et al 2001). The first group has
examined how antecedents contribute to a business-to-business exchange (Ganesan
1994, Morgan and Hunt 1994, Anderson and Weitz 1992, Frazier 1983, Dwyer
Schurr and Oh 1987). In this case, the dependent variable is the degree to which the
exchange is relational and the independent variables are derived from SET’s other
fundamental premises: economic/social outcomes from interactions, and
trust/commitment. The second group has investigated the outcomes or benefits of
relational exchanges (Anderson and Narus 1984, 1990, Bucklin and Sengupta 1993).
As a general observation, dependence and trust are commonly found to influence
relational behavior, and a positive effect of relational behavior on outcomes, such as
satisfaction and performance, is consistently found.
48
As mentioned above, researchers have investigated the antecedents of
relational behavior and the factors that have most importance in explaining relational
exchange. Anderson and Weitz (1992), for example, modeled commitment in
distribution channel relationships as a function of (1) each party’s perception of the
other party’s commitment, (2) self-reported and perceived pledges (idiosyncratic
investments and contractual terms) made by each party, and (3) other factors, such as
communication level, reputation and relationship history. Transaction-specific
investments and contractual terms (constraining contractual clauses; e.g., territorial
exclusivity, exclusive dealing, limit termination if some performance is not achieved)
function as important pledges to build and sustain commitment, affecting each party’s
perceptions of the other party’s commitment. Morgan and Hunt (1994), in their
seminal “commitment-trust theory” paper, showed that trust and commitment are key
mediating variables in explaining important relationship marketing outcomes. More
specifically, trust and commitment have a positive effect on acquiescence (degree to
which a partner accepts or adheres to another’s specific requests or policies) and
cooperation, while having a negative effect on the propensity to leave a relationship,
functional conflict, and decision-making uncertainty. Interestingly, it has been shown
that personal characteristics and the experience with an exchange partner also play
roles in relational behavior. Coulter and Coulter (2002), for example, showed that
person-related (e.g., empathy, politeness) and offer-related (customization, reliability)
service representative characteristics have an impact on trust, moderated by the length
of the relationship. They found that person-related service provider characteristics
had a greater effect on trust when customers are in the early stages of a particular
49
service relationship. As customers gained more direct product experience,
competence became more important. Izquierdo and Cillán (2004), in the context of
supplier-manufacturer relationships in the automobile industry, found that trust
enhances the effect of interdependence on the relational orientation of the exchange.
Other researchers have focused on the effects of relational behavior on
specific marketing outcomes, such as satisfaction or performance. Bucklin and
Sengupta (1993) developed a model of successful co-marketing alliances, which are
relationships between firms at the same level in the value chain, and found that a
history of interactions between partners increase the effectiveness (what they called
success) of the relationship. Moreover, reducing power and managerial imbalances
can foster gains in effectiveness as well. Smith and Barclay (1997) tested the effects
of organizational differences and trust on the effectiveness of selling partner
relationships. Their model showed that key organizational differences, mutual
perceived trustworthiness, and mutual trusting behaviors, all help explain perceived
task performance and mutual satisfaction. Hewett and Bearden (2001) developed a
model of success in relationships between foreign subsidiaries and headquarters
marketing operations. In their study, trust and dependence are modeled as antecedents
of relational behaviors (acquiescence and cooperation). In line with Smith and
Barclay’s (1997) findings, their results show that cooperative behaviors are positively
associated with product performance (index function of profitability, sales and market
share) in the subsidiaries’ markets. Anderson and Narus (1984) developed a model of
the distributor’s perspective of distributor-manufacturer relationships and found
support for SET premises. They found that distributors that perceived higher levels of
50
outcomes given CL
alt
perceived lower levels of manufacturer control. Manufacturer
control was found to be negatively related to distributor cooperation/satisfaction.
Also, outcomes given CL positively affected distributor cooperation/satisfaction. In a
later article, Anderson and Narus (1990) found that outcomes given CL, relative
dependence, and communication are critical constructs in the explanation of “on-
going” manufacturer and distributor working partnerships.
In addition, other researchers, such as Frazier (1983) and Dwyer, Schurr and
Oh (1987), conceptualize the process of exchange behavior between organizations
within marketing channels. As outlined above, these are process models in which the
events occur in stages. Frazier (1983)’s framework, for example, includes processes
of initiation, implementation, and review. His model also suggests that one source of
power is based on dependence. A series of interactions occurs between firms during
an exchange. Cooperation is high when communication is effective and participative
decision making occurs. Satisfaction is influenced by a variety of social and
economic factors. Dwyer, Schurr and Oh (1987) also propose a framework to
describe the development of exchange relationships, drawing a parallel with a marital
relationship model. They propose that relationships evolve in five general phases
identified as (1) awareness, (2) exploration, (3) expansion, (4) commitment, and (5)
dissolution. Each phase represents a major transition in how parties regard one
another.
The objective of this dissertation is to identify the antecedents of partnering
from a relationship marketing perspective. In the literature, Ganesan’s (1994)
“determinants of long-term orientation” model incorporates the major variables
51
(dependence, trust, satisfaction, and their antecedents) considered in relationship
marketing research. This dissertation applies and expands Ganesan’s model to the
context of logistics outsourcing relationships. A detailed description of Ganesan’s
model is presented in the next section.
2.3.5. Relationship marketing orientation
The objective of relationship marketing is to attract and develop mutually
beneficial, profitable exchanges with customers and other stakeholders (Harker
1999). In order to achieve this objective, scholars have argued that the relationship
marketing concept has to be incorporated into the organization’s culture and values,
placing the buyer-seller relationship “at the center of the firm’s strategic or
operational thinking” (Tse and Sin 2004). As Day (2000) pointed out, in order to
continually attract and keep customers, a relationship orientation must be immersed in
the mind-set, values, and norms of the organization. Following this logic, relationship
marketing scholars have recently developed the concept of relationship marketing
orientation – RMO (Tse and Sin 2004, Sin et al 2005 a, b), which captures the
behaviors and activities dedicated to relational exchange processes.
Although relational behavior is the core of the relationship marketing
discipline, RMO is a fairly new concept. In the marketing literature, the traditional
construct that captures a firm’s marketing behavior has been the market orientation
(MO) construct, which is defined as the “organizational culture that most effectively
and efficiently creates the necessary behaviors for the creation of superior value for
buyers and, thus, continuous superior performance for the business” (Narver and
52
Slater 1990). MO is composed of three behavioral dimensions - competitor
orientation, customer orientation and interfunctional coordination – and two decision
criteria – long-term focus and profitability. Although some research has highlighted
the positive relationship between MO and relational norms (e.g., Siguaw et al 1998),
Helfert et al (2002) were among the first to argue explicitly that the concept of MO
should be explored with particular focus on inter-organizational relationships. This
should occur since market oriented firms focus on understanding customer needs and
are willing to commit themselves to customers. Moreover, market oriented firms are
likely to provide financial, physical, and technical resources for relationships as they
value these relationships as a source of information generation and dissemination.
Although researchers in the service and industrial marketing schools have
indicated that relationship marketing has a positive effect on firm performance, very
limited empirical research has formally measured the RMO construct. Sin et al
(2005b), however, developed and validated a scale with six components – bonding,
communication, shared value, empathy, reciprocity, and trust – and found a positive
relationship between RMO and firm performance. In a second study, Sin et al (2005a)
investigated the moderating role of economic ideology and industry type in the
relationship between RMO and firm performance. They tested and found a positive
relationship between RMO and performance in two models: one for Hong Kong, and
another for Mainland China. RMO was found to be a stronger predictor in the service
sector in China, and in the manufacturing sector in Hong Kong. Tse and Sin (2004)
showed that the effects of RMO on performance are contingent on the competitive
53
strategic type of organizations. Also, the effect is stronger for market followers and
market “nichers” than for market leaders.
In the context of logistics outsourcing, investigating whether buyers of
logistics services engage in a relationship marketing philosophy is important to 3PLs
in that 3PLs can better select a marketing strategy to be employed with that specific
customer. As Day (2000) notes, some customers only want the timely exchange of
products or services with a minimum of hassles. Therefore spending resources and
effort on attempting to develop a relationship with these customers is not worthwhile.
This fact was observed in Garbarino and Johnson’s (1999) study with the customer
base of a nonprofit professional theater company. They demonstrated that the
decision to employ relational or transactional marketing should depend on the
relational orientation of the customer. For low relational customers (individual ticket
buyers and occasional subscribers), overall satisfaction is the primary mediating
construct between the customer attitudes towards the actors and the play and future
intentions of attending and subscribing to the theater. For the high relational
customers (consistent subscribers), trust and commitment, rather than satisfaction, are
the mediators between customer attitudes and future intentions. Therefore, the extent
to which 3PL customers engage in relationship marketing is an important
consideration when investigating a customer’s propensity to engage in partnerships
with their 3PL providers.
2.4. Ganesan’s (1994) model of long-term orientation
Since this dissertation builds upon Ganesan’s (1994) model of long-term
orientation in retail buyer – vendor relationships, and tests the model in the context of
54
logistics outsourcing, an overview of Ganesan’s model is appropriate. This section
briefly describes the model, its main variables and its hypothesized relationships. The
rationale for each of his propositions is described in detail in Chapter 3, along with
the propositions for this study.
The model. Ganesan, based on the premises of relationship marketing,
developed and tested the antecedents of long-term orientation in retail buyer – vendor
relationships. A special feature of his research is that he tested both vendor and
retailer perspectives, and was thus able to identify commonalities and differences
between the two groups. Note that since this study investigates the partnering
behavior from the 3PL customer’s perspective (i.e. the buyer of the service), the
discussion and analysis of Ganesan’s model in this section is from the buyer’s (i.e.,
the retailer’s) perspective.
Ganesan defined a retailer’s long-term orientation as the “perception of
interdependence of outcomes in which both a vendor’s outcomes and joint outcomes
are expected to benefit the retailer in the long run.” This means that while retailers
with short-term orientation are concerned with the outcomes of the current period,
retailers with long-term orientation are concerned with both current and future
outcomes, while emphasizing future conditions. However, Ganesan pointed out that
none of the orientations is altruistic, but focus on maximizing the outcomes obtained
through the channel. Retailer’s long-term orientation was modeled as a function of
two main elements: dependence and reliance on trust (see Figure 2). More
specifically, perceived dependence of a retailer on a vendor and retailer’s trust in a
vendor, are both positively associated with a retailer’s long-term orientation. In
55
addition, a retailer’s satisfaction with previous outcomes was hypothesized to have a
direct effect on retailer’s long-term orientation.
Figure 2. Ganesan’s (1994) model
The antecedents of a retailer’s dependence on a vendor are: the environmental
diversity and volatility in the market of the product that the retailer buys from the
vendor, as well as transaction specific investments
5
by both firms. Environmental
volatility, which is related to the extent that there are rapid fluctuations in demand
and inability to predict trends, was hypothesized to be positively related to a retailer’s
dependence on a vendor. Conversely, environmental diversity, which is related to the
5
Investments in tangible and intangible assets that are specific to the relationship and that have little
salvage value in case the relationship is terminated.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
56
presence of multiple competitors, products, etc., was hypothesized to be negatively
related to a retailer’s dependence on a vendor. Transaction-specific investments to the
relationship, when made by the retailer, were hypothesized to increase the retailer’s
dependence on a vendor, whereas investments made by the vendor were hypothesized
to have the opposite effect.
Ganesan (1994) operationalized trust with two components: credibility and
benevolence. Vendor’s credibility is related to the belief that the vendor is reliable,
whereas benevolence is related to the intentions and motivations of the vendor when
unanticipated circumstances arise. The antecedents of trust were modeled as
transaction specific investments undertaken by the vendor, reputation of the vendor,
retailer’s experience with the vendor, and retailer’s satisfaction with previous
outcomes of the relationship. All these elements were hypothesized to increase the
perceived trust of the retailer in a vendor.
Ganesan’s hypotheses are the following:
“H1: Trust in a vendor’s credibility and benevolence is positively related to retailer’s
long-term orientation.
H2: Dependence of a retailer on a vendor is positively related to the retailer’s long-
term orientation.
H3: Perceived dependence of a vendor on a retailer is negatively related to the
retailer’s long-term orientation.
H4: A retailer’s satisfaction with past outcomes is positively related to the retailer’s
long-term orientation.
H5: Reputation of a vendor is positively related to the retailer’s perception of
vendor’s credibility.
H6: A retailer’s satisfaction with past outcomes is positively related to the retailer’s
perception of a vendor’s benevolence and credibility.
57
H7: A retailer’s experience with a vendor is positively related to the retailer’s
perception of the vendor’s benevolence and credibility.
H8: A retailer’s perception of vendor TSIs is positively related to the retailer’s
perception of the vendor’s benevolence and credibility.
H9: Environmental volatility is positively related to a retailer’s dependence on a
vendor.
H10: Environmental diversity is negatively related to a retailer’s dependence on a
vendor.
H11: Retailer’s TSIs are positively related to a retailer’s dependence on a vendor
and negatively related to the retailer’s perception of the vendor’s dependence on the
retailer.
H12: A retailer’s perception of vendor’s TSIs is negatively related to a retailer’s
dependence on a vendor and positively related to the retailer’s perception of the
vendor’s dependence on a retailer.”
Ganesan’s data were obtained from two separate surveys. First, he mailed a
survey to retail buyers, who were asked to choose a specific vendor and respond to
questions about their relationships with those vendors. Then, a second questionnaire
was sent to the vendors indicated by the respondent retailers who were asked about
their relationships with the retailers. In his sample, the vendors represented a variety
of product lines, some of which had many competitors, others of which had few
competitors. The retailers, thus, had various levels of dependence on the selected
vendors, and vice-versa.
Results. Ganesan obtained excellent support for the primary antecedents of
retailer’s long-term orientation. He found that dependence, trust (credibility and
benevolence), and satisfaction have an impact on a retailer’s long-term orientation of
a relationship. Figure 3, below, depicts the results of the model. The overall model fit
was good (?2 = 39.95. df = 31). All the five factors hypothesized to affect the
58
retailer’s long-term orientation were significant except one: a retailer’s perception of
vendor benevolence. More specifically, dependence of a retailer on a vendor was
positively related to a retailer’s long-term orientation, and a retailer’s perception of a
vendor’s dependence is negatively related to a retailer’s long term orientation. A
retailer’s perception of the vendor’s credibility was positively associated with a
retailer’s long-term orientation. A retailer’s satisfaction with previous outcomes was
positively related to the retailer’s long term orientation. These four variables
explained 75.2% of the variance associated with a retailer’s long-term orientation.
The dependence of a retailer on a vendor was influenced by the availability of
alternative vendors (environmental diversity) and the retailer’s TSI in the
relationship. Vendor TSI was a main predictor of retailer’s trust in a vendor’s
credibility and benevolence. A retailer’s satisfaction with a vendor and a retailer’s
experience with a vendor were not significant predictors of a vendor’s credibility or
benevolence.
It is important to point out that Ganesan also tested all the antecedents of trust
and dependence in his model for their indirect effects on a retailer’s long-term
orientation. None of the indirect effects were significant, suggesting that the effects of
the independent variables on long-term orientation were mediated through the
dependence and trust constructs.
59
Figure 3. Ganesan’s (1994) model with results
With respect to the hypotheses related to the antecedents of trust, the vendor’s
reputation had a positive effect on vendor’s credibility, but not on benevolence,
supporting H5. H8 was also fully supported; i.e., a retailer’s perceptions of
transaction specific investments by a vendor affect the retailer’s perception of a
vendor’s credibility and benevolence. H7 was not supported; i.e., no effect was found
for the retailer’s experience with the vendor on the vendor’s credibility and
benevolence. Similarly, no significant relationship was found between satisfaction
with previous outcomes and trust.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
.112
.297*
.419*
-.266*
.323*
.442*
.433*
.755*
-.49
.039
-.135 .755
-.324*
.119
.397*
.257*
.588*
* Statistically significant p < .01
60
Mixed results were found for the hypotheses related to the antecedents of
dependence. Environmental diversity had a significant, negative effect on retailer’s
dependence, providing support for the hypothesis, H10, whereas environmental
volatility did not have a significant effect on a retailer’s dependence on a vendor, as
hypothesized in H9. Retailer’s transaction specific investments had a significant,
positive effect on the retailer’s dependence on the vendor, and on a retailer’s
perception of vendor’s dependence, providing partial support for H11. Finally,
perception of a vendor’s TSIs also had a significant, positive effect on a retailer’s
dependence on the vendor and on a retailer’s perception of a vendor’s dependence,
providing partial support for H12. Table 1, below, summarizes the findings.
Table 1. Summary of the findings of Ganesan’s (1994) model
Hypotheses (Retailer’s perspective) Result
Vendor’s credibility ? retailer’s long-term orientation Supported
Vendor’s benevolence ? retailer’s long-term orientation Not supported
Dependence of a retailer on a vendor ? retailer’s long-term
orientation
Supported
Perceived dependence of a vendor on a retailer ? retailer’s long-
term orientation
Supported
Retailer’s satisfaction with previous outcomes ? retailer’s long
term orientation
Supported
Reputation of a vendor ? vendor’s credibility Supported
Retailer’s satisfaction with past outcomes ? vendor’s
benevolence
Not supported
Retailer’s satisfaction with past outcomes ? vendor’s credibility Not supported
Retailer’s experience with a vendor ? vendor’s benevolence Not supported
Retailer’s experience with a vendor ? vendor’s credibility Not supported
Retailer’s perception of vendor TSI ? vendor’s benevolence Supported
Retailer’s perception of vendor TSI ? vendor’s credibility Supported
Environmental volatility ? retailer’s dependence Not supported
Environmental diversity ? retailer’s dependence Supported
61
Table 1 (cont.) Summary of the findings of Ganesan’s (1994) model
Hypotheses (Retailer’s perspective) Result
Retailer’s TSIs ? retailer’s dependence Supported
Retailer’s TSI ? retailer’s perception of vendor’s dependence Not-supported
Retailer’s perception of vendor TSI ? retailer’s dependence Not-supported
Retailer’s perception of vendor TSI ? retailer’s perception of
vendor’s dependence
Supported
Research that extends Ganesan’s. The results achieved by Ganesan (1994)
demonstrate the relevance of the predictors in explaining the perceived long-term
orientation of exchange partners. Although numerous scholars have referred to
Ganesan (1994) to support their hypotheses development, to the best of this author’s
knowledge, no research has explicitly replicated, or expanded Ganesan’s model.
However, subsequent research has focused on other facets of long-term orientation,
such as: the effect of long-term orientation on performance (e.g., Kalwani and
Narayandas 1995), the effect of long-term orientation on other relational behavior
characteristics (Lusch and Brown 1996), other antecedents of long-term orientation
(Schultz and Good 2000), and the potential negative impacts of long-term
relationships (e.g., Grayson and Ambler 1999).
Additional explanatory variables have been used as predictors of long-term
orientation, such as procedural and distributive justice (Griffith et al 2006) and
customer orientation of the seller (Schultz and Good 2000). Based on social exchange
theory, Griffith et al (2006) showed that a distributor’s perception of a supplier’s
procedural and distributive justice in its policies enhanced the distributor’s long-term
orientation and relational behaviors. In Schultz and Good’s (2000) model, a seller’s
orientation towards its customer was a predictor of long-term orientation. In this case,
62
interorganizational factors (i.e., trust, dependence) were not included in the model.
One of the contributions of the present dissertation will be to test these two factors
together. In addition, the present dissertation differs from previous models in that it
tests the effect of a relational orientation of the 3PL customer with its own customers
on the partnering behavior with a third party service provider.
The effect of long-term orientation on performance has also been investigated.
Kalwani and Narayandas (1995), for example, showed that long-term orientation with
select customers achieves higher profitability, but the same sales growth as does a
transactional approach to servicing customers. Lusch and Brown (1996) consider
long-term orientation as a mediator between dependence, type of contracts, and
relational behavior (i.e., flexibility, information exchange, solidarity). All of these
variables were found to have a positive impact on performance. The downside of
long-term relationships has also been discussed. For example, Grayson and Ambler
(1999) found that the dynamics of shorter relationships are different than those of
longer relationships. For example, the effect of trust on commitment was found to be
more important in earlier phases of a relationship. As well, in longer relationships,
rising expectations and perception of loss of objectivity might occur, leading to
dissatisfaction. Similar results were found by Claycomb and Frankwick (2005).
Industrial buyers perceive the costs of maintaining relationships with key suppliers
differently in the various relationship development phases. For example, information
search costs about suppliers are higher in the early stages of a relationship. Buyer
uncertainty is reduced over time, but human interaction costs increase substantially.
63
2.5. Cultural differences and logistics outsourcing
As noted in the previous section, most tests of theory-based models in
logistics outsourcing have been conducted with firms in the U.S (e.g. Knemeyer
2003, 2004, 2005, Stank et al 2003). The studies that investigated logistics
outsourcing in other countries have primarily relied on case studies and exploratory
surveys to depict the reality of logistics outsourcing in those countries, such as
Australia (Sohal, Millen, and Moss, 2002), Singapore (Sum and Teo 1999), New
Zeland (Sankaran, Mun, Charman, 2002), India (Sahay and Mohan 2006), and Saudi
Arabia (Sohail, Sadiq, and Obaid 2005). These articles focus on describing current
logistics outsourcing practices undertaken in these countries and identifying future
trends. The general claim is that logistics outsourcing practices are more developed in
the U.S. and Western Europe than in developing countries. This claim also holds for
Brazil, where the sample firms for this study are located. Indeed, as noted in the
Booz-Allen report discussed earlier (COPPEAD and Booz-Allen 2001), logistics
outsourcing is a recent trend in Brazil, given that the majority of firms still focus on
short-term, arm’s length relationships with 3PLs.
Given that most studies reinforce the notion that logistics outsourcing
relationships differ between U.S. and developing countries, a fair concern is that the
findings from this dissertation, that are drawn from a survey of Brazilian firms, may
not be generalizable to 3PL relationships in developed countries, such as the U.S. In
other words, the question is whether the relationships among the constructs proposed
here (e.g., trust, dependence, and partnering behavior) can be directly comparable to
the findings of studies conducted with U.S. firms.
64
Cultural differences and interorganizational relationships. As Anderson and
Weitz (1989) state, differences in cultures influence the nature of interorganizational
interactions. A sizable work on cross-cultural differences follows this logic and has
focused on how culture can shape firms strategies. A seminal piece is the work of
Hofstede (2001) who surveyed over 88,000 employees from more than 40 countries
and identified four dimensions upon cultures vary: 1) Power distance, which assesses
human inequalities of prestige, wealth, and power, 2) Uncertainty avoidance, which
indicates how people feel threatened by uncertainties or unknown situations, thus
preferring stability and rule orientation, 3) Individualism, which assesses how cultures
emphasize individuality versus collectivity, 4) Masculinity, which assesses the
importance cultures place on careers and money as opposed to social goals, such as
relationships or protection of the physical environment. Comparing Brazil and the
United States, for example, Brazil has higher scores of power distance and
uncertainty avoidance than the U.S., but much lower scores of individualism. Various
studies have validated these dimensions of cultural differences or have used them to
identify differences in business practices.
Other studies focus on cross-cultural comparisons and find that culture does
indeed play a role in the way business is conducted. In the context of headquarter –
subsidiaries relationships, Hewett and Bearden (2001) found that individualism
moderates the relationship between trust and cooperation. In other words, trust has a
stronger effect on cooperation in cultures with higher levels of collectivism. Kogut
and Singh (1988) found that the foreign direct investment (FDI) mode was influenced
by the cultural distance between the home country of the entering firm and the host
65
country. Lin and Germain (1998) found support for the contention that cultural
similarities affect joint venture performance.
One study that partially contradicts previous findings is Morris (2005). He
replicated a model previously tested with American firms, the seminal KMV – “Key
Mediating Variable” model of Morgan and Hunt (1995) that identifies the antecedents
and consequences of trust. In Morgan and Hunt’s (1995) study, the respondents were
retailers from the tire industry who normally do business with domestic suppliers.
Morris (2005), on the other hand, surveyed a sample of U.S. purchasers in different
industries that procure from international suppliers. He found an overall agreement
with Morgan and Hunt’s findings. Morris (2005) also calculated the cultural distance
between the purchasers and customers in the sample and tested two models: a
culturally distant sample (composed of firms that procured from culturally distant
countries), and a culturally similar sample (composed of firms that procured from
culturally similar countries). Interestingly, he found that the relationships in the
model were very similar for the two samples, implying that cultural differences did
not impact the general relationships between the constructs.
Therefore, there is mixed evidence as to how generalizable the findings from
one country can be applied to another country, given the cultural differences between
them. On the one hand, it should be noted that the theoretical bases for this
dissertation were developed by socio-psychologists and with no mention of potential
cultural issues in the development of their hypotheses. On the other hand, certain
studies have indicated that relationships between variables are intensified or
weakened in the presence of different cultural traits. As the work of Hofstede (2001)
66
shows, for example, the U.S. has a higher individualistic culture as opposed to Brazil,
which is more collectivistic. It might be the case that these differences in culture can
affect the results of this dissertation.
In conclusion, as with any other empirical work, the results from this
dissertation should be replicated in other industries and in other countries in order to
determine the generalizability of the results. Since some of the constructs measured
here are similar to those measured with American logistics outsourcing firms, a cross-
cultural comparison study should be feasible.
2.6. Conclusion
This chapter presented a review of the relevant literature that serves as the
basis of the development of the hypotheses presented in Chapter 3. First, the literature
in logistics outsourcing was discussed, with a focus on the relationships between
3PLs and their customers. Then the definition of partnering behavior adopted in this
dissertation was presented, along with a brief overview of the various research
streams in the logistics, marketing and strategy literatures that have investigated the
formation of “hybrid governance structures”, of which partnerships is one type. Next,
the relationship marketing perspective was introduced, with special attention to social
exchange theory that serves as basis for the development of the model in this
dissertation. Next, a brief description of Ganesan’s (1994) model of long-term
orientation in buyer-seller relationships was presented, upon which this dissertation
builds. Finally, a discussion of the generalizability of the findings of this study was
presented in light of the literature on cross-cultural differences.
67
Chapter 3: Model Development and Hypotheses
The objective of this chapter is to present the development of a model of the
antecedents of customer partnering behavior in logistics outsourcing relationships.
The initial section of the chapter describes the conceptual model based on the
relationship marketing perspective and, more specifically, social exchange theory.
Next, the rationale for the each of the hypotheses that compose the model is
discussed.
3.1. Conceptual model
In this dissertation, a customer’s partnering behavior in the relationship with a
3PL corresponds to the customer’s perception that this relationship presents the
following behavioral elements (Gardner et al 1994): planning, sharing of benefits and
burdens, extendedness, systematic operational information exchange, and mutual
operating controls. In order to identify the antecedents of this type of behavior, a
relationship marketing perspective is adopted.
Traditional relationship marketing models that investigate the development of
interorganizational relationships follow the premises of social exchange theory (SET)
and focus on the dynamics of the relationship under investigation (i.e. they focus on
interorganizational factors). In these models, trust and dependence are consistently
used as motivators for each partner to engage in and develop lasting and mutually
beneficial relationships (Hewett and Bearden 2001). In addition, social exchange
theory has emphasized that partners engage in relationships if they are rewarding or
satisfactory (Lambe et al 2001). This dissertation follows the traditional social
68
exchange theory rationale (e.g., Ganesan 1994, Hewett and Bearden 2001) and
includes trust, dependence, and satisfaction as main antecedents of customer
partnering behavior in the relationship with a 3PL. More specifically, it is
hypothesized that a customer’s trust in a 3PL’s credibility and benevolence,
dependence on a 3PL, perception of 3PL dependence on a customer, and satisfaction
with the relationship with a 3PL will be related to a customer’s partnering behavior.
However, largely based on premises of relationship marketing, and in
particular, social exchange theory, it is hypothesized that interorganizational factors,
such as dependence, trust, and satisfaction are not the only elements that explain a
customer’s partnering behavior in the relationship with a 3PL. It is proposed that
some customer-specific characteristics will also impact a customer’s partnering
behavior as well.
One of the foundational premises of social exchange theory is that social and
economic outcomes of an exchange are compared to a specific comparison level
(CL). This CL represents the benefits that a firm feels is deserved from a relationship
and is unique to each firm (Lambe et al 2001). In the logistics outsourcing context,
customers have also had unique partnering experiences with other 3PLs. This prior
experience may affect their expectations regarding their relationship with the current
3PL (i.e., its respective CL). It can be inferred that if a 3PL customer had positive
experience partnering with other 3PLs, then it would likely be more willing to exhibit
partnership behavior in the present. This argument is consistent with network theory,
in that one of the main assumptions is that experience from earlier relations is crucial
69
to understand the development of current cooperative behaviors (Skjoett-Larsen
2000).
Moreover, relationship marketing scholars propose that some firms have a
particular orientation towards engaging in relationships with their main partners.
More specifically, some firms have a “relationship marketing orientation” (RMO)
incorporated in the firm’s values and culture. Awareness of a customer’s relationship
marketing orientation can be crucial to identifying whether relational marketing is an
appropriate strategy to adopt (Rao and Perry 2002). For example, Garbarino and
Johnson (1999) found that for the customers of a New York repertory theater
company, trust and commitment were mediators only for high relational customers,
not for low relational customers. Therefore, in the 3PL context, it may be the case that
even if the 3PL is performing efficiently and effectively, certain customers will not
engage in partnerships with the 3PL simply because they are not focused on building
close relationships or partnerships.
In sum, the present model hypothesizes that the following interorganizational
conditions and customer-specific characteristics will influence a customer’s
partnering behavior with its 3PL:
- a customer’s dependence on its 3PL;
- a customer’s perception of a 3PL’s dependence on the customer;
- a customer’s trust in its 3PL’s credibility;
- a customer’s trust in its 3PL’s benevolence;
- a customer’s satisfaction with previous outcomes of the relationship with the
3PL;
70
- a customer’s prior experiences with partnering with other 3PLs;
- a customer’s relationship marketing orientation.
In addition, the model proposes antecedents for the interorganizational factors
as well (i.e., antecedents of both components of dependence and trust). Figure 4,
below, depicts the conceptual model to be detailed in the following section.
Figure 4. Conceptual model of customer partnering behavior in logistics outsourcing
relationships
In the pages that follow, the conceptual model is described in more detail
through the development of specific hypotheses. The primary antecedents of
customer partnering behavior are presented first, followed by the antecedents of
dependence and trust.
Antecedents of customer
and 3PL dependence
Antecedents of trust
Satisfaction
With 3PL
Customer Trust
in 3PL
(credibility and benevolence)
Customer and
3PL
Dependence
Customer
Partnering
Behavior
Interorganizational conditions
Customer characteristics
Customer
Relationship Mkt.
Orientation
Customer
Partnering
Experience
Customer
Relationship Mkt.
Orientation
Customer
Partnering
Experience
71
3.2. Hypotheses development
In this section the rationales for the hypotheses are presented. First, the
primary antecedents of customer partnering behavior are discussed. Next, the
antecedents of both customer dependence on a 3PL and the perception of 3PL
dependence on a customer are discussed. Finally, the development of the hypotheses
for the antecedents of both dimensions of trust (i.e. credibility and benevolence) is
presented.
3.2.1. Primary antecedents
The rationale for the hypotheses related to primary antecedents of customer
partnering behavior is based on the premises of social exchange theory, network
theory, and the relationship marketing orientation perspective. They are related to
both interorganizational conditions and customer specific characteristics. Five
interorganizational conditions are identified: 1) customer’s perception of its
dependence on a 3PL; 2) customer’s perception of a 3PL dependence on the
relationship with a customer; 3) customer’s trust in a 3P’s credibility; 4) customer’s
trust in a 3PL’s benevolence; 5) customer’s satisfaction with previous outcomes of
the relationship. Throughout the section, the discussion of both dimensions of
dependence and both dimensions of trust will be presented jointly. The customer-
specific characteristics hypothesized to impact customer partnering behavior with a
3PL are: 1) a customer’s prior experience with partnering with 3PLs and 2) a
customer’s relationship marketing orientation.
72
Figure 5, below, depicts the sub-model comprising the primary hypotheses. In
the paragraphs that follow, the rationale for each hypothesis is discussed in detail.
Figure 5. Primary antecedents of customer partnering behavior in logistics outsourcing
relationships.
Customer dependence, 3PL dependence, and customer partnering behavior.
In the marketing literature, dependence has been viewed as both an antecedent and an
outcome of a relationship. Dwyer et al (1987), for example, define dependence as
“the recognition by both partners that the relationship provides greater benefits than
either party could attain alone or that outcomes obtained from the exchange
Customer’s
dependence on 3PL
Customer’s
dependence on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Perception of 3PL’s
dependence on customer
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation +
+
+
+
+
+
-
73
relationship are greater than those possible from other business alternatives.” On the
other hand, Lambe et al (2000) argue that dependence is built over time as the
partners: 1) invest in the exchange relationship; 2) determine mutually compatible
goals; and 3) see positive outcomes from the relationship. Most studies in channels of
distribution, however, have viewed dependence as a determinant of organizational
conduct and strategic behavior (Ganesan 1994).
In this research, dependence of a customer on a 3PL is considered as an
antecedent of the relationship style between a customer and a 3PL, and is defined as a
customer’s need to maintain the channel relationship to achieve desired goals
(Frazier, 1983). Following the social exchange theory rationale, this study proposes
that dependence of a customer firm on its 3PL occurs when the benefits accruing
from the relationship are higher than those that could be obtained outside the
relationship, either through an alternative partner or with no partner at all (Thibaut
and Kelley 1959). As well, following Pfeffer and Salancik’s (1978) resource
dependence rationale, it is considered that dependence of a customer on a 3PL is
caused by the perceived need of a 3PL’s critical resource; i.e., the expertise and
capability of planning and performing complex logistics activities more efficiently
and more effectively. According to the resource dependency theory, the need to
acquire these critical resources creates a situation of dependency, and in order to
maintain a consistent supply of these resources, a firm (i.e. customer) may choose to
generate alliances with the supplier organization (i.e. 3PL) (Sakaguchi et al 2004).
Pfeffer and Salancik (1978) suggest that the typical solution to problems of
dependence and uncertainty involves increasing coordination, which means
74
increasing mutual control over one another’s activities. For the retailer-vendor case,
Ganesan (1994) proposes that one way for retailers to gain control over important and
critical vendors is to have a long-term orientation, and to improve the overall
profitability of both parties through investments in the relationship. Investing in the
relationship with both tangible and intangible resources will eventually reduce
asymmetries in dependence and increase mutual dependence. Examples of such
investments in the 3PL setting are: compatible software, training of personnel,
investment in physical assets such as warehouses, and so forth. Extending Ganesan’s
rationale and following Pfeffer and Salancik (1978), this study proposes that a 3PL
customer’s perceived dependence on a 3PL will lead to the customer’s close
involvement in the 3PL’s activities. This can be achieved by means of partnering.
Research has consistently shown the key role that dependence plays in
nurturing cooperation and adaptation in relational exchanges, thus contributing to
partner commitment (Knemeyer et al 2003). Sakaguchi et al (2004), for example,
created and tested a model of supply chain integration, theoretically grounded in the
resource dependency perspective (tested with U.S. small businesses). They adopted
the IT integration model of Chwelos, Benbasat and Dexter (2001) and found that
companies with a higher level of resource dependency were more likely to integrate
their supply chains compared to those with less resource dependency.
The opposite situation, however, might occur: a customer might perceive that
a 3PL is dependent on it. The same rationale discussed above will hold in this
situation. When a customer perceives a 3PL to be dependent on their relationship, a
customer may be less willing to assume the costs of maintaining a close relationship.
75
The net benefits provided by this 3PL may not be perceived to be greater than what
could be provided by alternative 3PLs (Ganesan 1994). In this situation, a customer
has little incentive to exhibit partnering behavior with this 3PL. Based on the above
discussion, two hypotheses are presented:
H1: Customer dependence on a 3PL is positively related to the customer’s partnering
behavior.
H2: Perceived dependence of a 3PL on a customer is negatively related to the
customer’s partnering behavior.
Customer’s trust in a 3PL’s credibility and benevolence, and customer
partnering behavior. As discussed previously, the relationship marketing literature
has emphasized that dependence is not sufficient to explain the decision to engage in
business-to-business relationships (Ganesan 1994, Lambe et al 2001). Firms with
exclusively high levels of dependence and asset specificity may seek to escape this
dependence (Ganesan 1994). With trust, however, the focus is on future conditions:
exchange partners weigh their outcomes through the lens of anticipated past and
future exchanges and the social benefits of compromise. Moreover, when reciprocal
motivations for developing relationships are in place, partners have the objective of
obtaining mutual benefits by means of cooperation, collaboration, and coordination
(Oliver 1990).
Trust is defined as the belief in an exchange party’s reliability and integrity
(Morgan and Hunt 1994) or as the belief that a party’s word is reliable and that a
party will fulfill its obligations in an exchange (Pruitt 1981). Previous research has
operationalized trust in a number of ways. Many studies operationalize trust as a
unidimensional factor (Morgan and Hunt 1994, Doney and Cannon 1997, Hewett and
76
Bearden 2001, Nicholson et al 2001). Knemeyer (2000) defines trust as a construct
with three dimensions: achieving results, acting with integrity, and demonstrating
concern. Achieving business results is related to the ability to perform tasks in which
the trustee is expected to be an expert. Demonstrating concern is equivalent to
benevolence, while acting with integrity is related to the trustor’s perception that the
trustee adheres to a set of principles that the trustor finds acceptable. This study
follows Ganesan (1994) and defines trust as a construct with two components:
credibility and benevolence. Credibility is based on the extent to which the customer
believes that a firm has the required expertise to perform the job effectively and
efficiently. This dimension is related to the consistency and stability of the trustee’s
behavior. Benevolence is based on the extent that the customer believes that a firm
has intentions and motives beneficial to the customer when new conditions arise;
conditions to which a commitment has not been made (i.e., it focuses on the
intentions of the exchange partner rather than on the exchange partner’s specific
behavior).
Trust is a major construct in most relationship marketing models (Wilson
1995) and the key social variable in explaining interfirm cooperation and long-term
relationships (Izquierdo and Cillán 2004). Indeed, Morgan and Hunt’s (1994) finding
that trust leads to acquiescence, cooperative behaviors, and a decrease in uncertainty,
supports the argument that from a relational perspective, trust is an important
mechanism for persuasion and fostering future exchanges (Hewett and Bearden
2001). Doney and Canon (1997), likewise, find that trust enhances the likelihood of
future interactions among parties. Pruitt (1981) indicates that trust is highly related to
77
a firm’s desires to collaborate. In the outsourcing and 3PL literatures, trust has been
often presented as an important driver or mediator of successful 3PL-customer
relationships. Zineldin and Bredenlow (2003), for example, in a case study with two
Swedish manufacturers involved in strategic outsourcing relationships, emphasized
that a long-term relationship does not guarantee success without trust and
commitment. Similarly, Knemeyer and Murphy (2004) surveyed 3PL users and found
a positive relationship between trust and the 3PL customer’s perceived performance.
In a 3PL-customer relationship setting, it is expected that a customer’s trust in
a 3PL increases customer partnering behavior. More specifically, a customer’s trust in
a 3PL affects its decision to enter into a partnership in three ways (Ganesan 1994): 1)
it reduces the perception of risk associated with opportunistic behavior by the 3PL; 2)
it increases the confidence of the retailer that short-term inequities will be resolved
over a long period, and; 3) it reduces the transaction costs in an exchange relationship
(Williamson, 1981). From the above discussion, it is hypothesized that higher levels
of customer trust in a 3PL are related to a higher level of customer partnering
behavior.
H3: A customer’s trust in a 3PL’s credibility is positively related to a customer’s
partnering behavior.
H4: A customer’s trust in a 3PL’s benevolence is positively related to a customer’s
partnering behavior.
Prior partnering experience and customer partnering behavior. Network
and social exchange theories propose that the earlier experiences that a firm has had
with other partners play a role in explaining a firm’s behavior in present relationships
(Skjoett-Larsen 2000). Network scholars, for example, emphasize the important role
78
of prior experience with other partners as a factor that will shape the organization’s
expectations regarding the new relationships and increase the likelihood of future
endeavors (Uzzi 1996). As Skjoett-Larsen (2000) emphasizes, one of the main
assumptions in the network model is that not only the “chemistry between
individuals” within the parties, but also the actual (positive) experience from earlier
relations is crucial to understanding the development of cooperative behaviors
between 3PLs and their customers. From a social exchange theory perspective, each
firm has a social and economic benefit standard that it feels is deserved in a
relationship; i.e., the so called comparison level CL (Thibaut and Kelley 1959). This
level is compared to the outcomes from a particular relationship. In a logistics
outsourcing relationship context, it can be inferred that customer firms with positive
previous experiences with partnering (with other 3PLs) have passed through the
inherent difficulties and challenges of the process. As a result, these firms have
acquired a capability to plan and coordinate operational and administrative logistics-
related activities and manage a partnership-type relationship with an external
organization. These firms are then more likely to have realistic expectations towards
their present relationship and to engage in a partnership with their 3PLs.
This line of reasoning is supported in several empirical studies. Ho et al
(2003), in the context of spin-off IT outsourcing (i.e., an IT department within an
organization gets “spun-off into a separate external entity”), found that firms with
prior outsourcing experience with other third-parties experienced less managerial
conflicts, with this previous experience having a positive impact on performance. In
his study of alliances and networks, Gulati (1999) noted that by participating in
79
alliances, firms can develop managerial capabilities that result from their experiences
and learning. This learning can enhance the likelihood of engaging in new alliances.
Gulati found that the greater a firm’s alliance formation capabilities, the greater the
likelihood for that firm to enter a new alliance. In another study, Gulati et al (2000)
noted that firms that forged a greater number of alliances appeared to extract more
value from their alliances over time. He suggested that experience with alliances can
be a source of strategic advantage.
Therefore, it is hypothesized that customers that had prior positive
experiences in partnering with 3PLs are more likely to exhibit higher levels of
partnering behavior with the present 3PL.
H5: Prior partnering experience with 3PLs is positively related to customer’s
partnering behavior with the focal 3PL.
Relationship Marketing Orientation and customer partnering behavior. The
marketing discipline has been reshaped with a relationship marketing orientation (Sin
et al 2005), in which short term transactional exchanges are replaced with long-term
buyer-seller relationships. When exhibiting a relationship marketing orientation, a
firm’s strategy emphasizes relationship building by cultivating trust, empathy,
bonding and reciprocity between a firm and its customers (Tse et al, 2004). The
nurturing of market relationships is considered in the literature as a top priority for
most firms (Day, 2000) and a valuable resource (Helfert et al, 2002).
Gronroos (1991) argued that the purpose of relationship marketing is to
“establish, maintain, and enhance relationships with customers and other parties at a
profit by mutual exchange and fulfillment of promises.” After a comprehensive
80
review of 26 definitions of relationship marketing, Harker (1999) proposed that “an
organization engaged in proactively creating, developing and maintaining committed,
interactive and profitable exchanges with selected customers (partners) over time is
engaged in relationship marketing” (p. 16). In order to maintain relationships with
valuable customers, a relationship orientation must pervade the mind-set, values, and
norms of the organization (Day, 2000). In other words, the buyer-seller relationship
must be at the center of the firm’s strategic or operational thinking (Tse et al 2004,
Sin et al 2005). One interesting point to highlight is that relationship marketing is in
line with the concept of supply chain management (SCM), since the one main
characteristic of the SCM philosophy is “a strategic orientation toward cooperative
efforts to synchronize and converge intrafirm and interfirm operational and strategic
capabilities into a unified whole, as well as a customer focus to create unique and
individualized sources of customer value, leading to customer satisfaction” (Min and
Mentzer 2004).
In the logistics outsourcing literature, the 3PL customer’s orientation towards
building and maintaining lasting relationships with customers and partners has been
considered to be a crucial factor in determining the supply chain role of logistics
providers (Bolumole, 2001). Larson and Gammelard (2001), for example, argued that
close cooperation between buyer and supplier may lead to plans to bring a carrier into
the collaborative process. In a study of the role of carriers within buyer-supplier
partnerships, Gentry (1996) enforced that logic and proposed that increasing the
involvement of carriers within these partnerships may enhance cost savings and
service improvements, as all parties work together to improve quality and operational
81
efficiencies. She found empirical support for the contention that carriers utilized in
buyer-supplier partnerships were viewed differently from carriers used in non-
partnering buyer-supplier relationships. More specifically, she found that carriers
within existing buyer-supplier partnerships were more likely to embody the
dimensions of: (1) long term commitments, (2) open communications and
information sharing, (3) cooperative continuous improvements on cost reductions and
increased quality, and (4) the sharing of risks and rewards of the relationship.
Based on the above discussion, it is hypothesized that organizations that
engage in a relational approach with customers and suppliers have a more external
focus and are thus more likely to perceive the 3PL as an integral part of their supply
chain, as a facilitator of supply chain integration. In other words, it is proposed that a
firm with a relationship marketing orientation towards (i.e. collaborates and bonds
with) channel partners and is using a 3PL provider will more likely exhibit
characteristics of partnering with a 3PL.
H6: A customer’s relationship marketing orientation is positively related to a
customer’s partnering behavior with a 3PL.
Satisfaction with previous outcomes and customer partnering behavior. As
social exchange theory emphasizes, firms engage in relationships because they expect
the benefits to exceed the costs of maintaining them. In exchange relationships, firms
utilize the history of a relationship to anticipate the costs and benefits of continuing
and developing the relationship (Lambe et al 2001). Although most studies include
satisfaction as an outcome variable of relational exchange, Ganesan (1994) considers
satisfaction with previous outcomes as a predictor of relational exchange (long-term
82
orientation is one dimension of relational exchange). Network scholars share the
perspective for this argument, and argue that connections between firms become
closer (i.e., become an “embedded tie”) if expectations are met, or in other words,
some level of satisfaction is achieved (Uzzi 1996). Therefore, if a 3PL customer is
satisfied with its relationship with the 3PL, it is reasonable for the customer to assume
that continuing the relationship is appropriate.
H7: A customer’s satisfaction with past outcomes of the relationship with a 3PL is
positively related to the customer’s partnering behavior with that 3PL.
3.2.2. Antecedents of dependence
Dependence, itself, is caused by a number of factors. Heide and John (1988)
follow Emerson’s (1962) theory of dependence and identify four circumstances in
which dependence is increased: 1) when the outcomes of a relationship are highly
valued; 2) when the outcomes of a relationship are higher than those obtained from
alternative relationships (notion of comparison of outcome levels); 3) when there are
few available alternative sources of exchange (concentration of resources); and 4)
when there are fewer potential alternative sources of exchange.
Ganesan (1994), as well as many other researchers (e.g., Anderson and Narus
1990, Anderson and Weitz 1992, Heide and John 1988), have emphasized the roles of
transaction specific investments and environmental volatility and diversity as
predictors of dependence. However, in the context of logistics outsourcing, it is also
proposed that the nature and complexity of logistics operations will impact the level
of perceived dependence of a customer on a 3PL. If any firm is operating
83
internationally, for example, it has to deal with complicating factors, such as customs
and local import/export regulations. In this case, the expertise of a 3PL may be much
more valued by a customer than if the firm only has domestic operations. Another
example relates to the breadth and complexity of the distribution or sourcing network:
If it is broad and complex, the network should require more expertise than if the
network is simple. Finally, according to the capabilities perspective, a firm’s
consideration of its internal resources and capabilities vis-à-vis the capabilities of
potential partners may also impact the decision to partner (White 2000).
In summary, the antecedents of dependence identified in this study include:
environmental volatility and diversity in the 3PL and product markets, transaction
specific investments, complexity of logistics operations, and internal logistics
capabilities. Figure 6, below, depicts the antecedents of customer and 3PL
dependence. Each of these antecedents is discussed in depth below.
84
Figure 6. Sub-model of antecedents of dependence.
Internal logistical capabilities and dependence. Resource based view and
dynamic capabilities literatures have defined capabilities or distinctive competencies
as “those attributes, abilities, organizational processes, knowledge and skills that
allow a firm to achieve superior performance and sustained competitive advantage”
(Peteraf 1993, Morash et al 1996). In the logistics setting, Morash et al (1996)
propose that logistics capabilities can be divided into two major groups: demand
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
Dependence of
customer on 3PL
TSI by 3PL TSI by 3PL
TSI by customer TSI by customer
3PL
dependence
3PL
dependence
Customer logistics
capabilities
Customer logistics
capabilities
Logistics
complexity
Logistics
complexity
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
-
-
-
+
+
+
+
+
+
-
85
oriented and supply oriented capabilities. Demand oriented capabilities emphasize
customer closeness and responsiveness to the target market, whereas supply oriented
capabilities are related to operational excellence, usually with an internal focus and an
emphasis on cost reduction. They point out that no strategy is necessarily superior to
the other. Each firm’s logistics strategies should be designed to support the firm’s
overall strategies.
The capabilities perspective postulates that a firm’s decision to make, buy, or
ally is generally made after consideration of not solely external competitive factors,
but also internal capability-related factors (White 2000). Each firm has unique
capabilities that incur in unique production costs that by their turn influence strategic
decisions, including the formation and development of interorganizational
relationships. Empirical evidence for this argument is shown in several studies. In a
case study article within a multidivisional firm that produced industrial goods for the
electronics, telecommunications, aerospace and electric power industries, Argyres
(1996) observed that firms vertically integrated into those activities in which they
have greater production experience and/or organizational skills (i.e., capabilities) than
their potential suppliers. Combining internal capabilities and TCE perspectives,
White (2000), in a study of state-owned pharmaceutical firms, found that firms with
prior experience in new compound development were more likely to be involved in
undertaking development activities. His rationale was that these firms had developed
capabilities that allowed them to do so.
The effect of firms’ logistics capabilities on their logistics outsourcing
decisions has been acknowledged in the logistics literature as well. As Gilley et al
86
(2004) point out, it is essential to include both internal and external antecedents of
outsourcing for the development of a general theory of outsourcing. Bolumole (2001)
explains that a firm’s outsourcing strategies will largely depend on the way a firm
perceives its own capabilities compared to its perception of 3PL abilities. Similarly,
Rao et al (1994) argue that one obstacle to the expansion of logistics outsourcing is
that many customers believe that their own departments provide more cost-effective
service than that provided by a 3PL.
Following this logic, this study proposes that a customer’s perception of its
logistical competencies will impact the degree of its perceived dependence on its
3PL. It is proposed that when a customer perceives its logistics capabilities to be
adequate, it feels self-sufficient and not dependent on its 3PL. Conversely, a customer
that has lower logistics capabilities perceives itself to be more dependent on its 3PL.
In this case, the outcomes obtained through the relationship with the 3PL are more
highly valued. It is proposed, then:
H8: A customer’s logistics capabilities are negatively related to a customer’s
dependence on a 3PL.
Environmental volatility, environmental diversity, and dependence.
Decision-making uncertainty refers to the degree to which a firm is not able to predict
or anticipate the environment. In the strategy literature, environmental uncertainty is
linked to different dimensions, such as demand unpredictability or difficulty in
anticipating actions from actual and potential competitors (Boyd, 1990). Following
Ganesan (1994), this research investigates the effects of two key dimensions of
environmental uncertainty - environmental dynamism and environmental complexity
87
– on dependence in buyer-seller relationships. Environmental volatility (or
dynamism) refers to rapid and unpredictable changes or fluctuations in demand in an
industry, representing the level of turbulence or instability facing an environment. In
a highly volatile environment, the difficulty to forecast demand and predict trends
increases substantially. The second dimension is environmental diversity (or
environmental complexity), which is defined as the heterogeneity of resources in an
environment (Boyd, 1990). A diverse environment is composed of many products,
vendors, and competitors.
The effects of these two sources of environmental uncertainty on firm strategy
and behavior have been extensively studied in the strategy, outsourcing, and
relationship marketing literature. Environmental complexity, for example, was found
to have a positive effect on firm linkages in terms of number of interlocks
6
(Boyd
1990). Environmental volatility or dynamism, often called “the strongest determinant
of environmental uncertainty” (Joshi and Campbell 2003), was found to be positively
associated with: 1) relational governance between manufacturers and suppliers (Joshi
and Campbell 2003), 2) outsourcing activities of small firms (Gilley et al 2004) and,
3) degree of modularity
7
(Schilling and Steensma 2001).
The question, then, is why uncertainty leads to dependence. In the retailer-
vendor context, Ganesan (1994) examines dimensions of uncertainty in the retail
market. Ganesan proposes that environmental volatility increases the dependence of a
retailer on a vendor because in a high volatile environment, in which sales fluctuate
6
An interlock between two firms occurs when one director of a firm also sits on the board of directors
of the second firm (direct interlock), or when two firms have representatives on the board of a third
firm (indirect interlock).
7
Usage of “flexible” organizational forms, such as contract manufacturing, alliances, or alternative
work arrangements.
88
and sales forecasts are difficult to predict, retailers may not be able to foresee all
circumstances in a contract. Therefore, they may engage in long term relationships
with vendors in order to prevent possible opportunistic behaviors. On the other hand,
Ganesan proposes that environmental diversity is negatively associated with a
retailer’s dependence on a vendor. He argues that in markets with a variety of
products and alternate vendors, retailers may have difficulty in developing
appropriate strategic programs for each product. The retailers, therefore, may be
encouraged to develop flexible and temporary channel structures with multiple
channel partners.
In the logistics outsourcing setting, it is proposed that environmental volatility
and diversity should be investigated in two distinct markets: 1) the market for the
product the 3PL customer buys from the 3PL; i.e., logistics services, and 2) the
market for the product the 3PL customer sells to its own customers. As the
paragraphs that follow illustrate, the proposed rationale for understanding the effects
of uncertainty differs between these two markets.
In the market for 3PL services, a source of dependence is related to
availability of alternative 3PLs to the one currently used by the customer, i.e., the
diversity of the market for 3PL services. If the customer perceives the 3PL industry to
have many competitors and service offerings (i.e., diverse) it will perceive itself to
have more alternatives to the focal 3PL, reducing the level of dependence on the focal
3PL. On the other hand, if the service offerings in a 3PL market is perceived to be
volatile, due to capacity problems or high demand, the customer may feel itself to be
more dependent on a 3PL (i.e., to lock-in supply).
89
Diversity and volatility in the market for the product the customer ships with
the 3PL (here called the product market) will also impact perceived dependence of on
a 3PL. In the context of logistics operations, when a firm is embedded in a volatile
environment, shipment sizes and locations may change rapidly, leading to higher
complexity in operational planning. (Cooper and Gardner 1993). Having a close
relationship with a 3PL may increase the probability of 3PL assistance in these
circumstances. With respect to environmental diversity (e.g., high level of
competition, short product life cycles), firms may more likely focus on their core
competencies and outsource its logistics functions (Quinn and Hilmer 1994;
Rabinovich et al. 1999). Therefore, a firm will tend to strengthen links with a 3PL
provider in order to gain better control over its operations.
Based on the above discussion, the following hypotheses are presented:
H9: Environmental diversity in the market for 3PL services is negatively related to a
customer’s dependence on a 3PL.
H10: Environmental volatility in the market for 3PL services is positively related to a
customer’s dependence on a 3PL.
H11: Environmental diversity in the product market is positively related to a
customer’s dependence on a 3PL.
H12: Environmental volatility in the product market is positively related to a
customer’s dependence on a 3PL.
Logistics complexity and dependence. With the advent of globalization and
internationalization, many firms have extended their geographic activities and product
scope, and are now dealing with a more diversified range of customers with different
tastes and preferences. These firms must face multifold and simultaneous pressures:
the need for ceaseless innovation to cope with shorter product-life cycles, the
90
requirements for consistent efficiency improvements in order to compete in highly
competitive global markets; and the need to meet customers increasing demands for
on-time performance, more frequent deliveries, etc. These pressures are not restricted
to multinationals. Even those firms focusing on domestic markets must compete with
foreign rivals and develop a global perspective (Mentzer et al 2000). These
challenging requirements increase the complexity of a firm’s logistics operations.
Rao and Young (1994) propose that logistics complexity has to do with the
following: 1) the volume and variety of logistics transactions, impacting both physical
and information tasks; 2) divergence in the number and sequence of transactions that
must be performed for the various products moving in different regions of the world,
and 3) interdependency of tasks within the supply chain process, which places a
premium on co-ordination and control. More specifically, they argue that logistics
complexity is composed of three components that affect the difficulty of coordinating
material and information flows:
Network complexity refers to both the geographic
dispersion of a firm’s trading partners as well as the
intensiveness of transactions with selected trading partners
which can give rise to volume leveraging effects.
Process complexity refers to time and task compression
(or lack thereof) in the supply chain. When the logistics
process is complicated by the number of tasks which have to be
performed and coordinated within a short span of time, such as
in JIT environments, numerous cost/service tradeoffs and
functional interdependency arise in operations.
Product complexity refers to the special circumstances
required by products and materials due to the complexity of the
environment (temperature, humidity, etc.) governing their
transportation, storage and handling. Hazardous materials,
goods with short shelf lives or that are susceptible to damage,
and other physical properties make logistics more difficult.
91
According to the resource dependence perspective, one critical factor that
increases the degree of perceived dependence is the importance of the resource to the
firm (Pfeffer and Salancik, 1978). It is proposed that 3PL customers, whose
businesses involve complex logistics operations in terms of network, process, and
product complexity, perceive logistics to be crucial to their businesses and thus may
perceive themselves to be dependent on the 3PL provider. It is proposed, then:
H13: A customer’s logistics complexity is positively related to a customer’s
dependence on a 3PL.
Transaction-specific investments (TSI) by customer and 3PL, and
dependence. 3PLs and their customers may have to undertake investments in assets
that may be specific to their particular relationships and not be easily deployed in
other relationships. Examples of such investments include: cold storage areas,
customized trailers, special warehouse material-handling equipment (Cooper and
Gardner, 1993), training of warehousing personnel, and the provision of “dedicated
electronic linkups for inventory control for a particular partner’s account” (Knemeyer
et al, 2003). These are called transaction-specific investments – TSI (Williamson,
1981), key considerations in make-or-buy decisions (Aersten 1993) and widely used
as antecedent factors affecting the degree of channel and supply chain integration
(e.g., Wu et al 2004).
TSIs have several relationship stabilizing properties (Wu 2004): for example,
they act as important pledges in the channel relationship and have a positive effect on
the partner commitment to the relationship (Anderson and Weitz 1992); they are
92
useful in minimizing opportunistic behavior, and; they facilitate expectations of
continued exchange (Heide and John, 1990). Indeed, TSIs are the most frequent
demonstration of commitment to a relationship (Rinehart et al 2004). In addition, all
other things being equal, as the need to invest in relationship-specific assets increases,
firms may seek to incorporate additional partnership elements into their relationship
(Cooper and Gardner 1993). Ganesan (1994) argues that TSIs create exit barriers to
the investing party, thus increasing dependence on its partner. In his research setting,
Ganesan proposes that retailer TSIs are positively related to the retailer’s dependence
on a vendor, and that a retailer’s perception of vendor TSIs are negatively related to a
retailer’s dependence on a vendor.
It can be argued that the same rationale holds in the context of 3PL-customer
relationships. A customer that has invested in specific assets, such as capital
investments or in training and equipment (or in the present context, has divested of
assets that are replaced by those of the 3PL) has created exit barriers and may
perceive itself to be more dependent on the 3PL. But 3PLs will often invest in
tangible and intangible assets dedicated to specific customers. In this case, specific
investments made by the 3PL decrease the customer’s perceived dependence on the
3PL because they reduce the threat that the 3PL provider might abandon the
relationship. Therefore, it is hypothesized that:
H14: A customer’s TSIs are positively related to a customer’s dependence on a 3PL.
H15: A customer’s TSIs are negatively related to a customer’s perception of a 3PL’s
dependence on a customer.
H16: A customer’s perception of a 3PL’s TSIs is negatively related to a customer’s
dependence on a 3PL.
93
H17: A customer’s perception of a 3PL’s TSIs is positively negatively related to a
customer’s perception of a 3PL’s dependence on a customer.
3.2.3. Antecedents of trust
The antecedents of a customer’s trust in a 3PL are related to the 3PL behavior
towards the relationship, experience of the customer with the 3PL, and satisfaction
with previous outcomes with the relationship. Figure 7, below, depicts the
antecedents of both dimensions of trust (i.e. credibility and benevolence) to be
discussed in detail in the following paragraphs.
Figure 7. Sub-model of the antecedents of trust
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
3PL reputation
3PL TSI
Satisfaction with
previous outcomes
+
+
+
+
+
+
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
3PL reputation 3PL reputation
3PL TSI 3PL TSI
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
+
+
+
+
+
+
94
TSIs by 3PL and trust. A customer’s perception of a 3PL’s specific
investments in a relationship provides a signal that the 3PL can be trusted (Ganesan
1994). An investment specific to a relationship is tangible evidence that a party is
committed to the relationship, and that it cares for such relationship (Anderson and
Weitz 1992). Indeed, these resources directed specifically towards the other party are
the most frequent demonstration of commitment to the relationship (Rinehart et al
2004). In addition, as mentioned earlier, a party that has invested in a relationship has
increased exit barriers and is less likely to exhibit opportunistic behavior (Heide and
John 1990), which are two factors that reduce the level of trust (Morgan and Hunt
1994). Therefore, it is hypothesized that:
H18. A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s credibility.
H19. A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s benevolence.
3PL reputation and trust. Firm reputation is defined as the opinion or
perception that stakeholders have about a firm’s knowledge, honesty, and care
(Doney and Cannon 1997, Deephouse 2000). Reputation is one of the most powerful
factors in acquiring and retaining customers (Jonsson and Zineldin 2003) and has
been referred to as a means to achieve competitive advantage (Barney 1991).
Bharadway et al (1993) refer to reputation as “brand equity” and define it as “a set of
brand assets and liabilities linked to a brand, its name and symbol that add or subtract
from the value provided by a product to a firm and/or that firm’s customers.” They
argue that firms having strong brand names and symbols are better positioned to
95
mitigate customer perceptions over variability in quality. Firms with strong brands
can, therefore, differentiate themselves from the competition.
The reputation of a firm is built over time through the demonstration of
consistent and reliable behavior (Ganesan 1994). Therefore, if a firm enjoys a
credible reputation in a market, it can be inferred that the firm is trustworthy in
relationships. Kwon and Suh (2004), for example, in a survey of members of four
organizations, found a positive relationship between a partner’s reputation in the
market and the level of trust in the partner.
This study follows Ganesan’s (1994) model and proposes that a 3PL’s
reputation will have a positive effect on a customer’s perception of its credibility, but
not on benevolence. As Knemeyer (2000) explains, reputation for fairness and
effective performance is easily transferable across firms. Therefore, when a customer
perceives its 3PL to have a reputation for achieving the desired results and for being
efficient, it is likely that it will trust the 3PL to perform correctly (i.e. credibility). On
the other hand, caring for the partner and demonstrating concern (i.e. benevolence) is
relationship specific. Perceiving this characteristic can only be realized through actual
interaction, not via word-of-mouth communication.
It is proposed that, when a 3PL has the reputation for effective performance, it
is likely that its customers will trust its credibility and its ability to achieve the desired
results (Knemeyer 2000).
H20: The reputation of a 3PL is positively related to its customer’s perception of the
3PL’s credibility.
96
Experience with 3PL and trust. Outsourcing logistics activities enables firms
to achieve operational flexibility and efficiency but, on the other hand, requires firms
to develop capabilities in order to coordinate their relationship with the 3PL.
Managing an interorganizational relationship involves using appropriate governance
mechanisms, developing inter-firm knowledge-sharing routines, making appropriate
relationship-specific investments, and initiating necessary changes to the partnership
as it evolves, while maintaining the partner’s expectations (Gulati et al 2000). In the
initial stages of a relationship, lack of experience working with the new partner can
put significant demands on management time, efforts and energy (Zineldin and
Bredenlow 2003). Failure is then more common in the initial period of relationships,
whereas longer relationships are less vulnerable to threats (Bucklin and Sengupta
1993) since older relationships have survived phases of adjustment and
accommodation (Anderson and Weitz 1989). Indeed, as Doney and Cannon (1997)
state, partners within older relationships are more familiar and more comfortable
working with each other.
Based on the above rationale, both relationship marketing (Dwyer, Schurr and
Oh 1987) and network perspectives (Gulati et al 2000) postulate that experience with
the partner is a crucial element in explaining increasing levels of trust and strategic
integration (Wu et al 2004). Relationship marketing scholars, such Dwyer, Schurr
and Oh (1987), argue that as experience with a vendor increases, a vendor-customer
dyad is more likely to have passed through critical shakeout periods in the
relationship. Bucklin and Sengupta (1993), in a study of co-marketing alliances,
argued that a long and stable history of business relations between partners builds
97
trust and commitment, achieving greater effectiveness of the relationship. Heide and
John (1990) found a positive association between the historical length of an alliance
and the expected continuity of future interaction. Network scholars share the same
view. Powell et al (1996)’s empirical work of “cycles of learning” in the
biotechnology industry has shown that initial collaborative relationships trigger the
development of experience in managing ties, thus enabling firms to become more
central in a network. This leads to the continuation of the ties, sustaining a positive
feedback process. In the 3PL context, Skjoett-Larsen (2000) defended the importance
of network theory to better understand the dynamics of third party cooperation, and
emphasized the importance of the exchange and adaptation processes in developing
the 3PL-customer relationship, since past and present experience play a major part in
the development of third party cooperation.
Therefore, it is proposed that experience in a relationship with a 3PL provider
will positively impact the customer’s perception of the 3PL’s credibility and
benevolence. Specifically:
H21: A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s credibility.
H22: A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s benevolence.
Satisfaction with previous outcomes and trust. One of the foundational
premises of social exchange theory is that over time, positive outcomes increase trust
(Lambe et al 2001). As Knemeyer (2000) points out, social exchange theory
postulates that outcomes affect behaviors in subsequent periods. Therefore, with
98
mutual exchanges of beneficial action over time, trust and cooperation can be
developed.
Ganesan (1994) proposes that satisfaction with outcomes positively impact the
perception of a partner’s credibility and benevolence. This rationale can be applied to
the 3PL-customer setting. A 3PL customer’s satisfaction is likely to affect the
customer’s perception of the 3PL’s credibility, because it means that the 3PL has
performed its services in an appropriate manner. Similarly, a customer’s satisfaction
is likely to affect the customer’s perception of 3PL benevolence, since it shows that
the 3PL is concerned for the welfare of its customer (Knemeyer 2000). This leads to
the following hypotheses:
H23: A customer’s satisfaction with past outcomes is positively related to the
customer’s perception of the 3PL’s credibility.
H24: A customer’s satisfaction with past outcomes is positively related to the
customer’s perception of the 3PL’s benevolence.
3.3. Hypothesized model
In sum, the overall model of the determinants of customer partnering behavior
in logistics outsourcing relationships (see Figure 8) is composed by the following 24
hypotheses presented in Table 2, below.
99
Table 2. List of hypotheses of the determinants of customer partnering behavior
Number Hypotheses
Primary hypotheses
H1
Customer dependence on a 3PL is positively related to a customer’s partnering
behavior.
H2
Perceived dependence of a 3PL on a customer is negatively related to a customer’s
partnering behavior.
H3
A customer’s trust in a 3PL’s credibility is positively related to a customer’s
partnering behavior.
H4
A customer’s trust in a 3PL’s benevolence is positively related to a customer’s
partnering behavior.
H5
Prior partnering experience with 3PLs is positively related to customer’s
partnering behavior with the focal 3PL.
H6
A customer’s relationship marketing orientation is positively related to a
customer’s partnering behavior with a 3PL.
H7
A customer’s satisfaction with past outcomes of the relationship with a 3PL is
positively related to the customer’s partnering behavior with that 3PL.
Antecedents of dependence
H8
A customer’s logistics capabilities are negatively related to a customer’s
dependence on a 3PL.
H9
Environmental diversity in the market for 3PL services is negatively related to a
customer’s dependence on a 3PL.
H10
Environmental volatility in the market for 3PL services is positively related to a
customer’s dependence on a 3PL.
H11
Environmental diversity in the product market is positively related to a customer’s
dependence on a 3PL.
H12
Environmental volatility in the product market is positively related to a customer’s
dependence on a 3PL.
H13
A customer’s logistics complexity is positively related to a customer’s dependence
on a 3PL.
H14 A customer’s TSIs are positively related to a customer’s dependence on a 3PL.
H15
A customer’s TSIs are negatively related to a customer’s perception of a 3PL
dependence on a customer.
H16
A customer’s perception of a 3PL’s TSIs is negatively related to a customer’s
dependence on a 3PL.
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Table 2 (cont.) List of the hypotheses of customer partnering behavior
Number Hypotheses
H17
A customer’s perception of a 3PL’s TSIs is positively negatively related to a
customer’s perception of a 3PL’s dependence on a customer.
Antecedents of trust
H18
A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s credibility.
H19
A customer’s perception of 3PL specific investments is positively related to the
customer’s perception of the 3PL’s benevolence.
H20
The reputation of a 3PL is positively related to its customer’s perception of the
3PL’s credibility.
H21
A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s credibility.
H22
A customer’s experience with a 3PL is positively related to the customer’s
perception of the 3PL’s benevolence.
H23
A customer’s satisfaction with past outcomes is positively related to the customer’s
perception of the 3PL’s credibility.
H24
A customer’s satisfaction with past outcomes is positively related to the customer’s
perception of the 3PL’s benevolence.
101
Figure 8. A model of the determinants of customer partnering behavior in logistics outsourcing relationships
Env. diversity
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
3PL’s credibility
(trust)
Customer
partnering behavior
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Reputation of
the 3PL
TSI by 3PL
TSI by customer
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Customer
capabilities
Logistics
complexity
Prior experience
3PL partnering
Relationship Marketing
Orientation
Env. diversity
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
+
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
TSI by 3PL TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of 3PL’s
dependence on customer
Perception of 3PL’s
dependence on customer
Customer
capabilities
Customer
capabilities
Logistics
complexity
Logistics
complexity
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
+
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
102
3.4. Contrasting the model of customer partnering behavior
with Ganesan’s model of long term orientation
This subsection has the objective to highlight the contributions and extensions
that the present model of customer partnering behavior in logistics outsourcing
relationships add to Ganesan’s (1994) model of the antecedents of long term
orientation in buyer seller relationships (Figure 9). As the following paragraphs
describe, the main contributions are related to: 1) the nature of the dependent
variable, 2) consideration of firm-specific factors as primary antecedents of the
dependent variable (i.e. customer partnering behavior), and 3) consideration of firm’s
internal capabilities and firm-specific competitive and operational environments as
antecedents of dependence. Figure 10, next, highlights these elements in the overall
model.
In Ganesan’s (1994) model, the dependent variable is “a retailer long term
orientation” in the relationship with its vendor, which is the expectation that the
relationship will last a long time. In the case of the present model, the dependent
variable is “customer partnering behavior”, which is composed of five dimensions:
extendedness, operational information exchange, operating controls, sharing benefits
and burdens of the relationship, and joint planning. Ganesan’s “long-term orientation”
is conceptually equivalent to “extendedness” in the present model. The dependent
variable “customer partnering behavior” is, thus, a broader representation of relational
behavior, whereas Ganesan’s dependent variable focuses on a single dimension of
relational behavior.
103
Figure 9. Ganesan’s (1994) model of long term orientation
The rationale of Ganesan’s (1994) model was primarily based on the premises
of social exchange theory, i.e., interorganizational conditions (trust, dependence, and
satisfaction) are the primary antecedents of relational behavior (long term orientation
in his case). In the model presented in this dissertation, in addition to
interorganizational conditions, firm-specific factors (i.e., experience with partnering
and relationship marketing orientation) are also considered as key antecedents of
relational behavior. The rationale for including firm-specific factors as antecedents of
partnering behavior is drawn from network theory and the strategic orientation
perspective.
Environmental
diversity
Environmental
diversity
Environmental
volatility
Environmental
volatility
Dependence of
retailer on vendor
Dependence of
retailer on vendor
Vendor’s credibility
(trust)
Vendor’s credibility
(trust)
Retailer’s long-term
orientation
Retailer’s long-term
orientation
Vendor’s benevolence
(trust)
Vendor’s benevolence
(trust)
Retailer’s
experience with vendor
Retailer’s
experience with vendor
Reputation of
the vendor
Reputation of
the vendor
Perception of
TSI by vendor
Perception of
TSI by vendor
TSI by retailer TSI by retailer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Perception of vendor’s
dependence on retailer
Perception of vendor’s
dependence on retailer
104
Figure 10. New variables introduced in the model of customer partnering behavior
Finally, based on the premises of resource dependence theory and transaction
costs economics, the antecedents of dependence in Ganesan’s model include the
effects of environmental diversity and volatility in the vendor’s market and
transaction-specific investments by retailer and vendor. The direct equivalents of
these variables in the present model are environmental diversity and volatility in the
3PL market (the 3PL is the vendor) and transaction specific investments by the
customer and 3PL. In the model presented in this dissertation the competitive and
operational environments of the customer are also considered. Specifically,
environmental volatility and diversity in the market in which the customer operates
(i.e., the product market) are also hypothesized to impact dependence. In addition, the
Perception of 3PL’s
dependence on customer
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
Customer
partnering behavior
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
Logistics
complexity
Prior experience
3PL partnering
Relationship Mktg
Orientation
Env. diversity
product market
Env. volatility
product market
*Based on and expanded from Ganesan (1994)
105
present model borrows the rationale of the capabilities perspective to introduce a
customer’s internal capabilities as an additional antecedent of customer dependence.
In conclusion, the model presented in this dissertation is a more complete
representation of the antecedents of relational behavior (more specifically, partnering
behavior). Moreover, it provides a combination of key theoretical perspectives which
have been shown to explain relational behavior.
3.5. Conclusions
This chapter presented the rationale for the conceptual model of customer
partnering behavior in logistics outsourcing relationships and for the hypotheses that
compose the model. The model was developed in light of relationship marketing,
especially social exchange theory and relationship marketing orientation. It included
interorganizational conditions and firm specific factors as well. The proposed main
antecedents of a 3PL customer’s partnering behavior are: 1) a customer’s perceived
dependence on a 3PL; 2) a customer’s trust in a 3PL’s credibility and benevolence; 3)
a customer’s prior experience with partnering with other 3PLs and; 4) a customer’s
relationship marketing orientation. The antecedents of dependence are hypothesized
to be: environmental volatility and diversity in the 3PL and product markets,
transaction-specific investments by the customer and the 3PL, complexity of logistics
operations, and a customer’s internal logistics capabilities. The proposed antecedents
of trust are: 3PL reputation, experience with the 3PL, satisfaction with previous
outcomes, and transaction-specific investments undertaken by the customer.
In Chapter 4, the methodological steps that were followed in order to test the
above hypotheses are described.
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Chapter 4: Methodology
This chapter details the methodology that was followed in order to address the
research questions discussed in the previous chapter. First, the selected research
design for the study is presented. The research structure has shaped the choice of
measures for the variables, as well as the methods of data collection and analysis.
A survey instrument was used in this study. The survey design and
implementation followed the steps described under the survey procedure by Dillman
(2000) - The Tailored Design Method. Following the research design subsection, in
accordance with Dillman’s method, this chapter details the operationalization and
measurement of the constructs and variables, as well as the survey design and
implementation.
A detailed description of all steps used in the data analysis, including the
treatment of possible non-response bias and the quantitative methods adopted, is
presented in Chapter 5.
4.1. Research design
This study utilized a non-experimental design
8
, testing a cross-sectional model
through a survey instrument, which is a standard procedure in the marketing
literature. The survey was conducted with the customer firms of a large Brazilian 3PL
provider called Rapidão Cometa (www.rapidaocometa.com.br). It is an asset-based
company that has been operating for over 60 years. Originating as a traditional
carrier, this firm has transformed itself into a logistics provider, offering a wide
8
i.e., no treatments are given: naturally occurring variation in the independent and dependent
variables without any intervention (by the researcher or anyone else) are used to conduct the research.
107
variety of services ranging from traditional transportation and warehousing to supply
chain solutions. This 3PL has wide geographic coverage in Brazil, and has access to
numerous international locations through an operational partnership with a major
global 3PL. Its customer base comprises firms from various industries, sizes and
markets, thus offering a good picture of the Brazilian logistics outsourcing industry.
The utilization of a survey instrument is necessary given that the majority of
the variables in the model are perceptual measures of behavior that cannot be
captured by secondary/archival data. In addition, one of the purposes of this research
is to adapt and test Ganesan’s (1994) model of determinants of long-term orientation
in buyer-seller relationships to the context of logistics outsourcing. Therefore,
utilizing the same type of methodology is appropriate.
Given that the study was to be conducted with Brazilian firms, performing a
traditional mail survey from Maryland was not feasible. In addition, electronic
surveys present certain advantages, such as faster delivery, faster data collection,
higher response rates, and low cost (Griffis, Goldsby, and Cooper 2003). Therefore, a
web-based survey instrument was considered to be the most efficient means to
acquire information from the 3PL customers.
Rapidão Cometa sent this researcher its list of customers comprising 4,523
firms. The list contained the names of the individuals who manage their companys’
accounts with Rapidão Cometa. It also contained the following information: company
name, industry, position of the contact, e-mail address of the contact, city and state of
company location. The unit of analysis is the firm, with one key informant.
108
4.2. Measurement of the constructs
This section describes the measurement items of the constructs to be tested.
Figure 11 depicts the model. The dependent construct is the customer partnering
behavior in its relationship with a 3PL. The main predictors are the customer’s
dependence on the 3PL, the perceived 3PL dependence on the customer, the
customer’s trust in the 3PL (decomposed into two parts – 3PL credibility and
benevolence), the customer satisfaction with previous outcomes of the relationship,
the customer’s prior experience with outsourcing, and the customer’s relationship
marketing orientation.
Dependence and trust are hypothesized to have specific antecedents. The
antecedents of 3PL and customer dependence are the customer’s perception of:
environmental volatility and complexity in the 3PL market and in the market of the
products its ships with the 3PL (i.e., the product market), transaction-specific
investments (TSI) by both customer and 3PL, the customer’s internal logistical
capabilities, and the logistics complexity of the customer’s operations. The
antecedents of both components of trust – credibility and benevolence - are the
customer’s perception of: transaction specific investments, reputation of the 3PL,
customer experience with the 3PL, and customer satisfaction with previous outcomes
of the relationship.
109
Figure 11. Model of customer partnering behavior in logistics outsourcing relationships.
The measures for most constructs were adapted from existing research and
have been previously tested for validity and reliability. Since one objective of this
study is test for the reliability of Ganesan (1994)’s study in the 3PL context, all items
for identical constructs were adapted from his study. It is relevant to point out that
Ganesan’s measures have been extensively adopted in subsequent articles, generally
presenting strong convergent validity. The remaining measures were adapted from
studies in relationship marketing (e.g., Sin et al 2005) and logistics (e.g., Rao and
Young 1994, Morash 1996, Gardner et al 1994). For two constructs - logistics
complexity and logistical capabilities - items were created rather than adapted.
110
In addition, a number of the variables not directly associated with the study
were included in the questionnaire in order to provide demographic information and
data for future research. These variables include: position of respondent, professional
experience of respondent, number of functions outsourced, various measures of
performance, number of logistics providers currently used by the respondent’s firm,
and demographics of the respondents’ firm (e.g. number of employees, annual sales).
In the paragraphs that follow, the measures for the constructs directly included
in the model are presented.
4.2.1. Dependent construct: customer partnering behavior
Customer partnering behavior. The measures for customer partnering
behavior were adapted from Gardner et al (1994). Parterning is a behavior style that
occurs along the continuum between arm’s-length and vertical integration, and is
composed of five dimensions: extendedness, operational information exchange,
operating controls, sharing benefits and burdens, and planning. The fifteen-item,
seven-point Likert scales (anchored by strongly disagree (1) and strongly agree (7))
are as follows.
Extendedness (EXT)
We expect our relationship with Rapidão Cometa to last a long time.
We are very loyal to Rapidão Cometa.
Maintaining a long-term relationship with Rapidão Cometa is important to us.
Operational Information Exchange (OIE)
We conduct many transactions via computers with Rapidão Cometa.
We exchange operational information with Rapidão Cometa.
111
We use software dedicated to our relationship with Rapidão Cometa. (i.e., EDI)
Operating controls (OCL)
We require shipment tracking ability.
We frequently request delivery control reports.
We request damage/lost control reports.
Sharing of benefits and burdens (SSB)
We are willing to help Rapidão Cometa in difficult situations.
We share risks with Rapidão Cometa.
We have a high willingness to handle unexpected situations by negotiation.
Planning
Rapidão Cometa and our company interact in the activities planning.
We and Rapidão Cometa exchange information that helps establishment of business
planning.
We regularly study Rapidão Cometa's operations for planning.
4.2.2. Primary antecedents
Dependence on 3PL. Dependence items assess the customer’s need to
maintain the relationship with the 3PL in order to achieve desired goals (Frazier
1983). The two measures for customer dependence were adapted from Ganesan
(1994). The first is composed of six item, 7-point Likert scale measures (anchored by
strongly disagree (1) and strongly agree (7)). The second measure refers to the
percentage of the customer’s business for which the 3PL is responsible. The items
are:
112
Measure 1
1) Rapidão Cometa is crucial to our performance.
2) Rapidão Cometa is important to our business.
3) If our relationship with Rapidão Cometa were discontinued, we would have
difficulty in performing its services.
4) It would be difficult for us to replace Rapidão Cometa.
5) We are dependent on Rapidão Cometa.
6) We do not have a good alternative to Rapidão Cometa.
Measure 2
What is Rapidão Cometa’s approximate share of your outsourced logistics
expenditures? ___%
Perception of 3PL provider’s dependence on customer. This construct was also
adapted from Ganesan (1994). The three-item, 7-point Likert-scale measures
(anchored by strongly disagree (1) and strongly agree (7)) are:
1) We are important to Rapidão Cometa.
2) We are a major customer for Rapidão Cometa in our trading area.
3) We are not a major customer for Rapidão Cometa (R).
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Trust. Following Ganesan (1994), trust is decomposed into two major
components: credibility and benevolence. Credibility is based on the extent to which
the customer believes that the 3PL has the required expertise to perform the job
effectively and efficiently. Benevolence is related to the customer’s beliefs in the
3PL’s good intentions and motives towards the customer. Therefore, two latent
constructs are tested. Credibility is composed of 7 items, whereas benevolence is
composed of 5 items. All items are measured by Likert scales (anchored by strongly
disagree (1) and strongly agree (7)).
Credibility
Rapidão Cometa's representative …
1) … has been frank in dealing with us.
2) … makes reliable promises.
3) … is knowledgeable regarding his services.
4) … does not make false claims.
5) … is not open in dealing with us. (R)
6) … is honest about the problems may they arise.
7) … has difficulties answering our questions. (R)
Benevolence
Rapidão Cometa's representative …
1) … has made sacrifices for us in the past.
2) … cares for us.
3) … has supported us in times of shortages.
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4) … is like a friend.
5) … has been on our side.
Satisfaction with previous outcomes. As social exchange theory emphasizes,
firms engage in relationships because they expect the outcomes to be rewarding.
Therefore, firms that are satisfied with their 3PLs are more likely to exhibit partnering
behavior in the relationships with their 3PLs. The seven measures are adapted from
Ganesan (1994) and are measured by Likert scales (anchored by strongly disagree (1)
and strongly agree (7)).
• Are you satisfied with the services provided by Rapidão Cometa? Please
describe your opinion with respect to the outcomes with Rapidão Cometa in
the past year:
Last year…
1) … we were pleased with the outcomes.
2) … working with Rapidão Cometa was very useful.
3) … Rapidão Cometa was ineffective. (R)
4) … we were dissatisfied. (R)
5) … the outcomes were outstanding.
6) … the outcomes were of bad value for our company (R)
7) … we were comfortable in working with Rapidão Cometa.
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Prior experience with partnering with 3PLs. This variable measures the number
of years that the firm has been partnering with 3PLs in general, not necessarily with
Rapidão Cometa. It is a continuous variable.
Has your company ever partnered with logistics providers? ___ Yes ___ No
If yes, how many years has your company partnered with other logistics providers (in
general, not necessarily with Rapidão Cometa)? ____ years.
Relationship Marketing Orientation. According to Sin et al (2005), RMO is
considered to be composed of six dimensions: trust, bonding, communication, shared
value, empathy, reciprocity. The 22-item, 7-point Likert-scale measures (anchored by
strongly disagree (1) and strongly agree (7)) are:
• The following sentences describe the relationship between your company and
your company’s major customers (attention: NOT Rapidão Cometa). Please
indicate your level of agreement.
Trust
1. We trust each other
2. They are trustworthy on important things.
3. According to our past business relationship, my company thinks that they are
trustworthy persons.
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4. My company trusts them.
Bonding
5. We rely on each other.
6. We both try very hard to establish a long-term relationship.
7. We work in close cooperation.
8. We keep in touch constantly.
Communication
9. We communicate and express our opinions to each other frequently.
10. We can show our discontent towards each other through communication.
11. We can communicate honestly.
Shared value
12. We share the same worldview.
13. We share the same opinion about most things.
14. We share the same perspectives toward things around us.
15. We share the same values.
Empathy
16. We always see things from each other’s view.
17. We know how each other feels.
18. We understand each other’s values and goals.
19. We care about each other’s feelings.
Reciprocity
20. My company regards “never forget a good turn” as our business motto.
21. We keep our promises to each other in any situation.
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22. If our customers gave assistance when my company had difficulties, then I
would repay their kindness.
4.2.3. Antecedents of dependence
Customer Transaction-specific investments. The customer specific investments
are tangible and intangible assets that are particular to the relationship and cannot be
easily redeployable. Examples of specific assets in the logistics setting undertaken by
3PL customers are dedicated software, personnel training, etc. The items are adapted
from Ganesan (1994) and are measured by a 7-point Likert scale (anchored by
strongly disagree (1) and strongly agree (7)).
1) We have made significant investments (e.g., technology, training etc.)
dedicated to our relationship with Rapidão Cometa.
2) If we switched to a competing logistics provider, we would lose a lot of the
investment we have made in this relationship.
3) We have invested substantially in personnel dedicated to this relationship.
4) If we decided to stop working with Rapidão Cometa, we would be wasting a
lot of knowledge regarding its method of operation.
Perception of 3PL’s specific investments. The items for this construct were
adapted from Ganesan (1994) and are measured by a 7-point Likert scale (anchored
by strongly disagree (1) and strongly agree (7)).
1) Rapidão Cometa has gone out of its way to link us with its business.
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2) Rapidão Cometa has tailored its services and procedures to meet the specific
needs of our company.
3) Rapidão Cometa would find it difficult to recoup its investments in us if our
relationship were to end.
Environmental diversity in the product market. Environmental diversity or
complexity is related to the heterogeneity and concentration of resources in an
environment. The measurement of the construct is borrowed from Ganesan (1994)
and the items are measured by a 7-point Likert scale (anchored by strongly disagree
(1) and strongly agree (7))
• How would you describe the market for the product you ship with Rapidão
Cometa?
1) There are many new products.
2) There are many new competitors.
3) The market is very complex.
Environmental volatility in the product market. Environmental volatility (or
dynamism) represents the level of turbulence or instability facing an environment,
and is related to unpredictable changes and fluctuations in demand in an industry. The
measurement of the construct is borrowed from Ganesan (1994). The items are
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measured by a 7-point Likert scale (anchored by strongly disagree (1) and strongly
agree (7))
• How would you describe the market for the product you ship with Rapidão
Cometa?
1) The demand is unpredictable.
2) Sales forecasts are accurate. (R)
3) The industry production is stable. (R)
4) The demand trends are easy to monitor. (R)
Environmental diversity in the market for 3PL services. 3PL environmental
diversity is related to the alternatives that customers have to the focal 3PL (i.e.,
competition in the 3PL industry). The scales are the same here as they are for the
customer’s environmental diversity, only with modifications to suit the 3PL industry.
• How would you describe the market for logistics services in Brazil?
The market for logistics services in Brazil…
1) … has many service offerings.
2) … has many carriers/logistics providers.
3) … is very complex.
Environmental volatility in the market for 3PL services. 3PL environmental
volatility is related to the instability in the availability of services in the 3PL industry.
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In the current situation of carrier and port capacity constraints, for example, the
availability of services cannot be taken for granted. The scales are the same here as
they are for the customer’s environmental volatility, only with modifications for the
3PL industry.
• How would you describe the market for logistics services in Brazil?
The market for logistics services in Brazil…
4) … has an unpredictable demand.
5) … has a stable service availability. (R)
6) … is easy to monitor. (R)
Logistics complexity. Rao and Young (1994) suggest that logistics complexity is
composed of three components that affect the difficulty of coordinating material and
information: 1) network complexity (e.g., geographic dispersion and intensiveness of
transactions); 2) process complexity (e.g., time and task compression in operations);
3) product complexity (i.e., special handling and transporting requirements). The
measures adopted here follow these three dimensions. The items are measured by a 7-
point Likert scale (anchored by strongly disagree (1) and strongly agree (7)).
• The following items describe the complexity of your company’s logistics
operations. Please indicate your level of agreement.
1) We have a complex network of trading partners.
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2) The timeliness of the transactions in our supply chain is crucial in our
business.
3) We must accomplish very short order cycle times for customer orders.
4) We have a complex network of origin/destination (OD) pairs.
5) Our products require specialized transportation, storage, or handling
(eg. temperature, humidity, etc.).
Internal logistics competencies. The following items aim to capture the extent to
which the firm dedicates human resources to the management of logistics operations
and to what extent these professionals possess knowledge to manage the operations
and overcome problems. The items are measured by a 7-point Likert scale (anchored
by strongly disagree (1) and strongly agree (7)).
• The following items describe the logistics personnel of your company.
Please indicate your level of agreement.
1) Relative to the size of our firm, we have a large group of upper-level
managers dedicated to logistics.
2) Relative to the size of our firm, we have a large group of employees
across all levels dedicated to logistics.
3) Our logistics personnel have a deep understanding of our logistics
operations.
4) Our logistics personnel know where problems and bottlenecks might
exist in our logistics operations.
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5) Our logistics personnel are capable of finding effective solutions when
problems arise.
4.2.4. Antecedents of trust
3PL Reputation. The items for the construct measure the extent to which the
customer perceives the 3PL to enhance the welfare of its customers. The four items
were adapted from Ganesan (1994) and are measured by a 7-point Likert scale
(anchored by strongly disagree (1) and strongly agree (7)).
1) Rapidão Cometa has a reputation for being honest
2) Rapidão Cometa has a reputation for being concerned about its customers
3) Rapidão Cometa has a bad reputation in the market (R)
4) Most customers think that Rapidão Cometa has a reputation for being fair.
Customer experience with 3PL. Following Ganesan (1994), customer
experience with the 3PL is measured by the number of years the customer has been
associated with the 3PL.
How many years has your company worked with Rapidão Cometa? ____ years. (e.g.,
2.5)
Transaction-specific investments by 3PL. (presented under “Antecedents of
dependence,” above)
Satisfaction with previous outcomes. (presented under “Primary
antecedents,” above)
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4.3. Survey design
After selecting the items for the constructs of interest, the next step was to
design the questionnaire, which involved not only the appropriate arrangement of
questions, but also the presentation letters, conduction of pretests to guarantee the
quality of content, ease of understanding and visual appeal of the questionnaire and
computer interface to the respondent, among other factors. According to Dillman
(2000), the questionnaire design has two main objectives: to reduce non-response and
to reduce or eliminate measurement error. Structure and visual appeal can be equally
important. Dillman (2000) points out that while a respondent-friendly appearance and
a good structure can improve response rates, a poor questionnaire layout can cause
questions to be overlooked. Therefore, it is important to keep the wording and visual
appearance of questions simple.
In terms of survey structure, Dillman (2000) emphasizes that the order of the
questions is crucial. The questions should be grouped in a general way from the most
salient to the least salient to the respondent. Moreover, the order must be logical to
the respondent, as if a conversation were taking place. Therefore, before each group
of questions, a general explanation has been included in order to clarify the logic flow
of the questionnaire to the respondent. Also, special attention should be paid to the
first question, which can impact the desirability of the respondent to complete the
questionnaire. It should be appealing to the respondent. The question regarding the
degree of partnering was selected to be first, while the demographic information was
positioned at the end of the questionnaire.
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Once the survey instrument (questionnaire plus letters) was created, several
pretests were implemented. The pretests involved four steps:
Review by experts. The survey instrument was refined with the aid of
feedback provided by logistics experts (professors with knowledge of logistics
outsourcing research and doctoral students experienced in survey research) in order to
finalize substantive content. Experts in logistics with experience in survey research
can identify problematic questions in terms of response rate or understandability. The
objective in this phase was to assure that all necessary questions were included and
that they were consistent with prior studies.
Think-aloud interviews. A think-aloud interview is a common technique in
which the respondent answers the questionnaire in the presence of the interviewer and
is asked to tell the interviewer whatever he/she is thinking from the moment he/she
opens the email until the questionnaire is finished and sent. After reviewing the
survey for substantive content, a first series of think-aloud interviews was conducted
with three doctoral students to assess possible inconsistencies in wording and
structure. In other words, the objective was to evaluate whether the respondents could
understand and answer all questions, and whether the e-mails and questionnaire on
the website created a positive impression. After this first series of interviews, the
survey was revised and translated into Portuguese. In order to prevent possible
translation bias, a Brazilian marketing scholar who works in the U.S., thus being
fluent in both languages and in marketing, reviewed the original and translated the
survey instrument. Other professionals, who are knowledgeable in both Portuguese
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and English, as well as in transportation and logistics, kindly agreed to review the
survey translation. Minor modifications were needed.
Final check. Next, the website was created with special attention to the ease
and comfort of the user. The online survey instrument was designed in a way that
groups of questions were presented together. Therefore it resembled the experience of
using the Internet. It was possible to interrupt and return to the survey website for
completion at any time. Once the website was ready, it was completed by industry
professionals not involved in any phase of the development or review of the
questionnaire or website. Minor adjustments were made.
Pretest with a reduced sample. The final phase of the pretest involved
conducting the survey online with a reduced sample. The objective was to identify
operational problems in the software utilization by the respondents, as well as in the
implementation of the survey itself. Four hundred customers were randomly selected
from the customer base. Rapidão Cometa sent them an e-mail in which they were
invited to visit a website and provide their names and e-mails if willing to participate
in the survey. One hundred, eighteen emails were returned due to non-existent e-mail
addresses, implying that 282 firms received the invitation. Forty-three e-mail
respondents agreed to participate in the project and received the link to the website.
Sixteen respondents completed the questionnaire, corresponding to a response rate of
5.67%. No problems were encountered and no modifications were made to any part
the survey instrument.
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4.4. Survey implementation
A major objective of carefully planning the survey implementation is to
reduce the non-response rate. According to Dillman (2000), repeated contacts with
potential respondents have been shown to be the most effective strategy in increasing
the response rate. His “tailored design” method of implementation includes: a
“respondent-friendly” questionnaire, up to five contacts with the recipient, plus a
financial incentive sent with the survey request (in the present case, the chance of
“winning” an iPod).
Given that in the pretest no problems were encountered in administering the
survey, the next step was to follow the same process with the remaining customers in
the database. An important point was to make each contact with respondents unique,
since it has been shown that a variety of stimuli are generally more powerful than a
repetition of previously used techniques in increasing response rate (Dillman 2000). It
is also relevant to point out that during the contact period, attention was given to
sending individualized messages (not showing multiple recipient addresses or a
listserv origin).
The survey implementation activities can be summarized as follows:
First contact: pre-notification e-mail. According to Dillman, this is important
for Internet surveys, given the ease in discarding e-mail messages. Following
Dillman’s recommendations, the email was aimed at building anticipation rather than
providing the details and conditions for participation in the survey. In the study, the
first contact began with a pre-notification e-mail sent by Rapidão Cometa in order to
guarantee that our source was trustworthy, to emphasize the confidentiality of the
127
responses, and to express support for the study. In this e-mail, Rapidão Cometa
invited the firms to access a website (created by this researcher) and to provide their
e-mail addresses in order to participate in the study. The e-mail also included a brief
description of the study and its purpose. The e-mail was sent to a total of 2,649
customer firms
9
. Three hundred, thirty-five customer firms accepted Rapidão
Cometa’s invitation, provided their contact information, and received the link to the
website.
Second contact: e-mail with link to website. This e-mail was sent to the 335
firms that responded to Rapidão Cometa’s invitation. It was sent few days after the
pre-notification e-mail. The e-mail contained a letter describing the objective and
importance of the study, emphasizing the confidentiality of the responses. In addition,
the possibility of receiving a gift was indicated.
Follow-up contacts: thank you/reminder e-mails. Thank you e-mails were
sent to all firms that completely filled out the questionnaire. Reminder e-mails and
announcements of the gift winners were sent once a week during a four week period
to all contacts on the e-mail list. It is interesting to note that once winners were
selected and announced to the entire contact list, a temporary increase in the number
of respondent replies was observed.
In total, 265 firms filled out the survey completely, representing a response
rate of 79.1% of those that accepted Rapidão Cometa’s invitation (or 10.0 %, of the
entire customer base that received Rapidão Cometa’s invitation net of the emails that
bounced back).
9
In reality 4,123 e-mails were sent, of which 1,474 “bounced back” as being unknown e-mail
addresses.
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Short version of the survey for non-respondents. Finally, in order to test for
non-response bias, a short version of the survey composed of 13 theoretically
meaningful items was sent to two groups of non-respondents: 1) those who accepted
the invitation but did not fill out the survey completely, and 2) those who did not
accept Rapidao Cometa’s invitation. In total, 5 customers from the first group and 93
customers from the second group filled out the short version of the survey.
4.5. Conclusions
This chapter presented the research methodology used to test the hypotheses.
The measurement of the variables was defined. A web-based survey instrument was
developed and pre-tested prior to its final implementation. A short version of the
survey was also implemented with the objective to test for non-response bias. All
steps followed in the data analysis, along with the model results, are presented in
Chapter 5.
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Chapter 5: Data Analysis and Results
This chapter presents a detailed description of all steps followed to analyze the
data and test the hypotheses proposed in this study. First, the characteristics of the
respondents are examined, followed by the descriptive statistics of the variables and
constructs. Next, the tests for non-response bias are presented. Finally, all quantitative
procedures and tests conducted during the structural equation modeling process are
discussed, along with the model results.
5.1. Final sample and respondents characteristics
As outlined in the previous chapter, 16 firms completed the survey during the
pretest phase and 265 firms completed the survey during the survey implementation
phase. Given that no modifications were made in the survey instrument between the
phases, and given that the pretest and survey were implemented consecutively (i.e.,
during the months of August and September), the combination of both response
groups was considered as the final sample. In total, a final sample size of 281
observations was used.
The position profile of the respondents was fairly diverse. The respondents
were mostly logistics supervisors, logistics managers, general managers, CEOs, and
partners (see Table 3). Considering that these individuals were Rapidão Cometa’s
contacts for coordination of their activities, and they were professionals in the
management level, this might indicate that the respondents were knowledgeable about
their company’s relationship with Rapidão Cometa. This implies that key informant
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bias may not be a concern in this study. These firms belonged to a variety of
industries, such as (Table 4): apparel (18.5%), health care (6.4%), automotive and
auto parts (5.7%), electronics (5.7%), cosmetics (5.3%), telecommunications (4.3%),
food and beverage (5.0%), and others.
Table 3. Position profile of the respondents
Position Count %
President/CEO/COO 18 6.41%
Owner/Partner 19 6.76%
Logistics director 8 2.85%
Logistics manager 53 18.86%
Logistics supervisor 35 12.46%
Logistics employee 23 8.19%
Logistics Analyst 14 4.98%
General manager 29 10.32%
Procurement manager 16 5.69%
Director 7 2.49%
Sales supervisor 5 1.78%
Sales manager 2 0.71%
Other 24 8.54%
Not informed 28 9.96%
Total 281 100.00%
Table 4. Respondents’ Industries
Industry Count %
Apparel 52 18.51%
Health care/Pharmaceutical 18 6.41%
Auto/Auto Parts 16 5.69%
Electronics 16 5.69%
Cosmetics 15 5.34%
Food and Beverage 14 4.98%
Chemicals and Plastics 14 4.98%
Telecommunications 11 3.91%
Retail 11 3.91%
Service 11 3.91%
Other 77 27.40%
Not informed 26 9.25%
Total 281 100.00%
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Almost 75% of the sample was composed of small firms with fewer than 250
employees. Larger firms with more than 1,000 employees composed less than 10% of
the sample. The complete distribution is found in Table 5. The small size of the firms
in the sample can be also seen by observing their annual sales distribution
10
. Of the
respondent firms, 18.5% had annual sales of less then US$ 0.5 million, 31.3% of the
firms had annual sales that ranged from US$ 0.5 to US$ 4.3 million, and 10.3% of the
firms had annual sales ranging between US$ 4.3 to 11.4 million. The remaining
respondents had annual sales greater than US$ 11.5 million, of which only 6% had
annual sales greater than US$ 120 million.
Table 5. Number of employees of the respondent firms
Number of Employees Quantity %
Fewer than 100 144 51.25%
100 - 249 53 18.86%
250 - 499 19 6.76%
500 - 999 15 5.34%
1,000 - 2,499 13 4.63%
2,500 - 4,999 5 1.78%
5,000 - 9,999 3 1.07%
more than 10,000 1 0.36%
Not informed 28 9.96%
Total 281 100.00%
10
The “unusual” breakdown of sales categories is a result of converting from the Brazilian currency
“Real” (R$) to U.S. dollars.
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Regarding the respondents’ logistics outsourcing practices, more than half of
the sample (52.7%) outsourced only one logistics function, while about 5% of the
firms outsourced 6 or more functions. The remaining firms outsourced from 2 to 5
functions (Table 6). The vast majority of firms outsourced transportation operations,
which seems to be the strongest capability of the 3PL. Other outsourced functions
were transportation planning, freight consolidation, and distribution to final customer
(see details on Table 7).
Table 6. Number of functions outsourced
Number of
functions
# Firms %
1 148 52.67%
2 38 13.52%
3 27 9.61%
4 12 4.27%
5 9 3.20%
6 6 2.14%
7 3 1.07%
8 2 0.71%
9 1 0.36%
10 0 0.00%
11 1 0.36%
Not informed 34 12.10%
Total 281 100.00%
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Table 7. Respondents’ logistics functions outsourced
Activity # Firms
Transportation operations 216
Freight consolidation 48
Final consumer distribution 43
Freight bill payment 34
Warehousing 26
Reverse logistics 22
IT systems 14
EDI capability 14
Traffic control (distribution) 14
Transportation planning 13
Network/route optimization 8
Inventory management/control 7
Order management 7
Cross-docking 6
Traffic control (supply) 5
Packaging 3
After-sale service distribution 3
Pick & pack operations 2
Lead logistics management 1
5.2. Descriptive statistics of the constructs
Table 8 provides the means and standard deviations of the constructs
11
. It can
be noted that, in general, the variable averages were slightly above the central point of
the Likert scale (i.e., 4) and presented good variability. Customer Transaction
Specific Investments (Customer TSI) presented the highest standard deviation
(1.684). Relationship Marketing Orientation (RMO), however, presented a smaller
standard deviation compared to the other constructs. Since some of the variable
means were located to the right of the central point of the 7-point Likert scale, there is
11
The value of each construct for each observation was calculated as the average of the scale items.
134
an indication that some distributions might be skewed (to be tested in the following
sections). For this reason, the robust estimation technique might need to be employed
in order to correct for skewed data.
Table 8. Descriptive statistics of the constructs
Construct Mean
Std.
Dev.
Customer partnering behavior
behabehavior
4.587 1.131
Satisfaction 5.179 1.375
Credibility 5.867 1.210
Benevolence 4.990 1.556
Reputation 5.966 1.074
Customer dependence 3.772 1.358
Customer TSI 2.797 1.684
3PL TSI 3.436 1.623
3PL dependence 4.983 1.485
Volatility product market 3.198 1.491
Diversity product market 5.083 1.334
Volatility 3PL market 3.886 1.253
Diversity 3PL market 4.241 1.160
Logistics complexity 5.237 1.323
Logistics capabilities 4.787 1.648
RMO 5.892 0.803
Table 9 presents the correlation table between the constructs. It should be
noted that most of the statistically significant correlations had small values. Many
other correlations were not statistically significant, which should have implications
for the model fit to be tested later in this chapter.
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Table 9. Correlation matrix for the averages of the constructs
Construct
L
C
A
P
V
O
L
P
M
D
I
V
P
M
V
O
L
3
P
L
D
I
V
3
P
L
L
C
O
M
P
T
S
I
3
P
L
T
S
I
R
E
P
E
X
P
T
P
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S
A
T
D
E
P
3
P
L
D
E
P
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R
E
D
B
E
N
E
V
E
X
P
P
A
R
T
R
M
O
Customer logistics capabilities
Volatility product market -0.033
Diversity product market 0.052 0.154
Volatility 3PL market 0.043 0.219 0.144
Diversity 3PL market 0.032 0.078 0.234 -0.089
Logistics complexity 0.448 0.079 0.115 0.022 -0.009
Customer TSI 0.261 -0.114 -0.142 -0.171 -0.039 0.290
3PL TSI 0.282 -0.109 -0.084 -0.124 -0.054 0.377 0.716
3PL reputation 0.072 0.092 0.057 -0.045 0.173 0.125 0.105 0.208
Experience with 3PL 0.104 -0.058 0.047 -0.010 -0.034 0.162 0.156 0.159 0.081
Satisfaction 0.132 -0.030 0.030 -0.187 0.117 0.071 0.218 0.299 0.457 0.058
Dependence 0.169 -0.067 -0.025 -0.129 0.050 0.290 0.686 0.636 0.298 0.196 0.422
3PL dependence 0.315 -0.021 0.080 -0.055 0.150 0.360 0.235 0.413 0.260 0.086 0.332 0.357
3PL credibility 0.101 0.010 0.082 -0.148 0.138 0.177 0.151 0.247 0.573 0.077 0.460 0.313 0.231
3PL benevolence 0.263 -0.031 -0.096 -0.188 0.113 0.262 0.372 0.478 0.426 0.149 0.411 0.489 0.372 0.583
Experience partnering 0.203 0.000 0.044 0.010 0.030 0.069 -0.156 -0.145 0.076 0.096 0.008 -0.117 0.078 -0.019 -0.021
RMO 0.281 -0.021 0.076 -0.157 -0.068 0.320 0.178 0.127 -0.013 0.128 0.081 0.054 0.159 0.007 0.096 0.070
Customer partnering behavior 0.383 -0.039 -0.003 -0.127 0.122 0.318 0.508 0.533 0.388 0.168 0.352 0.565 0.360 0.370 0.522 0.125 0.128
Observation: The figures in bold are statistically significant at the 0.05 level.
136
5.3. Tests for non-response bias
Before any analysis can be performed, a test for non-response bias must be
conducted. Non-response bias occurs when the answers of the respondents are
statistically different from the answers of the non-respondents (Lambert and
Harrington 1990). Testing for non-response bias is critical to the generalizability of
the research findings.
Two standard methods were used to test for non-response bias in this study:
1
st
method: Comparison of early and late respondents. From the software it was
possible to identify the date when each respondent had finalized the survey
instrument. Figure 12 presents a graph with the counts of respondents per day. A first
wave of respondents was identified from September 2
nd
to 5
th
, comprising 94
respondents. This first group was considered as the early respondents group. The last
94 respondents were considered as the late respondents. The thirteen key non-
demographic questions provided in the short version of the questionnaire were
compared through a Two Group Hotelling T-Squared Test - Manova (Table 10). The
test showed no statistical significance between the vector of early and late
respondents. This result indicated that the null hypothesis that the vectors are equal
could not be rejected. Therefore an absence of response bias between early and late
respondents was inferred.
137
Figure 12. Daily counts of survey completion.
0
10
20
30
40
50
60
9
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/
2
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7
9
/
4
/
2
0
0
7
9
/
6
/
2
0
0
7
9
/
8
/
2
0
0
7
9
/
1
0
/
2
0
0
7
9
/
1
2
/
2
0
0
7
9
/
1
4
/
2
0
0
7
9
/
1
6
/
2
0
0
7
9
/
1
8
/
2
0
0
7
9
/
2
0
/
2
0
0
7
9
/
2
2
/
2
0
0
7
9
/
2
4
/
2
0
0
7
9
/
2
6
/
2
0
0
7
9
/
2
8
/
2
0
0
7
Table 10. Comparison of vector means between early vs. late respondents
Item
Mean
(Early)
Mean
(Late)
Extendedness 6.28 6.42
Operational Information
Exchange
4.42 4.11
Operating Controls 5.24 5.04
Sharing Benefits and Burdens 6.03 5.69
Planning 4.08 3.60
Dependence 5.43 5.65
3PL Dependence 5.65 5.35
3PL Credibility 6.09 6.22
3PL Benevolence 5.48 5.32
RMO 6.49 6.10
Customer TSI 2.37 2.63
3PL reputation 2.02 1.79
Satisfaction 5.15 5.40
2-group Hotelling’s T-squared = 19.056
F test statistic = 1.3612, p = 0.1841.
2
nd
method: According to Lambert and Harrington’s (1990) method of testing for
non-response bias, a random sample of the non respondents should be selected and
contacted to answer the same set of questions used when examining the early and late
138
respondents. Their results then should be generalized to the non-respondent
population.
An e-mail with the link to the short version of the survey was sent to the
customers who did not respond to Rapidão Cometa’s initial invitation to participate in
the survey. Seventy-five responses were collected. Again, the Two Group Hotelling t-
squared test was used to compare the vector means between the respondent and non-
respondent groups (Table 11). The null hypothesis that the vectors of means are equal
for the two groups could not be rejected. Therefore, the absence of non-response bias
was inferred.
Table 11. Manova comparison of vector means (respondents vs. non-respondents)
Item
Mean
(Respondents)
Mean (Non-
respondents)
Extendedness 6.31 6.37
Operational Information
Exchange
4.39 4.70
Operating Controls 5.18 4.89
Sharing Benefits and Burdens 5.89 5.67
Planning 4.02 4.03
Dependence 5.56 5.86
3PL Dependence 5.57 5.60
3PL Credibility 6.15 6.17
3PL Benevolence 5.53 5.59
RMO 6.32 6.14
Customer TSI 2.59 2.90
3PL reputation 1.85 2.00
Satisfaction 5.32 5.60
2-group Hotelling’s t-squared = 12.741
F-test statistic = 0.9439, p = 0.5075
139
5.4. Structural equation modeling
Following Ganesan (1994) and a substantial group of relationship marketing
researchers (e.g., Morgan and Hunt 1994, Hewett and Bearden 2001, Knemeyer 2000,
2004), structural equation modeling (SEM) was the statistical technique employed in
this study. SEM is a powerful multivariate technique that can be used to investigate a
priori specified, theory-derived, hypothesized correlations or causal relations among
latent, unobserved variables. SEM is a largely confirmatory, rather than exploratory,
technique.
The underlying logic of SEM is as follows: A structural equation model
implies a structure of the covariance matrix of the variables that are used as
measurement items
12
for the latent variables, or constructs (hence an alternative name
for this field, "analysis of covariance structures"). Once the model's parameters have
been estimated, the resulting model-implied covariance matrix can then be compared
to an empirical or data-based covariance matrix. If the two matrices are consistent,
then the structural equation model can be considered a plausible explanation for the
hypothesized relations between the measurement items.
Overview of the SEM process. The SEM process centers around two steps:
validating the measurement model and fitting the structural model. The former is
accomplished primarily through confirmatory factor analysis, while the latter is
accomplished primarily through path analysis with latent variables.
In the first phase, the measurement phase, a confirmatory model allowing
covariances among all construct and stand-alone variables (not intended as indicators)
12
The terms “measurement items,” “items,” and “variables” are used interchangeably.
140
is tested. The objective of the measurement model is to assess how well the indicators
serve as a measurement instrument for the latent constructs (Garver and Mentzer
1999). Thus, the objective is to identify and correct measurement error, ensuring the
correct interpretation of the results of the structural model (phase 2). In the
measurement phase, the constructs are tested for reliability, convergent validity, and
discriminant validity. Theoretically meaningful respecifications in the measurement
model might be necessary in order to obtain an adequate model fit.
After a reasonable model fit is achieved in the measurement model, and it is
shown that the constructs are reliable and valid, the second phase of SEM process, the
structural phase, can be initiated. Here the hypothesized path model is tested and the
model fit and structural paths are examined.
5.4.1. Data preparation and preliminary analysis
Before starting the SEM process, it is necessary to follow a few pre-steps that
involve an overview of the quality of the data and data preparation (i.e., assessment of
unidimensionality and item cleaning).
Quality of data evaluation
Before utilizing the SEM software (EQS in this dissertation), a preliminary evaluation
of the quality of the data was conducted.
Outliers. All variables, which correspond to the measurement items, were checked for
obvious univariate outliers using box plots. No outliers were found.
Normality. The univariate distributions for each variable were checked for symmetry
through histograms. Many of the variables were shown to be skewed to the left (i.e.,
141
most observations fell on the right side of the scale). For this reason, the robust
estimation procedure in EQS, which accounts for non-normal data, was utilized. The
robust procedure corrects the maximum likelihood model ?
2
statistic and the standard
error to adjust for non-normal data. This was preferred to transforming the data, given
that a disadvantage of data transformation is that the new variable is no longer a
direct representation of the underlying construct.
Missing data. The data set was checked for the presence of missing data. There were
a few instances of missing data, which was expected given that the original
questionnaire had more than 100 questions. Although the cases and variables in
which the missing data occurred were random, it was decided to run the model with
the complete observations only. Substituting the missing data with the mean values of
the variables in question can lead to under-representation of the variance of the
population. Also, using pairwise deletion to generate variances and covariances can
lead to convergence problems and bias in the results.
Undimensionality
Before testing the model fit and the hypothesized relationships in the
structural phase, the set of variables (i.e., measurement items) for each of the
constructs in the model need to be tested for unidimensionality, reliability and
validity (Garver and Menzer 1999). Once unidimensionality has been established,
construct validity and reliability can be investigated.
Unidimensionality is “the degree to which items represent one and only one
underlying latent variable” (Garver and Mentzer 1999). Unidimensionality was
assessed with the aid of exploratory factor analysis. Reliability, convergent and
142
discriminant validity were assessed in the measurement phase of the structural
equation modeling process.
As most constructs in this dissertation were adapted from prior research, it
was expected that the measurement items would have high reliability and validity.
However, many of the constructs were originally used in fields other than logistics
(e.g. marketing and strategy). Therefore, it was still necessary to closely examine the
items comprising the constructs.
Initially, exploratory factor analysis (principal component analysis with
Varimax rotation,) was conducted for each construct to assess unidimensionality.
Varimax rotation maximizes the variance of squared loadings in the columns of the
structure matrix. Therefore it provides a simpler and clearer structure of loadings.
Those items that loaded weakly (e.g. less than 0.4) were removed from the scale,
while still ensuring content validity of the construct. Of the 88 items comprising 18
constructs, 79 loaded highly on their factors while 7 items were removed from the
scales due to low loadings.
Partial disaggregation
SEM might encounter convergence problems in models in which constructs
have many indicators (Knemeyer 2000). In models with factors with many items,
employing the traditional structural equation approach “can be unwieldy because of
likely high levels of random error in typical items and the many parameters that must
be estimated” (Bagozzi and Heartherton 1994, pp. 42-43). This can be especially true
in the case of the present model which is fairly complex, with many of the constructs
having six, seven, or more items.
143
In order to correct for this potential problem, partial disaggregation was
adopted in this dissertation. Partial disaggregation is operationally accomplished by
randomly assigning items of a construct into composites. These composites then
become the new measurement items. The process is conducted in a way that each
factor has no more that 3 combined indicators instead of many indicators. The
rationale of partial disaggregation is that all items related to a factor should
correspond in the same way to that latent factor; therefore any combination of these
items should yield the same model fit (Dabholkar et al 1996). The advantage of
partial disaggregation is that the multivariate aspect of the model tested is maintained
while the model is simplified and the levels of random error are reduced. In this
study, the items were randomly allocated to composites for the constructs that had
more than four items after the initial item cleaning.
5.4.2. Measurement phase
The objective of the measurement phase is to isolate model misspecification
and to verify that the measures adopted appropriately represent the latent constructs in
the model. Syntax was written for the confirmatory model allowing covariances
among all constructs and stand-alone variables (not intended as indicators). By
allowing all factors to co-vary, the structural portion became just identified (thus with
a perfect fit), and the measurement part of the model could be assessed.
The robust fit indices obtained for the measurement model were: ?2 =
1883.013 (df = 1279), CFI = .878, RMSEA = .045 and SRMR = .070. The ?2 statistic
was statistically significant. It is noted that RMSEA and SRMR indices presented
144
good fits. The CFI index, however, was marginally significant (threshold is .90). The
fit indices indicate that the data covariance matrix has a relatively good fit.
Convergent validity
Convergent validity refers to the extent that the items of the factor capture the
content of the construct. Two standard means of assessing convergent validity are: 1)
by examining whether the factor loadings of the measurement equations (that explain
all variables as a function of the factor) are positive and statistically significant, and;
2) by calculating the “variance extracted” by the construct, which corresponds to the
mean squared standardized loading. Ideally it should exceed .50 (Garver and Mentzer
1999).
Convergent validity was checked by both methods. By examining the
software output, it was noted that all loadings were positive and statistically
significant. It was thus inferred that convergent validity exists. In addition, the
“variance extracted” was calculated for all constructs (see Table 12). Out of 18
constructs, four fell significantly below the desired 0.50 threshold. Another four also
fell below the threshold but were very close to 0.5. Given that the PCA results
showed that these items did load on a single factor, it was decided not to eliminate or
rearrange the items used for these four constructs with low convergent validity.
145
Table 12 Variance extracted of the constructs
Construct
Variance
extracted
Logistics capabilities 0.685
Volatility product market 0.401
Diversity product market 0.582
Volatility 3PL market 0.342
Diversity 3PL market 0.651
Logistics complexity 0.498
Customer TSI 0.643
3PL TSI 0.451
3PL reputation 0.371
Experience with 3PL 1.000
Satisfaction 0.804
Customer dependence 0.542
3PL dependence 0.463
3PL credibility 0.737
3PL benevolence 0.721
Experience partnering 1.000
RMO 0.469
Partnering behavior 0.391
* The variance extracted for “Experience with 3PL”
and “Experience partnering” are 1 given that they
were measured by a single indicator.
Discriminant validity
Another test conducted in the measurement phase consisted of examining the
discriminant validity of the constructs; i.e., verifying that the items loaded on the
construct of interest and not on other constructs. According to several authors (Shook
et al 2005, Kline 2005, p. 182), achieving a good fit for the model in which each
indicator loads on only one factor provides a precise test of discriminant validity.
The measurement model presented reasonable fit indices; thus it was inferred
that discriminant validity existed. In addition, the factor covariances were fairly small
in the vast majority of cases and non-significant in many cases as well. This fact also
146
diminished concerns that factors assumed as independent were in reality a single
factor (i.e., not discriminant).
Shook et al (2005) indicate that an alternative method for testing for
discriminant validity is to calculate the shared variance between constructs and verify
that it is lower than the average variance extracted for each individual construct. This
procedure was conducted for all pairs of constructs. All but three pairs (TSI –
3PLTSI, TSI – DEP, 3PLTSI – DEP) did pass this test. Therefore, for these three
pairs, a fit comparison of nested models was conducted. Models with correlations
between the two factors set equal to 1 (i.e., where the two factors are considered a
single, unique factor) were compared to models where the two factors were free to
correlate. Given that the difference in ?
2
was statistically significant for all three pairs
(see Table 13), the existence of discriminant validity was inferred.
Table 13. Test for discriminant validity for construct pairs with high covariance
Single factor
model
Two factor
model
Fit difference
Construct pairs
? ?? ?
2
df ? ?? ?
2
df ? ?? ?? ?? ?
2
? ?? ?df
Stat. sig
at 5%
level?
Discrim.
Validity
?
TSI - 3PL TSI 62.765 14 51.178 13 11.587 1 Yes Yes
TSI - DEP 143.134 20 63.352 19 79.782 1 Yes Yes
3PL TSI - DEP 61.199 14 16.372 13 44.827 1 Yes Yes
Scale reliability
Scale reliability refers to the internal consistency of a particular scale to
measure a latent variable (Garver and Mentzer 1999); i.e., indicates whether a factor
is expected to be stable and replicable. Garver and Mentzer (1999) point out that the
coefficient alpha, the traditionally adopted measure of reliability, has some
147
limitations. In some cases, it tends to underestimate the scale reliability or become
inflated when the construct has a larger number of items. They suggest the use of
SEM reliability measures, such as the variance extraction measure and the SEM
“Reliability of the Construct” measure. Following their recommendations, SEM
measures of reliability were taken into consideration.
The “variance extracted” was calculated for all constructs (Table 12 above)
and most constructs had values above the recommended figure of 0.5. In addition, the
coefficient “Maximal Reliability”, Coefficient H developed by Hancock and Mueller
(2001), a measure of construct reliability, was calculated. Hancock and Mueller
(2001) argue that the traditional “Reliability of the Construct,” RC, developed by
Fornell and Larcker (1981) has some limitations: 1) its value is affected by loading
signs; 2) it is decreased by additional indicators if those have small loadings; 3) it can
be smaller than the reliability (squared loading) of the best indicator. Table 14, below,
presents the coefficient H for each construct. All values were found to be above the
0.7 threshold.
Given that the measurement model has been assessed in terms of fit and
convergent and discriminant validity, the next step was to test the structural model
where the theoretical links are investigated.
148
Table 14. Construct reliability results
Construct
Construct
Reliability
(H)
LCAP 0.921
VOLPM 0.816
DIVPM 0.737
VOL3PL 0.515
DIV3PL 0.830
LCOMP 0.797
TSI 0.892
3PLTSI 0.730
REP 0.781
SAT 0.938
DEP 0.876
3PLDEP 0.875
CRED 0.919
BENEV 0.948
RMO 0.848
PART 0.801
5.4.3. Structural phase
In the second phase, a new EQS program was written for the confirmatory
model. All independent constructs were allowed to correlate. The disturbances of the
construct pairs credibility/benevolence and dependence/3PL dependence were
allowed to correlate as well.
The following steps were followed:
Check of goodness-of-fit information. There are a dozen fit indices that are used to
assess the fit of structural equation models. Because there are so many options,
different articles report different indices and reviewers may request different fit
indices that they know or prefer (Kline 2005). Kline (2005) recommends the
149
following set of indices that reflect “the current state of practice and
recommendations about what to report in written summary of the analysis” (p. 134):
1) The model chi-square (?
2
): The chi-square statistic compares the observed and
the model-implied covariance matrices. Since the objective is that these two
matrices are similar, a non-significant chi-square is desired. However, it is a
very powerful test that can detect small discrepancies in the data. Therefore, it
is likely that the this statistic will be significant; i.e., will predict that the
model does not fit the data;
2) The Steiger-Lind root mean square error of approximation (RMSEA):
RMSEA is a fit index that evaluates the overall discrepancy between observed
and model implied (co)variances, while taking into account the model’s
simplicity. It improves as more parameters are added to the model, as long as
those parameters are making a useful contribution. It is a “badness-of-fit”
index, which means that a value of zero indicates the best fit. Values less than
0.06 are considered acceptable;
3) The Bentler comparative fit index (CFI): CFI is a data-model fit index that
evaluates the improvement in the model’s fit relative to a baseline model,
usually the independence model (also called null model). The independence
model is the worst possible model, in which there are no relationships in the
data (i.e., population covariances among observed variables are zero). A rule
of thumb is that CFI values greater than roughly 0.90 are considered
acceptable (Kline 2005);
150
4) The standardized root mean squared residual (SRMR): SRMR is a measure of
the mean absolute squared residual; i.e., the overall difference between
observed and predicted correlations. Values of SRMR less than 0.10 are
considered acceptable (Kline 2005).
This study follows Kline’s (2005) recommendations and uses these four
indices (?
2
, SRMR, RMSEA, and CFI) to assess the model fit. As expected, the ?
2
was statistically significant, which indicates that the model does not have a good fit.
This does not undermine the fit evaluation. As with the measurement model, the
values for RMSEA and SRMR fit indices fell within the desired range (see Table 15).
The CFI index, however, was marginally below the 0.90 threshold. The model fit was
considered to be marginally acceptable.
Table 15. Summary of fit indices for the full model
Chi-square CFI RMSEA SRMR
Desirable range > 0.9 < 0.06 < 0.10
Full model 1994.388 (df = 1328) 0.865 0.047 0.075
Check of inter-factor path coefficients. With the model presenting a marginal level
of acceptance, the structural paths were examined for theoretical and practical
implications. Table 16 provides an overview of the standardized solution of the
structural model. The first part of the table presents the primary antecedents of
customer partnering behavior, followed by the antecedents of dependence and
antecedents of trust.
151
Table 16. Standardized path coefficients.
Hypothesis Relation Full
model
Primary Hypotheses
1 Customer dependence ? Partnering 0.374*
2 3PL dependence ? Partnering 0.095
3 3PL credibility ? Partnering 0.027
4 3PL benevolence ? Partnering 0.245*
5 Partnering experience ? Partnering 0.206*
6 RMO ? Partnering 0.216*
7 Satisfaction ? Partnering 0.06
Antecedents of dependence
8 Customer capabilities ? Dependence -0.140*
9 Environmental diversity 3PL ? Dependence -0.043
10 Environmental volatility 3PL ? Dependence -0.056
11 Environmental diversity product market. ? Dependence 0.056
12 Environmental volatility in product market ? Dependence 0.025
13 Logistics Complexity ? Dependence -0.041
14 TSI by customer ? Dependence 0.234
15 TSI by customer ? 3PL dependence -0.639*
16 3PL TSI ? Dependence 0.668*
17 3PL TSI ? 3PL dependence 1.114*
Antecedents of trust
18 3PL TSI ? Credibility 0.217*
19 3PL TSI ? Benevolence 0.454*
20 Reputation ? Credibility 0.273*
21 Experience with 3PL ? Credibility -0.049
22 Experience with 3PL ? Benevolence 0.028
23 Satisfaction ? Credibility 0.271*
24 Satisfaction ? Benevolence 0.248*
Obs.: The figures indicated by * are significant at the 5% level.
5.5. Results
In this subsection, the model results are discussed in light of the hypotheses
proposed. Figure 13, below, presents a diagram with the statistically significant paths
and Table 17 presents the model results and support for the hypotheses. A more
detailed discussion of the implications of the results is found in Chapter 6.
152
Figure 13. Statistically significant path coefficients.
Customer
partnering behavior
Customer
partnering behavior
Perception of 3PL’s
dependence on customer
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Prior experience
3PL partnering
Prior experience
3PL partnering
Relationship Marketing
Orientation
Relationship Marketing
Orientation
Env. diversity
3PL market
Env .volatility
3PL market
Customer’s
experience with 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
TSI by customer
Satisfaction with
previous outcomes
Customer
capabilities
Logistics
complexity
Env. diversity
product market
Env. volatility
product market
Env. diversity
3PL market
Env. diversity
3PL market
Env .volatility
3PL market
Env .volatility
3PL market
Customer’s
experience with 3PL
Customer’s
experience with 3PL
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
Customer
capabilities
Logistics
complexity
Logistics
complexity
Env. diversity
product market
Env. diversity
product market
Env. volatility
product market
Env. volatility
product market
0.374*
0.216*
0.206*
0.245*
- 0.140*
- 0.639*
0.668*
1.114*
0.217*
0.454*
0.271*
0.248*
R
2
= 0.517
R
2
= 0.385
R
2
= 0.352
R
2
= 0.467
R
2
= 719
0.273*
153
Table 17. Summary of Results
Hypothesis Relation Full model
Support/
Nonsupport
Primary Hypotheses
1 Customer dependence ? Partnering positive, significant supported
2 3PL dependence ? Partnering nonsignificant not supported
3 3PL credibility ? Partnering nonsignificant not supported
4 3PL benevolence ? Partnering positive, significant supported
5 Partnering experience ? Partnering positive, significant supported
6 RMO ? Partnering positive, significant supported
7 Satisfaction ? Partnering nonsignificant not supported
Antecedents of dependence
8 Customer capabilities ? Dependence
negative,
significant
supported
9
Environmental diversity 3PL ?
Dependence
nonsignificant not supported
10
Environmental volatility 3PL ?
Dependence
nonsignificant not supported
11
Environmental diversity product
market. ? Dependence
nonsignificant not supported
12
Environmental volatility in product
market ? Dependence
nonsignificant not supported
13 Logistics Complexity ? Dependence nonsignificant not supported
14 TSI by customer ? Dependence nonsignificant not supported
15 TSI by customer ? 3PL dependence
negative,
significant
supported
16 3PL TSI ? Dependence positive, significant not supported
17 3PL TSI ? 3PL dependence positive, significant supported
Antecedents of trust
18 3PL TSI ? Credibility positive, significant supported
19 3PL TSI ? Benevolence positive, significant supported
20 Reputation ? Credibility positive, significant supported
21 Experience with 3PL ? Credibility nonsignificant not supported
22 Experience with 3PL ? Benevolence nonsignificant not supported
23 Satisfaction ? Credibility positive, significant supported
24 Satisfaction ? Benevolence positive, significant supported
Antecedents of customer partnering behavior. The primary hypotheses
proposed that, with the exception of perceived 3PL dependence, all primary
antecedents (i.e., customer dependence on a 3PL, 3PL credibility, 3PL benevolence,
154
customer experience with partnering, customer relationship marketing orientation,
and satisfaction with previous outcomes) have a positive effect on customer
partnering behavior. In other words, it was proposed that a customer with higher
levels of dependence on a 3PL, trust in a 3PL’s credibility and benevolence,
satisfaction with a 3PL, relationship marketing orientation, and satisfaction with the
relationship, will exhibit higher levels of partnering behavior.
Examining the signs and statistical significance of the structural paths linking
these constructs provides information on whether the hypotheses are supported. It was
found that there is a statistically significant positive relationship between a
customer’s dependence on a 3PL (H1), a customer’s trust in a 3PL’s benevolence
(H4), a customer’s experience with partnering (H5), a customer’s relationship
marketing orientation (H6), and a customer’s partnering behavior. Hypotheses H1,
H4, H5, and H6 were supported.
The paths linking a 3PL’s dependence (H2), a 3PL’s credibility (H3) and
satisfaction with previous outcomes (H7), to a customer’s partnership behavior were
not statistically significant. Therefore, these hypotheses were not supported.
These findings indicate that both interorganizational conditions (i.e., customer
dependence and customer’s trust in a 3PL’s benevolence) and firm specific factors
(i.e., customer partnering experience and a customer’s relationship marketing
orientation) play a role in shaping a customer’s perceived partnering behavior with a
3PL. It can also be observed from the magnitude of the standardized path coefficients
that interorganizational factors, especially dependence, have a stronger influence than
firm specific factors (i.e, customer’s partnering experience and relationship marketing
155
orientation). The surprising finding that 3PL credibility had no significant influence
on customer partnering behavior might indicate that the interpersonal relationships
between a 3PL representative and his/her customer cannot be underestimated and is
crucial in shaping a customer’s trust. These four constructs explained almost 52% of
partnering behavior’s variance.
Antecedents of customer dependence. The hypothesized antecedents of
customer dependence are related to a customer’s internal logistics capabilities, its
competitive and operational environment, a 3PL’s competitive environment, and
transaction specific investments (TSI) by both customers and the 3PL. It has been
proposed that a customer with higher levels of internal logistics capabilities (H8)
perceives itself to be less dependent on a 3PL. It was also hypothesized that a
customer will perceive itself to be less dependent on a 3PL if the 3PL is immersed in
a diverse environment (H9) and invests in their relationship (H16).
It was also proposed that a customer’s dependence on a 3PL increases if a
firm is immersed in a diverse (H11) and volatile market (H12), with complex logistics
operations (H13), and when the customer invests in its relationship with the 3PL
(H14). Volatility in the 3PL market (H10) was also hypothesized to have a positive
relationship with customer dependence.
Surprisingly, for the sample under study, competitive pressures, operations
complexity or lack of alternatives (i.e., H9, H10, H11, H12, and H13) and TSI by
customer (H14) do not have a statistically significant effect on a customer’s perceived
dependence on a 3PL. Only a customer’s logistics capabilities (H8) and TSI by 3PL
(H16) had statistically significant results.
156
It was found that there is a negative relationship between a customer’s
logistics capabilities and a customer’s dependence on a 3PL (H8 supported). This
means that when a customer has a greater understanding of the management of its
logistics operations, a customer will perceive itself to be less dependent on a 3PL. A
(strong) positive effect was found between a TSI done by a 3PL and a customer
perceived dependence on a 3PL, which is the opposite effect that was hypothesized
(H16 not supported). This finding means that when a 3PL invests in a relationship
with a customer, this customer perceives itself to be more dependent on the 3PL. This
point is very important. The traditional resource dependence rationale is based on an
adversarial point of view – dependence asymmetry. If a firm perceives its partner to
be dependent, a firm’s level of dependence is reduced. The unexpected findings might
suggest that a customer that perceived the 3PL to be investing in their relationship
perceives itself to be more dependent on the partner. This might suggest that they are
more loyal to trade partners that invest in a relationship, or may be an indication that
they perceive that no other 3PLs would be willing to make such investment on their
behalf.
Antecedents of 3PL dependence. Two hypotheses were presented for the
antecedents of 3PL dependence. First, it was hypothesized that a customer will
perceive the 3PL to be more dependent on it when the 3PL invests in the relationship
(H17). Second, it was hypothesized that a customer will perceive the 3PL to be less
dependent on it when the customer invests in the relationship (H15). Both hypotheses
were found to be statistically significant and in the expected direction. It was found
that there is a negative relationship between TSI by a customer and 3PL dependence
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(H15 supported) and a positive relationship between 3PL TSI and 3PL dependence
(H17 supported).
Antecedents of trust. TSI by the 3PL have a significantly positive impact on
both credibility and benevolence (H18 and H19 supported). Reputation has a
significantly positive impact on credibility (H20 supported). There was no
statistically significant link found between experience with 3PL and credibility and
benevolence (H21 and H22). Finally, satisfaction significantly impacts both
credibility and benevolence (H23 and H24 supported). Therefore, given that the direct
link between satisfaction and customer partnering behavior satisfaction (H7) was not
found, this implies that satisfaction does not lead directly to customer’s partnering
behavior, but indirectly through the building of trust.
Contrasting the results with Ganesan’s model of long term orientation.
Eventhough Ganesan’s (1994) model has a different dependent variable (i.e., retailer
long term orientation in its relationship with a vendor) than the one adopted in this
dissertation, it is useful to identify which results were consistent (or inconsistent) to
Ganesan’s findings. Regarding the antecedents of the long-term orientation, the
present model was consistent to Ganesan’s solely regarding the customer’s
dependence (equivalent to retailer’s dependence on Ganesan’s model). Satisfaction,
credibility, and 3PL dependence, which were significant in Ganesan’s model, were
not found to be significant in the present model.
Regarding the antecedents of dependence, only the effects of transaction
specific investments by the customer and 3PL on customer dependence were
consistent to Ganesan’s findings. There is an interesting point to highlight here. In
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both models, it was hypothesized that transaction specific investments by the vendor
(or 3PL) would have a negative effect on a firm’s dependence. In both models, the
results were the opposite as expected (i.e., transaction specific investments by a
vendor have a positive effect on a firm’s dependence) and significant.
Regarding the antecedents of trust, both models presented very similar findings.
It was found a positive effect of reputation on credibility in both models. Also in both
models, prior experience with the vendor (or 3PL) was not found to have a significant
effect on credibility and benevolence. Transaction specific investments by a vendor
(or 3PL) had a positive effect on both credibility and benevolence. However,
satisfaction was found to directly impact credibility and benevolence in the present
model, but not in Ganesan’s model. In his case, satisfaction directly impacted the
dependent variable. In the model of this dissertation, satisfaction impacts the
dependent variable mediated by trust.
As a conclusion, it can be said that Ganesan’s contentions were generally
validated although the dependent variable of this dissertation (partnering behavior) is
a broader description of relational behavior, as opposed to Ganesan’s long term
orientation, which is a single dimension of relational behavior. The model presented
in this dissertation contributes to the previous model by providing evidence that other
firm specific characteristics, such as prior experience with partnering, relationship
marketing orientation, and capabilities, do impact relational behavior as well.
5.6. Conclusions
This chapter presented the procedures followed in order to analyze the data
and the results from the formal tests of the hypotheses. The process started with
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testing for non-response bias and a preliminary analysis of the data. The steps
following included the analysis of the measurement model, including construct
reliability, discriminant validity, and convergent validity. Finally the structural model
was analyzed and the results presented. The next chapter presents the conclusions and
discussion of the results, along with an overview of the contributions of the study,
limitations, and directions for future research.
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Chapter 6: Discussion and Concluding Remarks
This chapter comprises four main topics. First, an overall discussion of the
model results is presented. Second, the contributions of this dissertation to the
academic literature and managerial implications are examined. Third, the limitations
of this study are addressed. Next, the directions for future research are outlined.
Concluding remarks finalize the chapter.
6.1. Discussion of model results
The objective of this dissertation was to develop a model of the determinants
of customer partnering behavior in logistics outsourcing relationships. Customer
partnering behavior in the relationship with a 3PL is defined as the customer’s
perception that this relationship presents five key behavioral elements (Gardner et al
1994): planning, sharing benefits and burdens of the relationship, systematic
operational information exchange, and mutual operating controls. Developing close
relationships between 3PLs and customers has been shown to bring them many
benefits, such as: 1) increased customer’s performance (Knemeyer and Murphy 2004)
and market share (Stank et al 2003), and 2) greater levels of customer retention,
service recovery, and referrals to new customers (Knemeyer and Murphy 2005).
The hypotheses that compose the model were developed based on theories and
empirical evidence in the marketing, logistics, and strategy literatures. The model was
tested following established statistical procedures and Figure 14 depicts the simplified
model comprising solely the statistically significant structural paths. Overall, the
model findings support the contention that interorganizational conditions created
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through the relationship interactions (i.e. trust, dependence, and satisfaction)
combined with firm specific factors (i.e. experience with partnering and customer
relationship marketing orientation) influence a customer partnering behavior with a
3PL. The interorganizational conditions are influenced also by both firms specific
characteristics (e.g., customer’s logistics capabilities, 3PL reputation), and both firms
actions towards the relationship (i.e., transaction specific investments). In the
paragraphs that follow, the results obtained from the data analysis are discussed in
detail.
Figure 14. A simplified model of customer partnering behavior in logistics outsourcing
relationships
13
Antecedents of customer partnering behavior. The antecedents of customer
partnering behavior in its relationship with a 3PL are related to interorganizational
13
The shaded constructs represent the validated extensions to Ganesan’s (1994) model of the
antecedents long term orientation in buyer-seller relationships.
Customer
partnering behavior
Perception of 3PL’s
dependence on customer
Dependence of
customer on 3PL
3PL’s credibility
(trust)
3PL’s credibility
(trust)
3PL’s benevolence
(trust)
3PL’s benevolence
(trust)
Prior experience
3PL partnering
Relationship Marketing
Orientation
Reputation of
the 3PL
Reputation of
the 3PL
Perception of
TSI by 3PL
Perception of
TSI by 3PL
TSI by customer TSI by customer
Satisfaction with
previous outcomes
Satisfaction with
previous outcomes
Customer
capabilities
0.374*
0.216*
0.206*
0.245*
- 0.140*
- 0.639*
0.668*
1.114*
0.217*
0.454*
0.271* 0.248*
R
2
= 0.517
R
2
= 0.385
R
2
= 0.352
R
2
= 0.467
R
2
= 719
0.273*
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conditions and customer specific characteristics. The model identified four main
antecedents of customer partnering behavior:
- The perception of a customer’s dependence on a 3PL (H1);
- A customer’s trust in a 3PL’s benevolence (H4);
- A customer’s prior partnering experience with other 3PLs (H5), and;
- A customer’s relationship marketing orientation (H6).
The model provided support for the contention that higher levels of customer
dependence lead to higher levels of customer partnering behavior (H1). It was also
found that the perception that a 3PL depends on the customer does not influence a
customer partnering behavior (H2 not supported). In other words, a customer will be
willing to exchange information, engage in joint planning, and share benefits and
burdens of the relationship, when it perceives itself to be dependent on the 3PL’s
expertise in providing logistics services. This occurs regardless of whether the
customer perceives itself to be a major customer of the 3PL (i.e., when the customer
perceives the 3PL to be dependent on its business relationship).
The model also supported the hypothesis that a customer’s trust in a 3PL’s
benevolence positively affects a customer’s partnering behavior (H4). This means
that when a customer perceives the 3PL to care for the relationship and to be willing
to make sacrifices for the relationship, a customer will be more likely to exhibit a
partnering behavior with a 3PL. Indeed, during semi-structured interviews conducted
in December of 2006 with customers of Rapidão Cometa, it was evident that
customers very much appreciated the weekly visits conducted by Rapidão Cometa’s
representatives and the personal and close relationship developed between them. In
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the event of operational problems and difficulties, all interviewed customers agreed
that Rapidão Cometa’s representatives were very active in assisting them.
Surprisingly, support was not found for the contention that a customer’s
perception of a 3PL’s credibility, i.e., reliability and consistency of behavior,
positively impacts a customer’s partnering behavior (H3). This result implies that the
belief in a 3PL ability to efficiently perform does not directly impact the customer’s
partnering behavior. To some extent, however, this dimension is captured by
customer satisfaction (discussed later in the antecedents of trust).
The model found strong support for the contention that customer specific
characteristics play an important role in shaping a customer’s partnering behavior
with a 3PL. It was found that a customer’s prior experience with other 3PLs (H5) and
a customer’s relationship marketing orientation (H6) positively affect a customer’s
partnering behavior. The first result indicates that firms that are more experienced in
partnering with a logistics provider organization may be better at implementing and
maintaining close and interactive relationships. In addition, the strategy a firm
embraces with regards to its own customers will influence the nature of the
relationship with the 3PL. Therefore, it is crucial that 3PLs investigate the history and
relationship practices of potential customers before incurring investment costs to
build relationships.
Antecedents of dependence. This sub-model presented the most surprising
results. Out of the eight hypothesized antecedents of customer dependence on a 3PL,
only two paths were statistically significant: the perception of a customer’s internal
logistics capabilities (H8) and the transaction-specific investments (TSI) performed
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by the 3PL (H16). The model found a negative relationship between a customer’s
logistics capabilities and the perceived dependence on a 3PL, providing support for
H8. The result supported the contention that when a firm perceives itself to be
knowledgeable about its logistics processes, it may believe itself to be less dependent
on a 3PL. An unexpected finding was related to the effect of TSI by 3PL on customer
dependence. It was hypothesized that when a customer perceives that a 3PL has
invested in their relationship, the customer would believe itself to be less dependent
on a 3PL. The rationale was that the 3PL would have incurred relationship costs, thus
creating exit barriers for the 3PL. Interestingly, the result was the opposite. It was
found that a customer that believes a 3PL has invested in a relationship feels that it is
more dependent on the 3PL. This might indicate that the customer has become more
loyal to the 3PL, or that the customer perceives that it would have difficulties finding
another 3PL that would make the same investments. During the semi-structured
interviews conducted with customers of Rapidão Cometa, anecdotal evidence was
found for this contention. One motorcycle manufacturer indicated that Rapidão
Cometa built and attached racks in their trucks to load motorcycles. Due to Rapidão
Cometa’s initiative and willingness to assume the costs of the racks, the manufacturer
reduced costs by not requiring heavy and expensive packaging. Another example is
related to Rapidão Cometa’s investments in its relationship with a large cosmetics
company. Rapidão Cometa assumed the costs for the “kits assembly” (packaging)
equipment that was installed in the manufacturer’s distribution center.
Interestingly, none of the factors related to environmental pressures in the
product market or in the market for 3PL services (H9, H10, H11, and H12) had an
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impact on the perceived customer dependence. As well, the complexities of logistics
operations showed no influence on customer dependence (H13).
Both hypothesized antecedents of perceived 3PL dependence on a customer
were supported. It was found that TSI by customer had a negative impact on
perceived 3PL dependence (H15) and that TSI by the 3PL had a positive effect on the
perceived 3PL dependence on a customer (H17). These results have limited interest in
the context of the overall model, since 3PL dependence had no significant effect on
customer partnering behavior (see main antecedents of partnering, above).
Antecedents of trust. With the exception of customer experience with 3PLs,
all proposed antecedents of both dimensions of trust, i.e., credibility and benevolence,
were supported. Transaction-specific investments (TSI) by the 3PL positively impact
a customer’s perception of a 3PL’s credibility and benevolence. The crucial
importance of 3PL investments are noted in that they ultimately influence a
customer’s partnering behavior. Not only does a customer perceive itself to be more
dependent on the 3PL, but it also believes the 3PL to be efficient and to care for the
relationship. As noted in the antecedents of dependence subsection above, customers
greatly appreciate Rapidão Cometa’s investments in their relationships. These
investments constitute, therefore, tangible demonstrations of benevolence. A 3PL’s
reputation in the market was also found to have a positive effect on a 3PL’s
credibility. Therefore it is crucial for 3PLs not only to invest in advertising, but,
especially, to build strong reputations through excellence of service. Reputation may
be disseminated via word-of-mouth communication. Satisfaction with previous
outcomes of the relationship also had a positive effect on credibility (H23) and
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benevolence (H24). Given that the direct link between satisfaction and customer
partnering behavior was not found to be statistically significant (H7), the results of
the model indicates that the effect of satisfaction on partnering behavior is indirect
through the building of trust.
6.2. Contributions
This research provides contributions to both academics and practitioners. As
the following paragraphs describe, contributions have been made to the logistics and
marketing fields, as well as to managers.
Contributions to the logistics literature: The main contribution of this
dissertation to the logistics literature is the development and testing of a theory-based
model. Most logistics outsourcing literature has been exploratory in nature and there
have been few examples of theory testing (Maloni and Carter 2005). This dissertation
provides and tests a theoretical framework of the conditions under which partnerships
between 3PLs and customers will more likely occur. A second contribution to the
logistics literature related to the integrative nature of the model that combines theories
and findings from other disciplines, such as marketing and strategy. More
specifically, rationales borrowed from network theory, the capabilities perspective,
and the strategic orientation perspective were combined with social exchange theory
in the model.
Another contribution of this dissertation to the logistics outsourcing literature
is its focus on the antecedents of partnering behavior. As noted in the literature
review, the few examples of theory testing in the logistics outsourcing literature have
focused on other aspects of these relationships, e.g., the positive effects of logistics
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outsourcing relationships on customer and 3PL performance (e.g. Sinkovics and
Roath 2004, Panayides and So 2005, Knemeyer and Murphy 2005).
Aside from identifying the antecedents of customer partnering behavior, an
important contribution of the model is to provide an understanding of how the
interplay among various factors occurs, leading to a customer’s partnering behavior
with its 3PL. The factors that composed the model were related to environmental
forces, interorganizational conditions and firm-specific factors. Since it has been
shown that these factors contribute positively to performance, understanding the
mechanisms through which these close relationships occur is very relevant.
Collecting data from Brazilian 3PL customers is a final contribution. The
majority of studies in the logistics outsourcing literature have focused on U.S. firms.
Other studies have focused on surveys and case studies in countries such as New
Zealand, Saudi Arabia, China, and Mexico. However, to the best of this author’s
knowledge, no study has used Brazilian data. Given the importance of the Brazilian
market to world trade, understanding the dynamics of logistics outsourcing
partnerships in that market is relevant. Moreover, since many of the constructs tested
here were first developed and used with U.S. data, future cross-cultural comparisons
can be undertaken.
Contributions to the marketing literature: An important contribution of this
dissertation to the marketing literature is extending the seminal marketing study
developed by Ganesan (1994). Although written more than ten years ago, this study
continues to be cited by marketing researchers. His model identified the antecedents
of long-term orientation in buyer-seller relationships and was tested with retailers and
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their vendors. The constructs adopted by Ganesan (1994) focused primarily on
interorganizational conditions (e.g. trust in the partner, dependence on the partner)
and elements of environmental conditions (e.g., environmental uncertainty). The
present model contributes to the extension of Ganesan’s model by combining firm-
specific characteristics with interorganizational factors in the explanation of a firm
partnering behavior. The results of the model indicate that Ganesan’s rationale also
holds in the case of partnering behavior in logistics outsourcing relationships, but
provides statistical validation that firm-specific factors also play an important role in
shaping partnering behavior as well.
Another contribution to the marketing and partnering literatures relates to the
multidimensional nature of the dependent variable: customer partnering behavior.
Ganesan’s (1994) study focuses on long-term orientation, which is one dimension of
partnering. To the best of its author’s knowledge, there has been no study in which
partnering behavior itself is the dependent variable. A final contribution to the
marketing literature is testing the model in an industry that is not commonly
investigated by marketing researchers, the logistics outsourcing industry.
Contributions to managers: This study brings relevant contributions to 3PL
managers. It has been consistently shown in the logistics literature that developing
and nurturing close relationships between 3PLs and customer firms results in benefits
for 3PLs and customers (e.g., higher performance, higher levels of customer retention
and referrals, increased market share, etc). It is thus in the 3PL’s interest to identify
the factors that are important or effective in stimulating their customers to engage in
close relationships; i.e., to exhibit partnering behavior. The present model identified
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these factors and their relative effects on shaping customer partnering behavior.
Identifying the factors that have a strong influence on customer partnering behavior
provides guidance to 3PLs on how to best nurture partnerships with their customers.
This could assist 3PLs in maintaining and expanding their customer base.
6.3. Managerial implications
This research has identified several major factors that influence a customer’s
partnering behavior in its relationship with a 3PL. Based on the results of the
research, several recommendations can be made to 3PL managers:
Increasing a customer dependence on a 3PL. A 3PL can increase the depth
of its partnerships with its customers by increasing customer dependence on its
services. It was shown that when a customer perceives to be dependent on a 3PL, a
customer exhibits higher levels of partnering behavior (H1). The level of a customer
dependence on a 3PL will be a function of two main factors: a customer’s logistics
capabilities and the degree to which a 3PL invests in the relationship. The results of
the model indicated that there is a negative relationship between a customer’s internal
logistics capabilities and customer dependence on a 3PL. The key idea is that a 3PL
should carefully protect its core competencies. If a customer perceives to fully
understand how to perform those activities outsourced to the 3PL, it will perceive to
be less dependent and exhibit a partnering behavior to a less degree. This increases
the likelihood that a customer will quit the relationship for an alternative 3PL and
contract less of the focal 3PL services.
Secondly, as counterintuitive as it may sound, the results from this dissertation
suggest that a 3PL should invest in a relationship (H16) in order to increase customer
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dependence. This might be either because the investments increase customer loyalty,
or because the customer perceives that no other 3PL may be willing to invest in the
relationship. A 3PL that invests in a relationship may feel appreciated by its
customer. These investments do not necessarily need to be in physical assets. They
can be related to training of transactional activities related to the operations between
3PL and customer, or processes developed exclusively for that particular relationship.
It is relevant to note that there was no evidence found that competitive pressures in
the customer industry, or that complexity of customer operations, or availability of
other 3PLs, impacts a customer’s perceived dependence on a 3PL. Therefore, in order
to increase a customer’s perceived dependence, a 3PL should focus on its capabilities
and investments in the customer relationship.
Increasing customer trust in a 3PL. The model results indicate that there is a
positive relationship between a customer’s perception of a 3PL’s benevolence and a
customer’s partnering behavior (H4). Therefore, it is crucial for a 3PL representative
to make an effort to develop personal and interactive relationships with its customers.
Personal relationships may be emphasized in many areas of a 3PL’s activities,
including those that deal with customer issues or complaints (e.g., the marketing
department and call-center). Several semi-structured interviews were conducted with
Rapidão Cometa’s customers. During the interviews, it was emphasized how
important the weekly visits from Rapidão Cometa’s representatives were for the
customers. The customers argued that Rapidão Cometa was responsive when
problems arose, such as late shipments. They pointed out that it was important to
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know that someone from Rapidão Cometa was paying attention to their problems and
working to solve them.
Aside from working on interpersonal interactions with customers, one way to
increase a customer’s perception of a 3PL’s benevolence is by investing in the
relationship. It was shown that there is a positive relationship between 3PL
transaction-specific investments and 3PL benevolence. Transaction specific
investments are a demonstration of concern and care for the relationship. Therefore, if
a 3PL invests in the relationship with a customer, the customer will not only perceive
itself to be dependent on the 3PL, but will also trust the 3PL.
An important means of increasing a customer’s perception of a 3PL’s
credibility is by increasing the 3PL’s reputation. A positive relationship was found
between a 3PL’s reputation and its customer’s perception of the 3PL’s credibility.
Reputational advertising may help here. In addition, reputation may spread through
word-of-mouth. One of the customers interviewed said that his company chose
Rapidão Cometa based on conversations with managers from other firms who already
worked with Rapidão Cometa.
Finally, it is crucial that a customer is satisfied with the services provided. The
model results show that satisfaction with outcomes of the relationship build trust that
in turn shapes a customer’s partnering behavior with a 3PL. During the interviews,
on-time performance, freight visibility through a satellite tracking system, and cargo
integrity (i.e., absence of damage and spoilage) were clearly the main factors that
customers used to evaluate Rapidão Cometa’s performance.
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Knowing a 3PL customer. The model results showed that a customer
relationship marketing orientation and a customer’s prior experience with partnering
had strong positive impacts on partnering behavior. These findings are particularly
important for a 3PL when deciding whether to start working a new customer. A
customer’s own marketing strategies and philosophies of relating with business
partners will influence the quality and dynamics of its relationship with the 3PL. A
3PL should try to understand how a customer relates to its own customers. If the
customer firm’s strategy focuses on nurturing mutually beneficial relationships with
its own customers, it is more likely that the customer will do the same with the 3PL.
In addition, a customer’s experience with other 3PLs will shape its
expectations in the present relationship. Therefore, it is recommended that 3PLs
investigate a customer’s prior experiences with other 3PLs. If a customer has any
prior experience partnering with other 3PLs, it may have more realistic expectations
with the current service level.
6.4. Limitations
There are key limitations of this study. The first set of limitations is related to
the nature of the model, variable measurement, and data collected. First, this study
examined customer partnering behavior in a relationship with a 3PL from the
customer’s perspective. The perception of the 3PL provider was not captured in the
data. Second, all constructs were measured by perceptual scales. Ideally objective
measures should be utilized to match the perceptual measures, especially those that
are related to operational activities (e.g., information exchange, planning, operating
controls, logistics operations). Third, the sample respondent firms are customers of a
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single 3PL. These customers might not represent the population profile of Brazilian
firms, in general.
Another concern is related to the comparability of the findings from this study
to those of other studies. The fact that the sample is composed of Brazilian firms may
make the findings difficult to compare to those from other studies, given that most of
the studies have been conducted with U.S. firms. In addition, given that the model
focused on a single industry (i.e. logistics outsourcing industry) the findings may not
be generalizabe to other industries.
The second set of limitations is related to methodological issues. Many
variables were skewed, which violates the normality assumption of structural
equation models. This research tried to overcome this problem by using robust
estimation techniques. In addition, despite concerted efforts to increase survey
responses, there were still a fairly small number of observations to test a complex
model.
6.5. Future research
Several avenues for future research can be identified:
First, the model could be enhanced by testing the effects of customer
partnering behavior on performance. Performance measures could include perceptual
measures from the customer’s perspective, or objective measures, such as on-time
performance, or sales. It would be interesting to contrast the results of models
estimated using perceptual versus objective measures.
Second, an alternative model without trust or dependence as mediating
variables could be tested. As explained in the literature review section, according to
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social exchange theory, environmental and firm-specific factors contribute to the
creation of interorganizational conditions (i.e. dependence and trust) that, in turn,
influence relational behavior (customer partnering behavior in this dissertation).
Studies that follow other theories, such as resource dependency and transaction costs
economics, link environmental and firm-specific factors directly to relational
behavior variables. Comparing alternative models would be a good extension of this
paper.
The model could be tested linking the independent variables separately to
each of the dimensions of partnering behavior. It might be the case that some
dimensions of partnering behavior, such as operating controls, might be highly
influenced by the partnering antecedents, while others might not be influenced to the
same extent. Similarly, a model where performance is the dependent variable could
be tested with separate dimensions of partnering as the independent variables. It
might be the case that some components of partnering have a greater effect on
performance than do others.
Simpler models could be tested well. For example, the effect of RMO on
partnering, moderated by environmental uncertainty could be tested. Customer
demographics may also play a role in shaping partnering behavior. For example, the
customer partnering behavior of small and large firms could be compared. As well,
different types of customers could be compared; e.g., partnership behavior could be
compared between those with few and many functions outsourced.
Finally, previous work on marketing and transaction costs economics has
shown that factors, such as contractual issues, legal issues and relationship
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measurement issues, could also affect partnering behavior. A future model could
encompass these additional variables.
6.6. Summary and concluding remarks
Although the logistics literature has reinforced the importance of relationship
building between 3PLs and their customers, a theoretical and testable model that
identifies the factors that lead customers to exhibit partnering behavior is still lacking.
This dissertation fills this gap by identifying the factors that lead to customer
partnering behavior in a relationship with a 3PL. In addition, the interplay between
environmental forces, interorganizational conditions, and firm-specific factors in the
shaping of such behavior is described.
Interorganizational conditions of trust and dependence were found to be key
drivers of a customer’s partnering behavior, and correspond to the factors 3PLs must
focus on to improve partnering. It is relevant to note that these interorganizational
influences are stronger than other factors, such as a customer’s experience with
partnering or the strategic orientation of the customer. In order to increase levels of
dependence and trust, transaction-specific investments may be made by the 3PL. This
single element has a strong influence on both trust and dependence. An important
point is that competitive and operational environments do not seem to have a
significant influence on a customer‘s dependence.
Aside from investing in the relationship with customers, levels of trust can be
increased by building reputation for excellence and fairness and, especially, by
demonstrating concern for the relationship with customers. The interpersonal side of a
relationship should not be underestimated.
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This research shows that a customer’s experience with partnering and strategic
orientation (i.e., relationship marketing orientation) also play important roles in
explaining a customer’s partnering behavior. This means that before investing in a
relationship with a specific customer, the 3PL could closely investigate how the
potential customer behaves in its relationships with its own customers. It is important
to identify the nature of a customer’s relationship with its own customers, since this
relationship might mirror the customer-3PL partnership.
In conclusion, maintaining interorganizational relationships requires a broad
knowledge of a partner’s strategic profile and expectations. It requires creating
conditions of satisfaction, trust, and dependence on the relationship. Partnerships are
hard to build and maintain. This study may shed some light on effective actions that
3PLs can undertake in order to build strong partnerships with their customers.
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Appendices
1. E-mails
2. Survey instrument
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Initial E-mailing cover letter
Dear ,
With the emphasis on cost reduction and service improvement, more companies have
engaged in partnerships with one or more third party logistics providers (3PLs). To gain a
better understanding of what factors trigger that decision and what are the performance
effects, Prof. Martin Dresner and Adriana Rossiter at the University of Maryland, with the
support of Rapidao Cometa, are conducting a research study that examines the roles of
dependence, trust, and strategic orientation in partnering relationships with logistics
providers, and what effects these relationships have on customer performance.
You are one of a small group of individuals selected as being particularly knowledgeable
about these types of relationships. We are asking you to provide input on your experience in
working with Rapidão Cometa. To ensure that the results of this research represent the
opinion of firms that are involved in these relationships, it is important that the web-based
survey be fully completed. The survey should take approximately 20 minutes to complete.
Your participation involves the completion of the survey at the link provided below. Sending
the questionnaire is your consent to participate in this study and the acknowledgement that
you are 18 years or over. Your responses will be completely confidential and combined with
data others provide for presentation purposes. Individual email addresses will be kept on file
for about six weeks after the launch of the survey for potential follow-up e-mails, but will be
removed from the database afterwards. Rapidao Cometa will not have access to individual
responses. As an incentive for you fully completing the survey, we offer you a summary
report of the research and the chance to win an iPod. You may withdraw from participation in
this survey at any time and your data will be removed from the study results. If you want to
withdraw from participation in this survey, please send an e-mail to Adriana Rossiter.
The overall results from this research may be very helpful to you in identifying the key
factors that lead to 3PL-customer partnering, and how that affects a customer’s performance.
If you would like to receive a summary copy of the results, please include your contact
information at the end of the survey. Some examples of questions in the survey include your
agreement with sentences such as: “The relationship with Rapidao Cometa has improved our
information technology”, or “We require shipment tracking ability.”
If you have any questions about this project, please call Adriana Rossiter at 1.301. 456.9163
or e-mail to [email protected]. Thank you very much for your support in this
important study about the 3PL industry. If you have questions about your right as a research
subject, please contact: Institutional Review Board Office, University of Maryland, College
Park, MD 20742, [email protected] or 1.301.405.4212.
Sincerely,
Prof. Martin Dresner
The R. H. Smith School of Business
University of Maryland
Adriana Rossiter
The R. H. Smith School of Business
University of Maryland
179
Follow-up e-mail
Dear ,
I recently e-mailed you seeking your input on your relationship with Rapidão
Cometa. If you already completed the survey, thank you for your participation. If you
have not yet had the chance to complete the survey, could you please take a few
minutes now and complete the survey found at the link below?
The results of this study should be very helpful to you in identifying the key factors
that led you to engage in a relationship with Rapidão Cometa. If you would like to
receive a summary copy of the results, simply include your contact information at the
end of the survey.
If you have any questions, please call me at 1.301. 456.9163 or send me an email at
[email protected]. Again, thank you for your cooperation. I greatly
appreciate your time and effort in completing the survey.
Sincerely,
Adriana Rossiter
University of Maryland
180
3PL-customer partnerhips:
A study about your relationship with Rapidão Cometa
The success of this research is depends on your participation. Thank you in advance for your time and support!
We would like to thank you for participating and offer you:
- an opportunity to win a digital camera/iPod (retail value $ 150.00)
- a summary report so you can identify the drivers of your relationship with Rapidão Cometa and how it
affects your performance.
Instructions
Please read the following instructions carefully before beginning the survey:
- Your responses to the questions will be strictly confidential and accessible only to the researchers.
Rapidão Cometa will not have access to your individual responses. Your responses will be used along
with responses from other participating customers to create summary reports.
- Please answer all the questions as well as you can, even if some questions may appear similar. If you
do not know the exact answer, please provide your best estimate.
- Please refer all questions to your business unit or the unit of your company responsible for managing
logistics.
- You can suspend the completion of the survey after each page that you have submitted and
continue later using the hyperlink and password that were included in your invitation email. Your entries
are saved by clicking on the “submit” button at the end of each page. Please note that the chances to
win gifts are available only to those respondents who complete the entire survey.
- Please use the “submit” and “back” buttons within the survey. Using the “back” and “next” buttons
of your browser may result in data loss.
Please contact Adriana Rossiter for questions:
Email: [email protected]
Phone: 1.301.314.9170
181
A. Relationship with Rapidão Cometa
This first set of statements describes the relationship between Rapidão Cometa
and your company. Please indicate your level of agreement.
Strongly
disagree
Neither
agree
nor
disagree
Strongly
agree
We expect our relationship with Rapidão Cometa to last a
long time ? ? ? ? ? ? ?
We are very loyal to Rapidão Cometa ? ? ? ? ? ? ?
Maintaining a long-term relationship with Rapidão
Cometa is important to us. ? ? ? ? ? ? ?
We have many direct computer to computer links with
Rapidão Cometa (i.e., EDI) ? ? ? ? ? ? ?
We use software compatible with Rapidão Cometa ? ? ? ? ? ? ?
We are linked to Rapidão Cometa through computers ? ? ? ? ? ? ?
We and Rapidão Cometa exchange information that
helps establishment of our business planning
We require shipment tracking ability ? ? ? ? ? ? ?
We require frequent fleet status reports ? ? ? ? ? ? ?
We require on-time performance reports
We are willing to help Rapidão Cometa in difficult
situations ? ? ? ? ? ? ?
We share risk with Rapidão Cometa ? ? ? ? ? ? ?
We have a high willingness to handle exceptions by
negotiation ? ? ? ? ? ? ?
Rapidão Cometa and our company have joint
committees/task forces ? ? ? ? ? ? ?
We heavily exchange technical information with Rapidão
Cometa ? ? ? ? ? ? ?
We regularly study Rapidão Cometa's operations for our
planning ? ? ? ? ? ? ?
182
The next set of statements is related to how your relationship with Rapidão
Cometa has helped improve your company performance. Please indicate your
level of agreement.
Strongly
disagree Neutral
Strongly
agree
This relationship has….
improved our logistics system responsiveness ? ? ? ? ? ? ?
improved our logistics system information ? ? ? ? ? ? ?
reduced our operational risk ? ? ? ? ? ? ?
improved our product/service availability ? ? ? ? ? ? ?
allowed us to achieve logistics costs reductions ? ? ? ? ? ? ?
improved our information technology ? ? ? ? ? ? ?
enabled us to implement changes faster/better ? ? ? ? ? ? ?
provided us more specialized logistics expertise. ? ? ? ? ? ? ?
enabled us to move from a "push" to a "pull" system ? ? ? ? ? ? ?
reduced our order cycle time ? ? ? ? ? ? ?
improved our post-sale customer support ? ? ? ? ? ? ?
helped us integrate our supply chain ? ? ? ? ? ? ?
183
Are you satisfied with the services provided by Rapidão Cometa? Please
describe your opinions with respect to the outcomes with Rapidão Cometa in the
past year:
Last year…
Strongly
disagree
Neither
agree
nor
disagree
Strongly
agree
…we were pleased with the outcomes ? ? ? ? ? ? ?
… working with Rapidao was very useful ? ? ? ? ? ? ?
… Rapidao Cometa was ineffective ? ? ? ? ? ? ?
… we were dissatisfied ? ? ? ? ? ? ?
… the outcomes were outstanding ? ? ? ? ? ? ?
… the outcomes were of bad value for our company ? ? ? ? ? ? ?
… we were comfortable in working with Rapidao
Cometa
How many years has your company worked with Rapidão Cometa? ____ years. (e.g.,
2.5)
Has your company ever partnered with logistics providers? ___ Yes ___ No
If yes, how many years has your company partnered with other logistics providers (in
general, not necessarily with Rapidão Cometa)? ____ years (e.g., 2.5)
.
184
The following statements describe your relationship with Rapidao Cometa’s
representative. Please indicate the level of agreement.
Strongly disagree Strongly agree
Rapidão Cometa’s representative…
… has been frank in dealing with us ? ? ? ? ? ? ?
… makes reliable promises ? ? ? ? ? ? ?
… is knowledgeable regarding his services ? ? ? ? ? ? ?
… does not make false claims ? ? ? ? ? ? ?
… is not open in dealing with us ? ? ? ? ? ? ?
… is honest about the problems may them arise ? ? ? ? ? ? ?
… has difficulties answering our questions ? ? ? ? ? ? ?
… has made sacrifices for us in the past ? ? ? ? ? ? ?
… cares about us ? ? ? ? ? ? ?
… has supported us in times of shortages ? ? ? ? ? ? ?
… is like a friend ? ? ? ? ? ? ?
… has has been on our side ? ? ? ? ? ? ?
Rapidão Cometa…
… has a reputation for being honest ? ? ? ? ? ? ?
… has a reputation for being concerned about its customers ? ? ? ? ? ? ?
… has a bad reputation in the market ? ? ? ? ? ? ?
… has a reputation for being fair according to most customers ? ? ? ? ? ? ?
185
How important is Rapidão Cometa to your company? Please indicate your level
of agreement with the following statements.
Strongly
disagree
Strongly
agree
Rapidão Cometa is crucial to our performance ? ? ? ? ? ? ?
Rapidão Cometa is important to our business ? ? ? ? ? ? ?
If our relationship with Rapidão Cometa were discontinued, we
would have difficulty in performing its services. ? ? ? ? ? ? ?
It would be difficult for us to replace Rapidão Cometa ? ? ? ? ? ? ?
We are dependent on Rapidão Cometa ? ? ? ? ? ? ?
We do not have a good alternative to Rapidão Cometa. ? ? ? ? ? ? ?
We are important to Rapidão Cometa. ? ? ? ? ? ? ?
We are a major customer for Rapidão Cometa in our trading
area. ? ? ? ? ? ? ?
We are not a major customer for Rapidão Cometa. ? ? ? ? ? ? ?
We have made significant investments (e.g., technology,
training etc.) dedicated to our relationship with Rapidão
Cometa ? ? ? ? ? ? ?
If we switched to a competing logistics provider, we would lose
a lot of the investment we have made in this relationship. ? ? ? ? ? ? ?
We have invested substantially in personnel dedicated to this
relationship ? ? ? ? ? ? ?
If we decided to stop working with Rapidão Cometa, we would
be wasting a lot of knowledge regarding its methods of
operation. ? ? ? ? ? ? ?
Rapidão Cometa has gone out of its way to link us with its
business ? ? ? ? ? ? ?
Rapidão Cometa has tailored its services and procedures to
meet the specific needs of our company ? ? ? ? ? ? ?
Rapidão Cometa would find it difficult to recoup its investments
in us if our relationship were to end. ? ? ? ? ? ? ?
186
How would you describe the market for the product you ship with Rapidão
Cometa ?
Strongly
disagree Neutral
Strongly
agree
The demand is unpredictable ? ? ? ? ? ? ?
Sales forecasts are accurate ? ? ? ? ? ? ?
The industry production is stable ? ? ? ? ? ? ?
The demand trends are easy to monitor ? ? ? ? ? ? ?
The market is very complex ? ? ? ? ? ? ?
There are many new products ? ? ? ? ? ? ?
There are many competitors ? ? ? ? ? ? ?
How would you describe the market for logistics services in Brazil?
Strongly
disagree Neutral
Strongly
agree
The market for logistics services in Brazil
…. has an unpredictable demand ? ? ? ? ? ? ?
…. has a stable of service availability ? ? ? ? ? ? ?
… is easy to monitor ? ? ? ? ? ? ?
… is very complex ? ? ? ? ? ? ?
… has many service offerings ? ? ? ? ? ? ?
… has many logistics providers ? ? ? ? ? ? ?
187
B. Questions on the operational and competitive profiles of your company
The following items describe the complexity of the logistics operations of your
company. Please indicate your level of agreement.
Strongly
disagree Neutral Strongly agree
We have a complex network of trading partners. ? ? ? ? ? ? ?
The timeliness of the transactions in our supply chain is
crucial in our business. ? ? ? ? ? ? ?
We must accomplish very short order cycle times for
customer orders. ? ? ? ? ? ? ?
We have a complex network of origin/destination (OD)
pairs. ? ? ? ? ? ? ?
Our products require specialized transportation,
storage, or handling (e.g. temperature, humidity, etc.) ? ? ? ? ? ? ?
The following items describe the logistics personnel of your company. Please
indicate your level of agreement.
Strongly
disagree Neutral
Strongly
agree
Relative to the size of our firm, we have a large group of
upper-level managers dedicated to logistics ? ? ? ? ? ? ?
Relative to the size of our firm, we have a large group of
employees across all levels dedicated to logistics ? ? ? ? ? ? ?
Our logistics personnel have a deep understanding of our
logistics operations ? ? ? ? ? ? ?
Our logistics personnel know where problems and
bottlenecks might exist in our logistics operations ? ? ? ? ? ? ?
Our logistics personnel are capable of finding effective
solutions when problems arise ? ? ? ? ? ? ?
188
The following sentences describe the relationship between your company and
your company’s major customers (attention: NOT Rapidao Cometa). Please
indicate your level of agreement.
Strongly
disagree
Strongly
agree
We trust each other ? ? ? ? ? ? ?
They are trustworthy on important things. ? ? ? ? ? ? ?
According to our past business relationship, my company thinks
that they are trustworthy persons. ? ? ? ? ? ? ?
My company trusts them. ? ? ? ? ? ? ?
We rely on each other. ? ? ? ? ? ? ?
We both try very hard to establish a long-term relationship. ? ? ? ? ? ? ?
We work in close cooperation.
We keep in touch constantly. ? ? ? ? ? ? ?
We communicate and express our opinions to each other
frequently. ? ? ? ? ? ? ?
We can show our discontent towards each other through
communication. ? ? ? ? ? ? ?
We can communicate honestly. ? ? ? ? ? ? ?
We share the same worldview. ? ? ? ? ? ? ?
We share the same opinion about most things. ? ? ? ? ? ? ?
We share the same perspectives toward things around us. ? ? ? ? ? ? ?
We share the same values. ? ? ? ? ? ? ?
We always see things from each other’s perspective. ? ? ? ? ? ? ?
We know how each other thinks. ? ? ? ? ? ? ?
We understand each other’s values and goals. ? ? ? ? ? ? ?
We care about each other’s feelings. ? ? ? ? ? ? ?
My company regards “never forget a good turn” as our business
motto. ? ? ? ? ? ? ?
We keep our promises to each other in any situation. ? ? ? ? ? ? ?
If our customers gave assistance when my company had
difficulties, then I would repay their kindness. ? ? ? ? ? ? ?
189
Thank you for completing the survey to this point. We appreciate the time you have
taken to complete this survey!
We would now like to ask you to complete a few background questions. As with the
rest of the survey, we guarantee strict confidentiality!
What is your position?
President/CEO/COO
Vice president, logistics, transportation, or distribution
Director, Logistics, transportation, or distribution
Manager, Logistics, transportation, or distribution
Supervisor, Logistics, transportation, or distribution
Employee, Logistics, transportation, or distribution
Logistics analyst
Other, please specify:
For how many years has your company been operating?
For ___ years (e.g. 2.5)
For how many years have you been working in this position?
For ___ years (e.g. 2.5)
For how many years have you been working for this company?
For ____ years (e.g. 2.5)
What category better describe your industry?
Please select only one industry.
Food and beverage
Automotive
Consumer goods
Industrial equipment
Electronics and related instruments
Computer hardware and peripheral equipment
Chemicals and plastics
Retailing
Healthcare
Other __
190
What are the current monthly revenues of your company (in R$
thousand/month)
Up to 9 10-100 101-
1000
1001-
5000
5001-
10000
10001 -
100000
100.001
-499.000
More
than
500.000
What is the approximate number of employees in your business unit?
…. Employees
How many logistics providers/carriers does your business unit use?
…. logistics providers.
Please complete the following questions having Rapidao Cometa in mind.
Which services does Rapidão Cometa provide to your company?
Please mark all applicable services.
Transportation planning ?
Transportation operations ?
International freight forwarding ?
Cross-docking ?
Warehousing ?
Inventory control/management ?
Pick/pack operations ?
Assembly ?
Reverse logistics ?
Logistics information systems ?
Lead logistics management ?
EDI capability ?
Order fulfillment ?
Freight forwarding ?
Route and network optimization ?
Freight consolidation ?
Outbound traffic control ?
Inbound traffic control ?
Other: _____
What is Rapidão Cometa’s approximate share of your total outsourced logistics
expenditures?
…. Percent (e.g., 2.5)
For how long has your unit been working together with Rapidao Cometa in a
way that you would call a “close relationship”?
For …. Years (e.g., 2.5)
What is the total duration of the current contract with Rapidao Cometa?
….. years (Zero – 0 – if no contract)
191
Thank you for taking part in this survey!
Please provide us with the address to which we may forward your summary
report:
Last name
First name
Email address
Company
Street
Zip
City
Phone number
Can we contact you in order to get further information? ___ Yes ___ No
Please use the space below for comments and suggestions:
192
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