Description
Study Reports on Electronic Mediation, Transformation, and Business Value: Three Essays in the Retail Auto Industry:- In management, business value is an informal term that includes all forms of value that determine the health and well-being of the firm in the long run. Business value expands concept of value of the firm beyond economic value (also known as economic profit, economic value added, and shareholder value) to include other forms of value such as employee value, customer value, supplier value, channel partner value, alliance partner value, managerial value, and societal value. Many of these forms of value are not directly measured in monetary terms.
STUDY REPORTS ON ELECTRONIC MEDIATION,
TRANSFORMATION, AND BUSINESS VALUE:
THREE ESSAYS IN THE RETAIL AUTO
INDUSTRY
ABSTRACT
This dissertation seeks to answer the following research questions: (1) what properties
enable some organizations to generate more value from information technology (IT) than
others? and (2) through what mechanisms do organizations generate value through IT?
It examines the role of technology in value creation through three essays using three
different aspects of organizational performance.
Chapter 1: Responding to Technology-Enabled Organizational Transformation: The Role
of Strategic Change Orientation
Essay one examines the role of strategic change orientation and three change enablers—
IT capabilities, climate for IT use, and mindfulness of IT adoption—in influencing
business process performance during a period of IT-enabled transformation. The data
source for this essay is a survey of auto retailers facilitated by a leading online
infomediary.
Chapter 2: Profiting From the Internet Channel: The Complementarity of Electronic
Commerce Capabilities and Business Process Change
Essay two accesses the joint role of electronic commerce capabilities and business
process change in a model that examines the value firms derive from the Internet channel.
The data source for this essay is a survey of auto retailers conducted by a leading market
research firm.
Chapter 3: Understanding Retailer Use of Online Auction Channels: Strategies In
Repeated Search Processes
Essay three examines sellers' use of the online auction market and the resulting value
obtained for a given product through the theoretical lens of search theory. We model
sellers' repeated listing of unsold products and adjustment of reserve price as a process of
searching for high valuation customers. The data source for this essay is transactional
data from a leading online auction site specializing in automobiles.
ELECTRONIC MEDIATION, TRANSFORMATION, AND BUSINESS VALUE:
THREE ESSAYS IN THE RETAIL AUTO INDUSTRY
by
Jason Nicholas Kuruzovich
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
2006
Advisory Committee:
Professor Ritu Agarwal, Chair
Professor Paul Hanges
Professor Sanjay Gosain
Professor P.K. Kannan
Professor Henry C. Lucas
Professor Sivakumar Viswanathan
© Copyright by
Jason Nicholas Kuruzovich
TABLE OF CONTENTS
LIST OF TABLES............................................................................................................. iii
LIST OF FIGURES ........................................................................................................... iv
OVERVIEW: ELECTRONIC MEDIATION, TRANSFORMATION, AND BUSINESS
VALUE: THREE ESSAYS IN THE RETAIL AUTO INDUSTRY.................................. 1
CHAPTER 1: RESPONDING TO TECHNOLOGY-ENABLED ORGANIZATIONAL
TRANSFORMATION: THE ROLE OF STRATEGIC CHANGE ORIENTATION ....... 2
1.1 ABSTRACT.............................................................................................................. 2
1.2 INTRODUCTION .................................................................................................... 3
1.3 LITERATURE REVIEW ......................................................................................... 5
1.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 20
1.5 RESEARCH METHODOLOGY............................................................................ 26
1.6 DISCUSSION ......................................................................................................... 33
1.7 SUMMARY............................................................................................................ 37
1.8 FIGURES................................................................................................................ 38
1.9 TABLES ................................................................................................................. 40
CHAPTER 2: PROFITING FROM THE INTERNET CHANNEL: THE
COMPLEMENTARITY OF ELECTRONIC COMMERCE CAPABILITIES AND
BUSINESS PROCESS CHANGE.................................................................................... 44
2.1 ABSTRACT............................................................................................................ 44
2.2 INTRODUCTION .................................................................................................. 45
2.3 LITERATURE REVIEW ....................................................................................... 48
2.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 54
2.5 RESEARCH METHODOLOGY............................................................................ 62
2.6 DISCUSSION ......................................................................................................... 67
2.7 SUMMARY............................................................................................................ 71
2.8 FIGURES................................................................................................................ 72
2.9 TABLES ................................................................................................................. 74
CHAPTER 3: UNDERSTANDING RETAILER USE OF ONLINE AUCTION
CHANNELS: STRATEGIES IN REPEATED SEARCH PROCESSES......................... 79
3.1 ABSTRACT............................................................................................................ 79
3.2 INTRODUCTION .................................................................................................. 80
3.3 LITERATURE REVIEW ....................................................................................... 83
3.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 89
3.5 RESEARCH METHODOLOGY............................................................................ 98
3.6 DISCUSSION ....................................................................................................... 103
3.7 SUMMARY.......................................................................................................... 110
3.8 FIGURES.............................................................................................................. 112
3.9 TABLES ............................................................................................................... 114
APPENDICES ................................................................................................................ 118
APPENDIX A - ESSAY 1 MEASURES................................................................... 118
APPENDIX B - ESSAY 2 MEASURES.................................................................... 124
REFERENCES ............................................................................................................... 126
ii
LIST OF TABLES
CHAPTER 1
TABLE 1.1 - DESCRIPTIVE STATISTICS FOR SURVEY MEASURES
TABLE 1.2 - CORRELATION MATRIX
TABLE 1.3 - ITEM CORRELATION TABLE
TABLE 1.4 - PLS OUTER MODEL LOADINGS
CHAPTER 2
TABLE 2.1 - SUMMARY OF TYPOLOGIES OF IT CAPABILITIES
TABLE 2.2 - DESCRIPTIVE STATISTICS
TABLE 2.3 - PRINCIPAL COMPONENT ANALYSIS
TABLE 2.4 - CORRELATION TABLE
TABLE 2.5 - REGRESSION ANALYSIS RESULTS
CHAPTER 3
40
41
42
43
74
75
76
77
78
TABLE 3.1 - DESCRIPTIVE STATISTICS (ALL AUCTIONS ENDING IN SALE) 114
TABLE 3.2 - CORRELATION MATRIX (ALL AUCTIONS ENDING IN SALE)
TABLE 3.3 - REGRESSION ANALYSIS OF FINAL SALE PRICE (USED IN
CALCULATION OF PRICE PREMIUM)
TABLE 3.4 - REGRESSION ANALYSIS OF EO MEAN AND EO VARIANCE
TABLE 3.5 - 3SLS ANALYSIS OF DURATION OF SEARCH AND PRICE
PREMIUM
115
116
116
117
iii
LIST OF FIGURES
CHAPTER 1
FIGURE 1.1 - CONCEPTUAL FRAMEWORK
FIGURE 1.2 - RESEARCH MODEL
FIGURE 1.3 - PLS RESULTS
CHAPTER 2
FIGURE 2.1 - RESEARCH MODEL
FIGURE 2.2 - GRAPH OF PROFIT VS. NUMBER OF LEADS
CHAPTER 3
FIGURE 3.1 - CONCEPTUAL FRAMEWORK
FIGURE 3.2 - RESEARCH MODEL
FIGURE 3.3 - PLOT OF PRICE PREMIUM VS. DURATION
FIGURE 3.4 - PLOT OF PRICE PREMIUM VS. DURATION FOR DIFFERENT
AUCTION LENGTHS
38
38
39
72
73
112
112
113
113
iv
OVERVIEW: ELECTRONIC MEDIATION, TRANSFORMATION, AND
BUSINESS VALUE: THREE ESSAYS IN THE RETAIL AUTO INDUSTRY
The study of the value generated through the use of information technology (IT)
has been a central theme within the information systems (IS) literature nearly since its
inception. This dissertation adds to this stream by seeking to answer the following
research questions: (1) what properties enable some organizations to generate more
value from IT than others? and (2) through what mechanisms do organizations generate
value through IT? We study the process of value creation in the context of the retail auto
industry using three different aspects of organizational performance. Essay one takes the
broadest view, examining the role of strategic change orientation and three change
enablers—IT capabilities, climate for IT use, and mindfulness of IT adoption—in
influencing organizational performance during a period of IT-enabled transformation.
Essay two focuses more specifically on the Internet channel, examining the
complementarities between electronic commerce capabilities and business process
change in influencing the business value obtained from the Internet channel. Essay three
examines sellers' use of the online auction market and the resulting value obtained for a
product through the theoretical lens of search theory—modeling sellers' repeated listing
of unsold products and adjustment of reserve price as a process of searching for high
valuation customers. Collectively, these three essays represent an important contribution
to the IS field by identifying critical organizational characteristics that influence the value
creation process. In doing so, they also provide a detailed examination of the specific
mediating mechanisms through which this value creation process occurs.
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CHAPTER 1: RESPONDING TO TECHNOLOGY-ENABLED
ORGANIZATIONAL TRANSFORMATION: THE ROLE OF STRATEGIC
CHANGE ORIENTATION
1.1 ABSTRACT
As a result of the emergence of the Internet and net-enabled business processes,
many industries have experienced a period of IT-enabled transformation in which
organizations and business operations changed very rapidly. A natural question that
arises is how can firms survive and even thrive during such transformations? In
addressing this question, we show how a firm's strategic change orientation—a meta-
construct consisting of technological opportunism, market orientation, and
entrepreneurial orientation—can influence the assimilation of IT and the resulting
performance of business processes. We identify and examine three separate change
enablers through which this influence occurs: (1) through the development of IT
capabilities; (2) through the creation of a positive climate for IT use; and (3) through
mindfulness of IT adoption. These three change enablers influence the assimilation of
technology within the organization and the resulting business process performance. We
test the proposed model using a survey capturing the level of assimilation and the benefits
from customer-focused information systems for 153 organizations in the retail auto
industry, a compelling example of an industry that has undergone an IT-enabled
transformation. Results provide significant support for the proposed relationships,
explaining nearly 47% of the variance in IT assimilation, 34% of the variance in process
performance, and 31% of the variance in financial performance.
-2-
1.2 INTRODUCTION
"The information superhighway, it turns out, goes right past a car dealership. A long-
overdue revolution in auto retailing has arrived."
-Fortune Magazine, March 4, 1996
The emergence of the Internet and net-enhanced business processes has had a
transformational impact on many industries, meaning that they have substantially altered
business processes and the nature of competition. In response to these IT-enabled
transformations, organizations have had to develop new ways to interact with and provide
value to customers while facing both new online competitors and new arenas for
competition. These transformations have thus created opportunities and challenges for
organizations, placing otherwise stable industries into periods of extensive operational
change and intense competition (Crowston and Myers 2004). The concept of technology
as a transforming force in the competitive relationships among firms is by no means new,
but rather can be traced back to the Schumpeterian idea of creative destruction
(Shumpeter 1942). However, with the increasing prominence of IT in firm business
processes and as a force in many industries, there is a need for researchers to better
understand how organizations can effectively navigate an IT-enabled industry
transformation (Agarwal and Lucas 2005).
Existing research has identified the transformational aspects of radical IT
innovations on individuals (e.g., Barrett and Walsham 1999; Robey and Sahay 1996;
Winter and Taylor 1996), organizations (e.g., Cross and Earl 1997; Markus and Benjamin
1997; Straub and Watson 2001; Yates and VanMaanen 1996), and society (e.g., Aupperle
-3-
1996; Campbell-Kelly 1996; Davenport and Stoddard 1994; El Sawy et al. 1999).
Although this research provides useful insights into the characteristics and impacts of
transformational technologies, much less is known about the general organizational
properties that enable effective responses to the competitive challenges posed by IT-
enabled transformations. In addition, as effective responses to IT-enabled
transformations are those which create value for organizations, there is a need to
incorporate our understanding of how organizations respond to IT-enabled
transformations into the broader IT-value literature while providing actionable
recommendations to managers and organizations.
This research examines the role of strategic change orientation (SCO)—a meta-
construct consisting of entrepreneurial orientation, technological opportunism, and
market orientation—in influencing the ability of the organization to respond to periods of
IT-enabled transformation. Specifically, we argue that strategic change orientation
influences IT assimilation and the resulting value the organization obtains through three
different change enablers. These three change enablers—IT capabilities, climate for IT
use, and mindfulness of IT adoption—provide a way to understand the theoretical
mechanisms through which SCO can enable an effective response to the challenges posed
by IT-enabled transformations. The overall conceptual framework describing the link
between SCO and performance is shown in Figure 1.1.
We test the proposed model in the context of the retail auto industry, a compelling
example of an industry that has undergone an IT-enabled transformation, examining the
assimilation of customer management systems across 153 dealerships and two business
units (sales and service). While the transformation of the industry has been initiated by a
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variety of technologies—including the Internet, online infomediaries, and dealer
websites—we argue the customer management systems represent a key technology which
helps organizations to manage the transformation. Overall, findings indicate that an
organization's strategic change orientation can be an important determinant of how the
organization assimilates technology into business processes and generates value. In
developing this understanding, we integrate several different streams of research to
explain differences in organizational assimilation and benefits from customer-focused
information systems.
The rest of the paper is organized as follows. The next section reviews the
literature associated with transformation and the other constructs associated with the
model, providing the primary theoretical foundation for this work. We then argue for the
specific hypotheses shown in Figure 1.2. Next, we describe the details related to the
testing of the model, including the measures and analysis techniques used. Finally we
discuss both the limitations and the implications of the findings.
1.3 LITERATURE REVIEW
In this section, we first fully define and review the relevant research on IT-
enabled transformation and then substantiate our claim that the retail auto industry in
particular has experienced such a transformation. Next, we provide a foundation for the
different streams of research integrated through this work. In doing this, we first examine
three types of strategic orientation—technological opportunism, market orientation, and
entrepreneurial orientation—and discuss how each influences an organization's
willingness to respond to technological change. We then examine the literature related to
three change enablers—IT capabilities, climate for IT use, and mindfulness of IT
-5-
adoption—which provide organizations with the ability to effectively deal with
technological change. Finally, we discuss the role of IT assimilation as an important
mediator to value creation. With this as a foundation, in the following section we discuss
the specific hypotheses related to the model shown in Figure 1.2.
IT-Enabled Transformation
Change and IT innovations are necessarily very closely linked, and have been
since Leavitt and Whisler's (1958) seminal article "Management in the 1980's" projected
an overall change in the size of middle management as a result of IT innovations. The
term "transformation" has been used frequently to describe substantial organizational
changes resulting from the presence of a radical IT innovation (e.g., Cross and Earl 1997;
Crowston and Myers 2004; Daniel and Wilson 2003; Jarvenpaa and Ives 1996; King
1996; Robey and Sahay 1996; Scott Morton 1991; Uhlenbruck 2003; Yates and
VanMaanen 1996). Although IT is an important initiator of organizational
transformation, the stream of research examining organizational transformation is much
broader than just that related to IT innovation and draws from the fields of economics,
strategy, and sociology (for reviews, see Pettigrew 1985; Wilson 1992).
Two frequently cited theoretical foundations addressing transformation include
creative destruction (Shumpeter 1942) and punctuated equilibrium (Gersick 1991;
Tushman and Romanelli 1985). Creative destruction refers to the process through which
organizations attempt to gain competitive advantage through innovation. This
competitive advantage, however, is necessarily temporary as market entrance or imitation
by competitors erodes (destroys) profits and forces the firm to continue to innovate.
-6-
The paradigm of punctuated equilibrium provides a separate though closely
related perspective of change, suggesting that there are longer periods of incremental
changes that are punctuated with periods of radical change (Gersick 1991; Tushman and
Romanelli 1985). These periods of radical change may alter the nature of competition,
the domain of direct competitors, the value of firm assets, or the nature of interactions
between customers and suppliers. Thus, the emergence of new technology and the
radical changes associated with it have important implications for organizations.
A third view of transformation has emerged directly from the IS literature. The
situated change perspective (Orlikowski 1996) provides an alternative lens for
transformation that stresses the ongoing and incremental nature of organizational change.
In addition, this perspective identifies the joint role of both social actors and technology
in determining the organizational outcomes from transformation. Another characteristic
of this perspective is that substantial transformations may actually be composed of a
series of smaller changes—suggesting that several incremental IT innovations and social
actors may interact over a period of time to lead to a more discernible transformation.
In order to further our understanding of the characteristics which enable
organizations to thrive during an IT-enabled transformation, we do not have to adopt a
specific perspective on transformation which is exclusive of any one of the three
perspectives reviewed above. Organizational responses to these transformations are
likely to be concentrated in particular periods of more and less change (as in the creative
destruction and punctuated equilibrium models) and also involve ongoing change (as in
the situated change perspective). We do, however, need to argue that the specific context
of transformation meets the basic criteria for a transformation set forth in prior research.
-7-
Prior research has defined transformation in different ways. In their discussion of
general organizational transformation, Romanelli and Tushman (1994) argued that a
transformation occurred when firms had substantial changes in strategy, structure, and
power over a period of two years. In the context of IT-specific investments, Dehning et
al. (2003) argued that transformational technology investments: (1) redefine business
processes or relationships; (2) involve acquisitions or entry into new markets; or (3)
dramatically change how tasks are carried out. While the specific criteria for determining
whether a transformation has occurred may be difficult to fully justify, the general theme
is that there is a substantial change in the competitive environment to which the firm
must respond.
IT-Enabled Transformation in the Retail Auto Industry
In examining the context of the retail auto industry, we adopt similar criteria as
Dehning et al. (2003) and argue that a number of technologies have emerged over time to
constitute an IT-enabled transformation. We describe three closely related but distinct
changes specifically impacting the business processes, relationships with customers, and
the creation of new markets—which together, we suggest, contribute to the overall
industry transformation.
Business Processes: The process of purchasing vehicles has changed substantially in the
last decade (Ratchford et al. 2003). The presence of the Internet and online infomediaries
enable individuals to communicate with multiple dealers, placing significant new
demands on traditional salespeople in terms of their workload and work tasks. While in
the past salespeople may have only interacted with individuals face-to-face, many
customers are now likely to expect to be able to negotiate through electronic channels.
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This substantially changes the business process through which dealerships initiate,
manage, and close transactions. In addition, as individuals wanting a particular vehicle
can more easily contact and negotiate with multiple dealerships, dealerships must adjust
their business process to actively manage a larger number of relationships.
Relationships with Consumers: Both the lowered search costs provided by the Internet
and the emergence of online infomediaries offering information and transactional
services have contributed to changes in the negotiations with customers. With the
emergence of the Internet, consumers rapidly began utilizing the web as a source of
information, and in 2004 over 64 percent of new vehicle purchasers used the web in some
way as part of the purchase process (Power 2004). This has significantly reduced the
level of information asymmetry, shifting the advantage in negotiations away from the
dealership. Purchasing through an online infomediary has been shown to result in a
savings of approximately 2% when compared to individuals who negotiate directly with
the dealership (Scott-Morton et al. 2001). This demonstrates the important financial
impact resulting from this change in the relationships with customers.
Creation of New Markets: By reducing the search costs of obtaining a price quote, the
lowered search costs of the Internet have reduced the overall importance of location for
competing dealerships (Bakos 1997). Dealerships have the opportunity to compete for
customers that they would have had little access to before the emergence of the Internet,
online infomediaries, and online auctions. As a large scale and vivid example of this, the
eBay Motors market represents a national auction market where dealers can sell to
consumers from around the country. This has been facilitated by the emergence of
shipping services which facilitate the transport of vehicles from dealerships to consumers
-9-
residing in different areas of the country. Overall, this and other online markets for
vehicles provide dealers with substantial new opportunities to reach customers.
Customer Management Systems
Customer management systems have features which provide auto-retailers with
the ability to manage many of the challenges of IT-enabled transformation described
above. These features can be generally categorized into three types:
workflow/messaging, information management, and analysis.
Workflow/Messaging: A key feature of customer focused systems is workflow
automation. As dealers may receive leads from many different sources. Customer
management systems provided an integrated way of centralizing these leads and routing
them to the individuals responsible. This can also enable such things as automated
scheduling of follow-up calls and messaging features which maintain contact with a
customer both before and after the purchase. Maintaining constant contact through email
may help the sales and service area to improve overall performance by increasing
customer loyalty.
Information Management: As the Internet enables dealerships to more easily access
customers from a variety of sources, managing information related to the preferences and
status of customers is more important than ever. Following-up with customers with
appropriate information allows the salesperson to meet the needs of the customer more
quickly and appropriately. Much of the usefulness of customer management systems as
an information tool requires that employees actually use the system to record interactions
with customers
- 10 -
Analysis: Customer management systems also enable dealerships to conduct detailed
analyses on work processes and customers. Customer management systems enable
dealerships to obtain detailed information on outcomes such as the conversion rate of
leads and the overall return on the investment of infomediary partnerships. Detailed
analyses may also be directly linked with marketing, providing a way to target offers and
mailings to meet the needs of specific customer profiles. In the face of increased
competition, targeted actions can help dealerships to increase the efficiency of their
advertising spending for both sales an service functions.
Strategic Orientation
The ability to identify and respond to changes initiated by technology is critical to
the performance and often even to the survival of firms—especially for firms located in
industries undergoing IT-enabled transformation. While the dominant paradigm of IT
investment suggests that IT adoption is necessarily a positive thing (Fichman 2004), we
acknowledge that this is not always the case with all technologies and situations.
However, we argue that when a transforming technology emerges, it becomes necessary
for firms to take action. Often, this action may involve the direct adoption of technology
associated with the transformation or technology which helps the firm to manage the
challenges of the transformation. In this case, as each of the three transformational
factors related to the used car industry involves some change in their interactions with
customers, we argue that customer-focused information systems represents a technology
likely to make an important difference in overall performance by enabling firms to deal
with the challenges created by the Internet and infomediaries. The willingness of a firm
- 11 -
to implement and use customer-focused systems is therefore expected to be associated
with performance outcomes of the related business processes.
The strategic orientation of the firm is one way to capture the willingness of an
organization to adopt technologies. The characterization of strategic orientation has
taken two different general strategies: (1) the characterization of different strategic types
and (2) the identification of strategic characteristics. The first method argues that there
are general ways of characterizing organizations which are essentially value neutral—i.e.,
neither good nor bad—and performance results from the alignment between the different
parts of the organization or between the organization and the environment. Miles and
Snow's (1978) topology of Defenders, Analyzers, and Prospectors is an example of this
method of characterizing strategic orientation. This view of strategic orientation has been
used as a way to understand the alignment with respect to both the IT (Sabherwal and
Chan 2001) and the marketing function (Vorhies and Morgan 2003).
The second method of conceptualizing strategic orientation is to measure it across
different specific constructs. In this case, rather than being of a strategic type (i.e.,
defender, prospector, etc.) organizations are either high or low in a trait used to
conceptualize aspects of the strategy of the organization. Work by Venkatraman (1989)
developed six general measures of strategic orientation capturing dimensions: (1)
aggressiveness, (2) analysis, (3) defensiveness, (4) futurity, (5) proactiveness, and (6)
riskiness. These general dimensions of strategic orientation, however, are limited in
some cases because of their generality. By attempting to capture all aspects of an
organization's strategy, they may be unduly complex in addressing specific contextual
challenges the organization may face. As an alternative, researchers have developed
- 12 -
constructs capturing specific organizational traits that lead to performance for such things
as innovation (Hurley and Hult 1998; Lukas and Ferrell 2000) and entrepreneurship
(Dess and Lumpkin 2005; Jambulingam et al. 2005; Wiklund and Shepherd 2005).
As the objective of this work is to identify those characteristics which enable
organizations to perform well during an IT-enabled transformation, we chose to capture
the strategic orientation of the organization using specific rather than general dimensions
of strategic orientation. In reviewing the extant literature on the various concepts
researchers have used to describe an organization's orientation toward the types of
challenges most prominent in IT-enabled transformations, we identified three relevant
constructs: (1) technological opportunism, (2) market orientation, and (3) entrepreneurial
orientation. Each of these constructs represent organizational characteristics which
enable a response to challenges faced by the organization during an IT-enabled
transformation, as are further discussed below.
Technological Opportunism
Technological opportunism refers to the willingness of the organization to
identify, understand, evaluate, and respond to new technologies (Srinivasan et al. 2002).
Technological opportunism is consistent with several other streams of research
examining firm processes as they relate to new technologies, such as Wheeler's (2002)
Net-Enabled Business Innovation Cycle (NEBIC). From a strategic perspective,
technological opportunism is an organizational characteristic similar to what is identified
in the Miles and Snow (1978) typology as a prospector firm, but with specific orientation
toward technology (Srinivasan et al. 2002). Thus, technological opportunism is expected
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to be an important factor in capturing a firm's willingness to respond to the technological
changes associated with an IT-enabled transformation.
Market Orientation
Within the marketing field there has been a long history of focus on the
importance of the customer relationship (Kotler 1967), and market orientation is a critical
construct which researchers have extensively used to understand a firm's strategic
orientation toward its customers. Market orientation identifies the degree to which a firm
engages in behaviors associated with the identifying and satisfying of customer needs,
behaviors found to have many positive outcomes for firms (for a review, see Kirca et al.
2005). Research has identified the positive relationship between market orientation and
customer and employee outcomes, innovation, and organizational performance (Jaworski
and Kohli 1993; Navar and Slater 1990). As a result, market orientation provides a way
of assessing the willingness of the firms to respond to the needs of their customers
through the application of technology.
Entrepreneurial Orientation
The organizational literature has addressed the overall importance of taking risks
and making the changes necessary to take advantage of new opportunities.
Entrepreneurial orientation captures the degree to which firms engage in processes,
practices, and decision-making styles that involve risk taking and may lead to entry in
new markets (Covin and Slevin 1989; Lumpkin and Dess 1996; Miller and Barbosa
1983). Entrepreneurial orientation has also been found to be an importation factor in firm
performance and innovation (Attuahene-Gima and Ko 2001; Lee et al. 2001; Lumpkin
and Dess 1996; Zhou et al. 2005). In the context of an IT-enabled transformation,
- 14 -
entrepreneurial orientation captures important characteristics which may cause firms to
have a willingness to enter newly-created markets provided by the Internet and electronic
commerce.
Change Enablers
In this section we integrate several streams of literature to identify firm
characteristics which facilitate and enable change. These include IT capabilities, climate
for IT use, and mindfulness of IT adoption.
IT Capabilities
IT capabilities refer to the ability of the organization to implement and manage
technologies and are based on the resource based view (RBV) of the firm. Originating in
the field of strategic management, the RBV of the firm (Barney 1986; Barney 1991;
Penrose 1959; Wernerfelt 1984) has become a critical lens through which researchers
have examined the drivers of firm performance and competitive advantage. The RBV
suggests that firms compete on the basis of VRIN resources—those that are valuable,
rare, inimitable, and non-substitutable—and argues that the possession of VRIN
resources can lead to a strategic competitive advantage (Barney 1991; Conner 1991).
While some have noted that defining a resource as something which leads to competitive
advantage is tautological (Priem and Butler 2001a, b), the RBV has been a useful lens for
organizational researchers from strategy (Brush and Artz 1999; Mahoney and Pandian
1992; Peteraf 1993; Tippins and Sohi 2003), marketing (Hunt and Morgan 1996; Slater
1997; Slotegraaf et al. 2003; Vorhies and Morgan 2005), human resources (Becker and
Gerhart 1996; Bowen and Ostroff 2004; Colbert 2004; Delaney and Huselid 1996), and
- 15 -
IS (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer 2002) to explain
both organizational performance and business process performance.
Although there are numerous dimensions of IT capabilities that have been
identified (for a review, see Wade and Hulland 2004), IT management capabilities and IT
infrastructure represent two of the most critical. IT management capabilities reflect an
organization's ability to manage IT implementation projects and implement new systems,
and they have been argued to be the only IT-related capabilities which lead to sustained
competitive advantage (Clemons and Row 1991). IT infrastructure is a more
fundamental enabler, providing organizations with a foundation of technology on which
to build new IT innovations (Zhu 2004). Together the ability to manage technology and
the infrastructure on which to build new technology applications provide an important
way to characterize the overall IT capabilities of an organization.
Climate for IT Use
Numerous individual-level studies have identified the importance of social
influence on IT adoption and use (for a review, see Venkatesh et al. 2003). In this
research we introduce climate for IT use as employees' shared perceptions of the
importance of IT use within the organization. Climate has been defined generally as the
message employees get about the values which are important to the organization
(Schneider and Bowen 1995), and climate has been used to understand technology
implementations (Klein et al. 2001), service outcomes (Glisson and James 2002;
Schneider et al. 1998) and product innovation (Wei and Morgan 2004). In its essence,
climate provides a way to understand how the collective policies and practices of the
organization influence the willingness of individuals to behave in specific ways—here,
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related to the use of new IT. Climate for IT use is expected to be an important factor in
understanding how organizations implement necessary change related to the assimilation
of IT innovations.
Mindfulness of IT adoption
The dominant paradigm within the IT literature has emphasized the positive
aspects of IT adoption and investment. However, recent work has highlighted the
important role of mindfulness of IT adoption in making IT-related decisions (Butler and
Gray 2006; Fichman 2004; Swanson and Ramiller 2004). Organizational mindfulness
originates in the study of high reliability organizations, such as aircraft carriers and
nuclear power-generation stations, where failure and experimentation are untenable
(Weick et al. 1999). In studying IT adoption, mindfulness provides a way of
understanding how organizations address adoption decisions related to new technologies
(Butler and Gray 2006; Fichman 2004; Swanson and Ramiller 2004). Not all
technologies may be valuable, and the mindful organization is cognizant of that. Thus,
mindfulness influences the resulting value gained from technology adoption through the
improvement of decisions related to the adoption itself—i.e., mindful organizations adopt
IT only when it makes business sense and fits the needs of the organization within the
specific competitive environment. In addition to influencing technology adoption
choices, mindfulness is also expected to play an important role in what an organization
does with a technology once it is adopted.
Assimilation, Use, and IT Value
An extensive literature exists on the adoption, assimilation, and use of IT
innovations, and a thorough review is beyond the scope of this work. However, in
- 17 -
reviewing relevant points from this literature, two important observations can be made:
(1) technology assimilation is a critical mediator in generating value from technology,
and (2) understanding the specific conceptualization of assimilation is important to
understanding the value created by a given technology.
The assimilation of IT has been characterized as a series of key events, the most
significant being the acquisition of the system, (i.e., the technology is purchased by the
organization) and the deployment of the technology throughout the organization (i.e., the
organization implements and begins to use the system). Information technology cannot
create value for an organization unless it is both acquired and deployed in the
organization. The conceptualization of assimilation as an event or series of events,
however, is very limited in helping to develop an understanding of how organizations
obtain value from technology. In other words, the treatment of assimilation as binary
(yes/no) provides very little insight into how characteristics of the implementation itself
may influence performance outcomes. As an alternative, many researchers have instead
opted for technology use as a key way to capture the assimilation of information
technology within organizations (e.g., Armstrong and Sambamurthy 1999; Devaraj and
Kohli 2003; Massetti and Zmud 1996; Zhu and Kraemer 2005). Generally, those
organizations which use a system more extensively are argued to obtain more value from
it, and empirical studies have largely supported this assertion (Armstrong and
Sambamurthy 1999; Devaraj and Kohli 2003). This is the same underlying assumption
that has driven much of the focus on use as a key measure of success at the individual
level (DeLone and McLean 2003; DeLone and McLean 1992).
- 18 -
While use provides a way of characterizing how a technology has been
assimilated within an organization, it has some limitations. The one-dimensional
characterization loses potential richness in how the assimilation occurs. For example, use
as characterized by employee interactions with a technology (e.g., Zhu and Kraemer
2002) may be much different than measures of actual use (e.g., Devaraj and Kohli 2003).
Although issues related to differences in the objective and subjective measures of use
have been extensively examined in the study of use on the individual level (Ettema 1985;
Straub et al. 1995), these differences are likely to be exacerbated in the study of use at the
organizational level, as organizational level use may include both the employees which
use the technology and the automated processes which are configured as part of the
technology implementation.
As an alternative to the single dimensional conceptualization of use, in this
research we conceptualize assimilation as a second-order construct consisting of the use
of employees and the automation of the technology implementation itself. Use captures
the extent to which individuals within the organization look to the technology as an
effective solution to tasks. Automation instead captures the extent to which different
parts of the business process have been automated as part of the IT implementation. By
capturing separately use originating from the employees (use) and the implemented
technology itself (automation) through a second-order construct, we provide a richer
treatment of the understanding of assimilation and thereby extend theory focusing
directly on the technology artifact (Benbasat and Zmud 2003; Orlikowski and Iacono
2001).
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1.4 RESEARCH MODEL AND HYPOTHESES
This section provides the theoretical background and justification for the model
outlined in Figure 1.2. In supporting this model, we integrate the streams of research
outlined earlier to predict both the overall level of assimilation and the resulting business
process performance.
Strategic Change Orientation
Each of the three dimensions of SCO—technological opportunism, market
orientation, and entrepreneurial orientation—are associated with specific organizational
motivations and have associated organizational processes involved in the fulfillment of
these motivations. Organizations high in technological opportunism have an underlying
motivation and the processes to identify technologies relevant to business (Srinivasan et
al. 2002). Likewise, organizations high in market orientation have underlying
motivations to meet the needs of customers and the processes in which to identify when
actions are necessary in order to meet those customer needs (Day 1994; Kirca et al.
2005). Finally, organizations high in entrepreneurship orientation have motivation to
identify new markets in which to compete and the processes in place to identify those
market opportunities (Attuahene-Gima and Ko 2001; Zhou et al. 2005). Thus, though
three organizations, each high in only one of the dimensions of SCO, may react to
different stimuli of an IT-enabled transformation, they may each enable a comparable
response.
SCO and IT Capabilities
SCO is expected to positively influence the level of IT capabilities. The primary
theoretical understanding of how organizations develop capabilities is through the
- 20 -
concept of dynamic capabilities. Dynamic capabilities refer to those firm capabilities that
enable organizations to integrate, build, and reconfigure to address changes in the
competitive environment (Teece et al. 1997). This perspective has provided an important
way for researchers to understand how firms develop IT-related capabilities in the midst
of continually changing technologies (Daniel and Wilson 2003; Sambamurthy et al. 2003;
Wheeler 2002; Zhu and Kraemer 2002).
While the conceptualization of dynamic capabilities as a mechanism through
which organizations develop capabilities has been extensively utilized, limited research
has identified specific constructs which constitute dynamic capabilities. Here, we argue
that SCO constitutes a dynamic capability because of its role in initiating changes. The
presence of stimuli associated with the IT-enabled transformation will enable firms high
in SCO to develop IT capabilities. In other words, the strategic processes associated with
SCO will lead organizations to build the IT capabilities necessary to meet the challenges
and opportunities of the competitive environment. As a result, we hypothesize:
H1: Strategic change orientation is positively associated with IT-capabilities.
SCO and Climate for IT Use
Climate, as noted earlier, can be generally characterized as the message the
employees receive from the organization. When introducing IT to a particular situation,
research has found that contextual factors can make a tremendous difference in both how
the technology is perceived and how it is appropriated by members of the organization
(Barely 1990; DeSanctis and Poole 1994; Fulk 1993). As a result, when technology
becomes prevalent in a new context, the messages the members of the organization
receive about this technology, which form the climate for IT use, can be expected to
- 21 -
originate from other more fundamental organizational characteristics captured by SCO.
For example, if an organization high in market orientation had already established the
importance of meeting customer needs, a CRM software package identified as a way to
meet customer needs would more likely lead to a positive climate. Similar arguments
would hold for both technological opportunism and entrepreneurial orientation.
Therefore, we expect:
H2: Strategic change orientation is positively associated with the climate for IT
use.
SCO and Mindfulness of IT Adoption
Like the various components of SCO, mindfulness implies a distinct and
measured process of issue identification, consideration, and response. This process
involves matching the needs of the organization with the offerings of the technology.
The opposite of mindfulness—i.e., mindlessness—implies that decisions are made as a
result of trends and fads without consideration of the objective business case (Fiol and
O'Connor 2003; Krieger 2005). SCO is expected to increase mindfulness through the
increased sophistication of the decision making process. Thus, organizations high in
SCO may be expected to more easily adopt mindful practices. It follows that:
H3: Strategic change orientation is positively associated with the mindfulness of
IT adoption.
IT Capabilities and IT Assimilation
IT capabilities and the broader theoretical lens of the RBV have provided
researchers with an important way to understand how the effective management of
technology can lead to improved business performance (Bharadwaj 2000). IT
- 22 -
capabilities have been argued to be multidimensional (for a review, see Wade and
Hulland 2004), and the two dimensions examined here—IT management capabilities and
IT infrastructure—influence assimilation in different ways. IT management capabilities
may improve assimilation of the technology through improved system planning or
implementation (Clemons and Row 1991; Mata et al. 1995). These capabilities allow
organizations to more effectively bring IT-related innovations from the decision to adopt
the technology through to full organizational assimilation.
IT infrastructure has also been found to be a critical firm capability necessary to
fully take advantage of new technologies (Armstrong and Sambamurthy 1999; Keen
1991; Weill and Broadbent 1998). IT infrastructure can be categorized as a strategic
option or real option (Bowman and Hurry 1993). The options lens provides a way for
managers to view IT investments when the level of uncertainty is high, investments are
irreversible, and projects are flexible in nature (Dixit and Pindyck 1994). The option
enabled by infrastructure technology is the option to implement more complex
technologies in the future, such as customer-focused information systems. A strong
infrastructure may also enable the organizations to assimilate complex technologies more
rapidly and at lower cost. Infrastructure may enable greater integration between systems
and fewer user problems related to system failure and or unavailability. Together, IT
management capabilities and IT infrastructure provide an important way of characterizing
the IT capabilities of an organization and are expected to influence the level of IT
assimilation that occurs. As a result, we hypothesize:
H4: IT capabilities are positively associated with IT assimilation.
Climate for IT Use and Assimilation
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As indicated, climate originated as a way to conceptualize the message those in
the organization receive relative to a specific action. A positive climate for IT use will
improve assimilation in two key ways. First, because the general attitude of the
organization toward a given technology improves with a more positive climate, the
degree to which the individuals within that organization will use the systems involved
will increase. Research on individual-level IT adoption has consistently shown
subjective norm to be an important influence of an individual's use of the technology
(Agarwal 2000; Karahanna and Limayem 2000; Venkatesh et al. 2003). This collective
effect, captured on the organizational level through climate for IT use, is expected to
influence overall assimilation levels. Second, as much of the investment in
configuration, integration, and automation will only provide payback to the organization
if associated systems are used by individuals, a positive climate for IT use will lead
organizations to increase their investment in automated processes with the expectations
of greater returns from use. In other words, organizations with more positive climates for
IT use will invest more in assimilation because they expect the technology to be used and
provide value. For these reasons, we hypothesize:
H5: Climate for IT use is positively associated with IT assimilation.
Mindfulness of IT Adoption and Assimilation
Mindfulness of IT adoption is also expected to be positively related to the level of
IT assimilation. The importance of mindfulness in the adoption and appropriation of
technology rests on the belief that engaging in systemic processes will lead to better
decisions in evaluating a technology (Swanson and Ramiller 2004). In other words,
sense-making processes involving the business implications of a new technology may be
- 24 -
necessary in order to effectively assimilate that technology into the organization (Anand
and Peterson 2000). Mindful organizations will adopt the technologies that will be the
most beneficial and pass on those technologies that involve a fad or bandwagon effect.
The overall effect is that for a given group of organizations which have adopted a
technology, those which engaged in mindful processes will be more likely to have a good
fit between the technology and the organization. This fit can be expected to improve the
overall assimilation within the organization, as the IT can be more easily integrated into
business processes. In addition, as the process of mindful adoption is likely to inform the
assimilation process more than mindless processes, mindful organizations will be more
likely to take the implementation steps necessary to promote assimilation. Thus, we
hypothesize:
H6: Mindfulness of IT adoption is positively associated with IT assimilation.
Assimilation and Performance
The extent of assimilation is also expected to be positively associated with
performance. This general point has been made in the study of individual use of IT
(Davis 1989; Venkatesh and Davis 2000; Venkatesh et al. 2003), IT appropriation or
structuration (DeSanctis and Poole 1994; Orlikowski 1992, 2000), and technology
assimilation (Armstrong and Sambamurthy 1999; Chatterjee et al. 2002; Fichman and
Kemerer 1999; Gallivan 2001). Individuals, groups, and organizations must use
technology in order for the technology to have an impact on organizations. As noted by
Orlikowski (1992, p. 410), "On its own technology is of no import; it plays no
meaningful role in human affairs. It is only through the appropriation of technology by
humans (whether for productive or symbolic ends) that it plays a significant role and
- 25 -
exerts influence." Further, empirical studies have found the extent of IT assimilation and
use to be an important mediator in the value generated by an IT system (Devaraj and
Kohli 2003; Zhu and Kraemer 2005). Therefore, we hypothesize:
H7: Assimilation is positively associated with process performance.
H8: Assimilation is positively associated with financial performance.
An additional relationship between business process performance and financial
performance is expected. Process performance benefits the organization in terms of
operational benefits associated with productivity and inventory management. These
intermediate process-related outcomes also have a relationship to financial performance,
as improved productivity and lower inventory costs are also likely to result in improved
financial performance. As a result, we hypothesize:
H9: Process performance is positively associated with financial performance.
1.5 RESEARCH METHODOLOGY
Sampling and Data Collection
To facilitate the execution of this study, we partnered with a large online
infomediary and CRM software provider for the retail auto industry, which we refer to
hereafter as NetAuto. This partnership allowed access to senior level individuals within
organizations which had implemented CRM software packages, and the organization had
an existing mechanism to distribute and collect surveys. The subject pool included the
sales, service, and general managers of auto retailers, identified from a listing of
NetAuto's customers and public listings of auto retailers. Analysis was conducted on the
level of the business unit, with sales and service business units measured separately. The
population of dealerships selected with validated contact information was 893. An email
- 26 -
was sent to each individual asking them to participate in the study. In the email, we
included a brief description of the study, informed them that the survey will take
approximately 15-20 minutes to complete, and provided the hyperlink to the online
survey. The first thing the respondent saw when directed to the survey was the informed
consent form. After reading the consent form and selecting "I Agree," the dealership
general manager, sales manager, or service manager began the survey. In an effort to
improve the response to the survey, participants were offered a chance to be randomly
selected for a prize of an IPOD Nano (4 were given away) and offered a summary of the
results of the survey when completed. In addition, reminder letters were sent 2 weeks
and 4 weeks after the initial contact, and follow-up phone calls were conducted during
the 3 months following the initial contact.
Measures
Constructs were measured using scales that have been validated in previous
research or developed in conjunction with industry professionals in order to ensure
content validity. Unless specifically indicated otherwise, we measured items on a 7-point
Likert scale with anchors 1 = "strongly disagree" and 7 = "strongly agree."
A full listing of the survey items is found in Appendix A.
Technological opportunism was measured using an abbreviated version of the
scale developed by Srinivasan et al. (2002). A sample item for technological
opportunism is "we actively seek intelligence on technological changes in the
environment that are likely to affect our business." Measures of market orientation
capture the degree to which the firm places customer needs at the top of its organizational
chart, but the debate of how to measure market orientation has been extensive in the
- 27 -
marketing literature (Homburg and Pflesser 2000). The main division in the
measurement of market orientation is whether market orientation is a series of
organizational processes (Kohli and Jaworski 1990) or whether it is a type of culture
(Navar and Slater 1990). The Navar and Slater scale was selected because it exhibited a
high level of efficiency—i.e., it used fewer items to obtain a consistent measurement
(Matsunoa et al. 2005). Because of the differing features of customer interactions in
different industries, market orientation scales are frequently updated and adapted to the
specific context in which they occur (Attuahene-Gima and Ko 2001; Dobni and Luffman
2003; Zhou et al. 2005). As a result, measures were adapted to conform to the retail auto
industry. A sample item from the market orientation scale includes "We closely monitor
and asses our level of commitment in serving customers' needs." Measures for
entrepreneurial orientation capture the degree to which the organization engages in risk-
taking and proactive behaviors and were based on Covin and Slevin (1989). A sample
item for entrepreneurship orientation is "We are very often the first business to introduce
new services to customers."
Measures of IT management capabilities and IT infrastructure were adopted from
Bharadwaj et al. (1998). IT management capabilities capture the extent to which the
organization effectively manages the IT function, including processes associated with the
delivery, operation, and maintenance of systems. A sample item for IT management
capabilities is "Our IT management team has established an effective system for IT
planning." IT infrastructure captures the extent to which the organization has a
foundation of networks, system architectures, and computers necessary for ensuring
adequate processing capabilities. A sample item for IT infrastructure is "Our corporate
- 28 -
infrastructure (computers, networks, etc.) has appropriate network architecture to meet
business needs."
Measures for climate for IT use were adopted from Schneider et al. (1998)'s measures
of service climate and capture the extent to which employees within the organization
receive a positive message about the use of IT. A sample item for climate for IT use is
"How would you rate.the recognition and rewards employees receive for using the
customer management system(s)?" The anchors low (1) to high (7) were used.
Measures for mindfulness of IT adoption were adapted from Knight's (2004)
development of measures of collective mindfulness. These measures incorporated
organizational qualities such as preoccupation with failure and reluctance to simplify
interpretations (Weick et al. 1999). A sample item for mindfulness is "Our senior
managers...take choices of whether we adopt a new technology very seriously."
Measures for the assimilation of systems are typically context-specific. As described
earlier, our assimilation measures separately captured the degree to which the systems
were used by individuals within the organization and the degree to which the systems
were automated. In addition, we developed separate measures for assimilation in sales
and service business units. These measures were developed through an examination of
customer-focused systems and extensive consultations with industry experts and actual
dealers. Measures for use captured the degree to which the systems were utilized by
individuals within the organization to both store and gather information and analyze
business processes. A sample item for use in the sales area included "Our dealership uses
customer management system(s) to...record interactions with customers (i.e., phone calls,
customer needs)." A sample item for use in the service area included "Our dealership
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uses customer management system(s) to...record interactions with customers (i.e., service
visits, direct marketing materials)." The measure for automation captured the degree to
which systems had business and work tasks configured to run automatically. A sample
item for automation in the sales area was, "Automated scheduling of tasks for members
of the sales force." A sample item for automation in the service area was, "Automated
creation of personalized service reminders." Scales for automation were adopted from
Ray et al. (2005) and included the anchors: 0=Don't Intend to Implement, 1= Not yet
begun, 3 = Standard Implementation, 5 = Advanced Implementation.
Objective performance data is often very difficult to obtain for privately-held
firms and when data is needed on the level of the strategic business unit (SBU). As a
result, researchers frequently collect performance data through survey measures. Prior
research has found a high degree of correlation between subjective and objective
measures of performance (Dess and Robinson 1984; Gerhart et al. 2000; Wall et al.
2004), and subjective measures of performance have frequently been utilized in
organizational research (Barua et al. 2004; Guthrie 2001; Youndt et al. 1996; Zhou et al.
2005). With regard to the performance of firms, we are interested in both the process and
financial measurements of performance at the level of the business unit. In addition, as
different business units have different performance measures, we utilized business unit
specific measures of process performance to capture operational improvements. A
sample item for process performance in the sales area was "Please describe the extent
customer management systems have affected.the level of service provided to
customers." A sample item for process performance in the service area was "Please
describe.the utilization of the service area (i.e., the percentage of capacity used)."
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Financial performance captured the sales growth and profit level for both the period
1994-1996 (i.e., before the transformation) as well as the period 2002-2004 (i.e., after the
transformation). Answers were captured using a 7-point Likert scale with anchors 1 =
"much worse than competitors" and 7 = "much better than competitors."
From the original 893 individuals contacted, 153 useable responses were received,
representing a response rate of 17%. Of the respondents, 106 answered questions related
to the performance of the sales area and 47 related to the service area. The average
number of employees among the respondent organization was 70.1 (SD=58.9). To
address concerns of non-response bias (Armstrong and Overton 1977), we compared the
responses of those individuals who responded after the initial email to those who had
responded after the phone follow-up. We did not find a statistical difference between
these two groups.
Analysis
To establish the convergent and divergent validity of the constructs and to
examine the statistical significance of the proposed relationships, we used PLS. PLS has
advantages over traditional regression-based analysis and covariance-based structural
models because it has minimal requirements for sample size and makes few normality
assumptions (Chin 1998). Our analysis involved two stages. We first examined the
convergent and discriminant validity of the constructs through the measurement model
and then examined the full set of structural relationships.
Measurement Model
Descriptive statistics for the constructs are shown in Table 1.1. Construct validity
analysis with PLS was completed in accordance with the recommendations of Gefen and
- 31 -
Straub (2005). Convergent validity accesses the degree to which the item measures
represent a single construct. Outer model loadings greater than 0.70 are considered to
indicate adequate convergent validity (Fornell and Larcker 1981). As shown in Table
1.4, outer model loadings for each item were greater than 0.7, with most greater than 0.8.
Additional measures of convergent validity shown in Table 1.1 include Cronbach's alpha
and the reliability coefficient (P
c
), each further supporting the convergent validity of the
item measures.
Discriminant validity assesses the degree to which item measures represent
unique constructs. Using PLS, discriminant validity is assessed through two criteria
(Chin 1998; Gefen and Straub 2005): (1) cross loadings for the item-factor correlation
table should be small and (2) the square root of the average variance extracted should be
larger than the inter-construct correlations. As is shown by the item-factor correlations in
Table 1.2 and the correlations matrix in Table 1.3, the measures show a high level of
discriminant validity. In sum, the results of the measurement model display an adequate
level of convergent and discriminant validity.
Structural Model
In the PLS structural model, path coefficients can be interpreted in the same way
as beta coefficients for regression analysis. As SCO, IT capabilities, and assimilation
each included second order constructs, the overall structural model was completed using
the factor scores from the confirmatory factor analysis, and all first order factors were
modeled as reflective. SCO was measured as a formative construct while IT capabilities
and assimilation were each measured with reflective indicators. As mentioned in the
theory, SCO can originate in any of the three strategic orientations, leading to a
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conceptualization that is formative in nature. Dimensions of IT capabilities and
assimilation, on the other hand, are expected to capture underlying qualities of the
organization and are thus modeled as reflective.
All path coefficients and significance levels are shown in Figure 1.3. Strategic
change orientation was highly related to IT capabilities (48% of variance explained),
climate for IT use (47% of variance explained), and mindfulness of IT adoption (51% of
variance explained). IT capabilities, climate, and mindfulness of IT adoption were
slightly more weakly related to assimilation, jointly explaining nearly 47% of the
variance in IT assimilation. IT assimilation was more strongly related to process
performance (34% of variance explained) than financial performance (31% of variance
explained), though both relationships were significant. The relationship between process
performance and financial performance was in the hypothesized direction but not
significant. Controls for sales vs. service were significant for the prediction of
assimilation but not for performance. Overall, with the exception of H6 all hypotheses
were supported.
1.6 DISCUSSION
A great deal of research has established the positive link between IT investment
and firm performance (for a review, see Kohli and Devaraj 2003). However, the
mechanisms through which value gets created and the organizational characteristics
which enable some organizations to obtain more value than others are still topics of open
interest for researchers and practitioners alike. In this work, we have integrated research
from multiple theoretical foundations as a way to understand how organizations
effectively respond to an IT-enabled transformation. Within a general theoretical
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framework linking strategic orientation, change enablers, assimilation, and performance,
we are able to identify theoretical mechanisms through which strategic orientation can
have a performance impact. In doing so, we also identify and empirically measure
organizational traits which improve the assimilation and resulting benefits from a given
technology investment.
Strategic change orientation has provided a useful construct through which to
understand how strategic characteristics of the organization may influence performance
during an IT-enabled transformation. Technological opportunism, market orientation,
and entrepreneurial orientations are highly related constructs that capture aspects of how
organizations respond to change. Though organizations may respond for different
reasons when faced with a competitive challenge, jointly understanding those
characteristics which provide the underlying motivation to change is important to both
manage change and understand the relevant benefits and challenges.
Implications for Theory
In addition to providing a look into those organizational characteristics which
promote performance during an IT-enabled transformation, this work may also provide
an integrated way of understanding the assimilation of different technologies within
organizations. This can also be understood as an organizational-level model of IT use.
The likely progression of an organizational-level model of IT use can be understood
through the extensive work on technology adoption and use at the individual level. The
technology acceptance mode (TAM) was integrated with social aspects of the theory of
reasoned action (TRA), the performance aspects of social cognitive theory (SCT), and
other contributing research to eventually emerge as the Unified Theory of Acceptance
- 34 -
and Use of Technology (UTAUT, Venkatesh et al. 2003). Similarly, a unified model
explaining why organizations use IT—i.e., an organizational level model of use—will
likely incorporate many of the constructs identified here. In sum, we argue that this
research provides an important extension to the understanding of IT use as an
organizational-level construct.
Implications for Practice
A key implication for practice is that organizations may influence the benefits
they obtain from IT investments as a result of their strategic orientation. This could have
both positive and negative feedback loops which may accentuate the effects on the
organization. Organizations with high levels of strategic change orientation may obtain
benefits which further support the ability to both use technology and to change
organizational practices. Similarly, organizations with low levels of strategic change
orientation may have little success with the assimilation of technology, further pushing
them towards both the avoidance of technology and change. In addition, this research
stresses the importance of establishing both technology use and automation as part of any
IT implementation.
Limitations
Prior to concluding, the limitations of the work must be acknowledged. External
validity can be assessed by considering the context in which the research took place. In
this research, we surveyed managers within a single industry—auto retailing. One
concern is that a single industry study may result in theory which is not generalizable to
other industries and contexts. While the fact that this work incorporated data from two
business units (sales and service) helps to mitigate this concern, future work should
- 35 -
examine the identified relationships within additional industries and technologies to
access to what extent the findings are generalizable.
An additional limitation of the work is that independent and dependant measures
were each gathered through survey techniques, thereby introducing the possibility of
common method bias. While a Harmon-single factor test for common method bias
indicated that no more than 34% of the variance could be explained by any one factor,
consistent with other works utilizing surveys, future work should assess to what extent
the model adequately predicts objective financial performance measures.
A third potential source of bias is related to the use of a single key informant to
obtain information about the organization. While key informants are frequently used
within the organizational literature, there is a great potential of measurement error when
organizational properties are identified by only one person. Individuals may answer
questions in which they have limited knowledge of the specific domain, leading to
assessments which do not specifically relate to the dependant variables.
A fourth potential source of bias is related to the use of the customers from a
single organization—i.e., NetAuto. While this enabled us to examine the role of
organization factors in a situation in which the technology is held constant, this
introduces a selection bias. If there were specific organizational traits which caused the
organization to adopt the CRM system in the first place, it may be that this research could
under or over estimate the magnitude of the relationships for the industry in general.
However, in conversations which numbers dealerships we found that the there were no
reasons to believe that the dealers surveyed differed systematically from the overall
population of dealerships.
- 36 -
1.7 SUMMARY
Information technology can have an important influence on competition in many
industries, and effectively responding to IT-enabled transformations can be critical to
firm performance. By understanding better how organizations can respond to IT-enabled
transformations, we can provide both practical recommendations to managers and
technology vendors while improving the value organizations derive from IT. This
research integrates theoretical perspectives which capture both the willingness and the
ability of the organization to respond to change.
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1.8 FIGURES
FIGURE 1.1 - CONCEPTUAL FRAMEWORK
FIGURE 1.2 - RESEARCH MODEL
- 38 -
1.8.3 FIGURE 1.3 - PLS RESULTS
Note: 1. *** significant at 0.001, **significant at 0.01, *significant at 0.05.
- 39 -
1.9 TABLES
TABLE 1.1 - DESCRIPTIVE STATISTICS FOR SURVEY MEASURES
Variable Mean Std Chronbach P
c
Alpha
SCO: Technological Opportunism (TO) 4.976 1.485 0.930 0.944
SCO: Market Orientation (MO) 6.215 0.941 0.824 0.876
SCO: Entrepreneurial Orientation (EO) 5.125 1.307 0.801 0.872
ITCAP: IT Management Capabilities
(ITMAN) 5.186 1.342 0.907 0.934
ITCAP: IT Infrastructure Capabilities
(ITIN) 5.567 1.324 0.943 0.958
Climate for IT Use (CLIM) 5.077 1.496 0.915 0.940
Mindfulness of IT Adoption (MIND) 5.622 1.205 0.947 0.956
Assimilation: Automation (AUTO) 3.394 1.187 0.846 0.897
Assimilation: Use (USE) 5.269 1.724 0.891 0.926
Process Performance (PP) 5.347 1.151 0.828 0.970
Financial Performance Control (FPC) 4.631 1.362 0.936 0.956
Financial Performance (FP) 5.308 1.289 0.910 0.901
Note: P
c
= Composite Reliability = (Eì
i
)
2
/[(Eì )
2
+E(1-ì
2
)] where ì is the factor loading.
i i i
- 40 -
1.9.2 TABLE 1.2 - CORRELATION MATRIX
Variable 1 2 3 4 5 6 7 8 9 10 11 12
1 SCO: TO 0.837
2 SCO: MO 0.518 0.721
3 SCO: EO 0.621 0.519 0.717
4 ITCAP: ITMAN 0.607 0.462 0.505 0.818
5 ITCAP: ITFRA 0.372 0.389 0.435 0.367 0.869
6 CLIM 0.578 0.570 0.544 0.497 0.621 0.830
7 MIND 0.634 0.580 0.497 0.616 0.484 0.654 0.799
8 ASSIM: AUTO 0.371 0.369 0.278 0.383 0.321 0.445 0.386 0.752
9 ASSIM: USE 0.511 0.471 0.392 0.529 0.460 0.562 0.578 0.610 0.802
10 FPC2 0.179 0.142 0.154 0.207 0.028 0.041 0.167 0.091 0.118 0.944
11 FP2 0.389 0.290 0.298 0.387 0.226 0.255 0.298 0.201 0.353 0.469 0.922
12 PP 0.363 0.358 0.350 0.543 0.378 0.500 0.376 0.497 0.539 0.186 0.307 0.803
Note: The bold values along the diagonal are the square root of the AVE (Average Variance Extracted). The off-diagonal variables
are the correlations among the constructs. For discriminant validity, the square root of the AVE should be larger than the correlations
(Gefen and Straub 2005).
- 41 -
TABLE 1.3 - ITEM CORRELATION TABLE
1 2 3 4 5 6 7 8 9 10 11 12 TO1 0.92 0.49 0.55 0.53
0.35 0.54 0.63 0.37 0.48 0.21 0.35 0.31 TO2 0.90 0.45 0.54 0.56
0.32 0.50 0.63 0.33 0.46 0.14 0.29 0.33 TO3 0.87 0.48 0.58 0.49
0.33 0.51 0.53 0.30 0.50 0.11 0.39 0.27 TO4 0.91 0.44 0.56 0.60
0.34 0.53 0.49 0.33 0.40 0.18 0.36 0.39 MO1 0.44 0.80 0.36
0.45 0.37 0.46 0.50 0.24 0.46 0.14 0.29 0.25 MO2 0.44 0.82
0.55 0.31 0.46 0.59 0.53 0.43 0.46 0.07 0.21 0.38 MO3 0.40
0.77 0.27 0.40 0.09 0.31 0.42 0.24 0.34 0.21 0.30 0.23 MO4
0.37 0.80 0.45 0.33 0.28 0.44 0.39 0.25 0.23 0.04 0.14 0.27
EO1 0.53 0.47 0.84 0.46 0.35 0.41 0.44 0.24 0.29 0.16 0.28 0.32
EO2 0.60 0.42 0.83 0.52 0.42 0.48 0.49 0.21 0.32 0.11 0.22 0.33
EO3 0.32 0.37 0.74 0.23 0.34 0.40 0.26 0.17 0.31 0.13 0.20 0.16
EO4 0.48 0.38 0.76 0.36 0.27 0.44 0.35 0.27 0.33 0.08 0.25 0.28
ITMAN1 0.64 0.38 0.51 0.87 0.26 0.41 0.55 0.33 0.45 0.21 0.40 0.40
ITMAN2 0.48 0.34 0.40 0.87 0.30 0.44 0.46 0.29 0.45 0.20 0.27 0.54
ITMAN3 0.51 0.42 0.45 0.93 0.40 0.43 0.58 0.32 0.44 0.18 0.36 0.47
ITMAN4 0.53 0.49 0.44 0.86 0.32 0.47 0.58 0.41 0.54 0.15 0.33 0.50
INFRA1 0.38 0.34 0.48 0.37 0.91 0.60 0.48 0.25 0.41 0.08 0.22 0.34
INFRA2 0.34 0.37 0.33 0.31 0.90 0.54 0.44 0.30 0.41 -0.01 0.16 0.36
INFRA3 0.31 0.40 0.36 0.37 0.93 0.59 0.44 0.35 0.45 0.02 0.22 0.37
INFRA4 0.34 0.33 0.43 0.30 0.94 0.56 0.43 0.28 0.43 0.01 0.24 0.32
CLIM1 0.54 0.54 0.42 0.44 0.53 0.91 0.67 0.43 0.49 -0.03 0.20 0.42
CLIM2 0.46 0.43 0.58 0.37 0.52 0.84 0.45 0.33 0.44 0.07 0.20 0.43
CLIM3 0.55 0.56 0.46 0.48 0.59 0.93 0.69 0.42 0.54 0.02 0.23 0.45
CLIM4 0.50 0.50 0.49 0.48 0.57 0.88 0.52 0.40 0.54 0.09 0.28 0.48
MIND1 0.48 0.51 0.28 0.57 0.34 0.54 0.85 0.36 0.56 0.18 0.25 0.40
MIND2 0.55 0.48 0.34 0.57 0.34 0.56 0.87 0.37 0.56 0.18 0.22 0.37
MIND3 0.59 0.51 0.51 0.52 0.40 0.56 0.88 0.34 0.45 0.12 0.29 0.30
MIND4 0.60 0.57 0.49 0.53 0.46 0.64 0.93 0.39 0.56 0.12 0.26 0.33
MIND5 0.55 0.43 0.40 0.56 0.43 0.52 0.87 0.36 0.53 0.19 0.25 0.30
MIND6 0.58 0.55 0.54 0.57 0.48 0.60 0.86 0.27 0.44 0.22 0.32 0.33
MIND7 0.49 0.47 0.47 0.43 0.50 0.53 0.81 0.25 0.40 -0.01 0.21 0.27
AUTO1 0.30 0.24 0.18 0.25 0.14 0.24 0.22 0.78 0.46 0.16 0.14 0.38
AUTO2 0.30 0.40 0.24 0.30 0.33 0.46 0.36 0.81 0.50 0.06 0.20 0.41
AUTO3 0.29 0.30 0.19 0.30 0.21 0.34 0.32 0.88 0.51 0.03 0.11 0.42
AUTO4 0.34 0.28 0.32 0.41 0.37 0.43 0.37 0.84 0.55 0.06 0.22 0.43
USE1 0.42 0.30 0.28 0.40 0.38 0.48 0.42 0.47 0.84 0.08 0.21 0.43
USE2 0.45 0.46 0.29 0.46 0.37 0.52 0.54 0.56 0.92 0.10 0.33 0.49
USE3 0.54 0.41 0.37 0.52 0.47 0.52 0.55 0.58 0.91 0.09 0.37 0.52
USE4 0.37 0.46 0.43 0.45 0.38 0.43 0.49 0.51 0.81 0.14 0.31 0.43
FPC1 0.17 0.11 0.10 0.14 0.00 0.04 0.15 0.06 0.07 0.96 0.40 0.13
FPC2 0.17 0.16 0.19 0.25 0.05 0.04 0.17 0.11 0.15 0.98 0.50 0.22
FP1 0.38 0.27 0.25 0.35 0.20 0.23 0.28 0.16 0.29 0.49 0.96 0.28
FP2 0.37 0.29 0.32 0.39 0.23 0.26 0.29 0.22 0.39 0.41 0.96 0.31
PP1 0.30 0.28 0.31 0.50 0.32 0.41 0.32 0.38 0.47 0.14 0.26 0.90
PP2 0.36 0.35 0.31 0.53 0.36 0.48 0.38 0.51 0.56 0.20 0.36 0.94
PP3 0.29 0.30 0.31 0.36 0.30 0.42 0.26 0.38 0.33 0.13 0.13 0.76
Note: Factor numbers refer to the appropriate factors as indicated in bold.
- 42 -
TABLE 1.4 - PLS OUTER MODEL LOADINGS
PLS Outer
Item
TO1
TO2
TO3
TO4
MO1
MO2
MO3
MO4
EO1
EO2
EO3
EO4
ITMAN1
ITMAN2
ITMAN3
ITMAN4
INFRA1
INFRA2
INFRA3
INFRA4
CLIM1
CLIM2
CLIM3
CLIM4
MIND1
MIND2
MIND3
MIND4
MIND5
MIND6
MIND7
AUTO1
AUTO2
AUTO3
AUTO4
USE1
USE2
USE3
USE4
FPC1
FPC2 FP1
FP2 PP1
PP2 PP3
Weight
0.283
0.276
0.277
0.276
0.311
0.350
0.280
0.308
0.342
0.350
0.260
0.301
0.263
0.275
0.313
0.280
0.276
0.261
0.280
0.267
0.282
0.257
0.296
0.284
0.157
0.164
0.169
0.185
0.162
0.166
0.146
0.279
0.298
0.313
0.318
0.269
0.302
0.305
0.271
0.462
0.568
0.532
0.513
0.374
0.482
0.280
Model
Loading
0.915
0.905
0.871
0.906
0.800
0.820
0.775
0.802
0.842
0.831
0.740
0.760
0.866
0.874
0.932
0.863
0.914
0.899
0.935
0.940
0.912
0.844
0.932
0.882
0.846
0.874
0.879
0.931
0.874
0.863
0.811
0.781
0.809
0.881
0.838
0.843
0.917
0.907
0.813
0.964
0.976
0.958
0.955
0.897
0.939
0.758
Note: All loadings significant at p< 0.001.
- 43 -
CHAPTER 2: PROFITING FROM THE INTERNET CHANNEL: THE
COMPLEMENTARITY OF ELECTRONIC COMMERCE CAPABILITIES AND
BUSINESS PROCESS CHANGE
2.1 ABSTRACT
The emergence of the Internet channel presents opportunities and challenges for
traditional retailers. As a result, understanding those factors that enable retailers to
benefit from the Internet channel can aid in management decision making as well as
contribute to the stream of research addressing electronic commerce. In this essay, we
develop and empirically test a model which examines the complementarities between
electronic commerce capabilities and business process change in influencing Internet
channel performance. A survey of over 639 organizations in the retail auto industry
enables us to empirically test the proposed model of Internet channel performance. We
find that electronic commerce capabilities are made more effective when accompanied by
business process change. These complementarities, however, are accompanied by a
direct negative relationship between business process change and Internet channel
performance that makes the net effect positive only for organizations with high levels of
EC capabilities. By identifying the benefits and hazards of business process change, this
research provides practical advice to practitioners while integrating theory from the
resource based view (RBV) of the firm and business process reengineering (BPR) under a
common complementarities framework.
- 44 -
2.2 INTRODUCTION
Information technology and the Internet have changed the way that many retail
organizations interact with consumers, providing both an efficient transaction mechanism
for retailers (Smith et al. 2000) and an efficient price comparison mechanism for
consumers (Bakos 1997). While the Internet channel typically offers retailers lower
prices compared to traditional offline channels (Brynjolfsson and Smith 2000), efficiency
gains from electronic communications offer the promise of lower costs (Barua et al.
1995), thereby creating the opportunity for retailers to provide increased value to
consumers. In order to benefit from the Internet channel, however, retailers must develop
the capabilities necessary to interact via the Web (Amit and Zott 2001). As a result, the
value proposition for selling online may be unclear for some retailers. Retailers must
justify the expenses which result from channel cannibalization (Viswanathan 2005), in
which individuals who would have purchased through the higher-price offline channel
select the lower-price online channel, and the expenses necessary for building the
capabilities to utilize the Internet channel. Therefore, understanding the factors that
influence a retailer's ability to create value through the Internet channel is an area of
importance for both practitioners and researchers alike.
As the Internet channel represents an important way for organizations to interact
with consumers, it has not surprisingly generated a great deal of interest from researchers.
A growing stream of research has addressed consumers' use of the Internet, examining
consumer web satisfaction (e.g., McKinney et al. 2002), price paid (e.g., Brynjolfsson
and Smith 2000), infomediary use (e.g., Scott-Morton et al. 2001), value (e.g., Keeney
1999), and welfare impacts (e.g., Brynjolfsson et al. 2003). Similarly, a second stream of
- 45 -
research has addressed organizations' use of the Internet, examining infomediary use
(e.g., Chen et al. 2002), net-enablement (e.g., Wheeler 2002) and net-enabled business
transformation (e.g., Barua et al. 2004; Straub and Watson 2001). While empirical
examinations of outcomes from consumers' use of the Internet—i.e., price paid—have
been common (e.g., Brynjolfsson and Smith 2000; Scott-Morton et al. 2001), empirical
examinations of outcomes from organizations' use of the Internet—i.e., Internet channel
performance—are lacking. One possible explanation for this may be that overall
financial metrics do not typically capture outcomes by channel. As a result, few studies
have specifically examined the drivers of Internet channel performance outcomes.
This paper seeks to fill this gap in the literature by examining the performance
outcomes of business processes through which retailers interact with the Internet channel.
Drawing from the resource-based view (RBV) of the firm (for a review, see Wade and
Hulland 2004) and framing the research within the IT value stream (for a review, see
Melville et al. 2004), we examine relationships among electronic commerce (EC)
capabilities, business process change, and Internet channel performance. The RBV has
given researchers a theoretical foundation to understand how organizations interact with
the Internet channel (Amit and Zott 2001; Barua et al. 2004; Zhu and Kraemer 2002) and
researchers have argued that capabilities are important to the generation of value from a
production economics perspective (Bharadwaj 2000; Santhanam and Hartono 2003).
While EC capabilities have been found to be an important driver of firm performance,
little research has identified ways that existing EC capabilities can be made more
valuable through the existence of other firm resources (for an exception, see Zhu 2004).
- 46 -
We test our model in the context of the auto retailing industry. Auto retailing
represents the biggest retail sector in the US, with annual sales estimated at around a
trillion dollars. In addition to being an important part of the US economy by itself, recent
research and commentaries have pointed to the important contribution of industry-level
studies. Findings by Hawawini et al (2003) reveal that industry-level effects may
overshadow firm-level effects when examining the influence of radical IT innovation
such as the Internet on organizational outcomes, suggesting that within-industry studies
may be better able to examine the role of specific organizational characteristics in
influencing organizational performance. Industry-level studies are also more likely to
yield actionable outcomes managers will believe, increasing the relevance and impact of
IS research (Agarwal and Lucas 2005; Chiasson and Davidson 2006).
The retail auto industry is also a compelling example of an industry that has
undergone an IT-enabled transformation, as the emergence of online infomediaries and
the web have lowered search costs and fundamentally altered the way that firms compete
(Chen et al. 2002; Zettelmeyer 2000). Unlike most retail sectors, there is no national
brand and the ten largest dealerships enjoy less than 6% of overall sales, resulting in
significant variance in how organizations deal with the effects of industry transformation.
Thus, an industry-level study of detailed organizational characteristics may inform
practitioners of how to navigate the difficult road of technology transformation, making
this research relevant, useful, and impactful (Agarwal and Lucas 2005). Our analysis of a
survey of 639 auto retailers conducted by a leading market research organization includes
both detailed measures of retailer characteristics and objective measures of Internet
channel performance.
- 47 -
Through this research we make three main contributions. First, we provide
further support for the important role of electronic commerce capabilities in influencing
Internet channel performance, demonstrating the dimensions of EC capabilities which are
most likely to offer sustained benefits to retailers. Second, by identifying the
complementarities between electronic commerce capabilities and business process
change, we are able to obtain a better understanding of the how organizations can make
capabilities more effective in generating value for the organization. Third, by identifying
a negative relationship between business process change and Internet channel
performance, we demonstrate that change efforts can have a negative consequence when
not paired with the development of associated capabilities. These contributions add to
the growing literature utilizing complementarities as a theoretical lens through which to
understand the role of IT-related capabilities in predicting important firm outcomes.
The rest of the paper is organized as follows. In section one we review the
relevant research related to the RBV and IT value—two streams that have been critical to
the understanding of the relationship between technology and firm performance.
Following this, we discuss the model of the retailers' interaction with the online channel,
making arguments for the determinants of Internet channel performance. We then review
the sample and analysis technique. We finally examine the implications of this research
for retailers' use of the Internet.
2.3 LITERATURE REVIEW
In this section we review significant findings for the RBV and explore how it has
been used in the IT value literature.
The Resource Based View of the Firm
- 48 -
Originating in the field of strategic management (Barney 1986; 1991; Penrose
1959; Wernerfelt 1984), the resource-based view (RBV) of the firm has become a critical
lens through which researchers have examined the drivers of firm performance and
competitive advantage. The RBV suggests that firms compete on the basis of VRIN
resources—those that are valuable, rare, inimitable, and non-substitutable (Barney 1991;
Connor 1991). Possession of VRIN resources can lead to a temporary competitive
advantage and positively influence performance, as shown through empirical studies in a
variety of domains (Bharadwaj 2000; Dutta et al. 1999, 2005; Srivastava et al. 2001).
The RBV provides a useful framework for IS researchers to understand the
strategic value of IT and its influence on firm performance. Using the RBV, researchers
can characterize how specific technology-related capabilities influence firm outcomes,
adding greater detail to the value creation process. In addition, as the RBV has been
applied to numerous areas of organizational research, it provides a vehicle through which
to compare the magnitude of the effects from IT to the effects from other organizational
phenomena and a theoretical foundation through which to conduct cross-disciplinary
research (Wade and Hulland 2004).
Resources and Capability Identification
The identification of resources and capabilities is an important step towards the
use of the RBV to understand firm behavior and outcomes. Grant (1991) distinguished
between resources and capabilities, defining resources as tangible assets of the firm and
capabilities as a bundle of resources that has the potential for action. Wade and Hulland
(2004) adopt the definition of resources as assets and capabilities which are useful in
detecting or acting on threats or opportunities in the competitive environment (Sanchez et
- 49 -
al. 1996). They further define assets as those resources associated with the development,
manufacture, or sale of products and capabilities as repeatable organizational actions
(Wade and Hulland 2004).
The identification of what constitutes a specific IT-related resource has created
some challenges for IT researchers in particular. Several studies have argued that IT
resources can lead to a strategic advantage only when they are combined with other
organizational resources (Clemons and Row 1991; Ross et al. 1996). In examining
specific types of IT resources, Mata et al. (1995) argued that of the variety of resources
related to IT, only IT management skills can lead to a lasting strategic competitive
advantage. Multidimensional typologies of core IT capabilities have been developed by
several researchers. Feeny and Willcocks (1998) identified nine core IT-related
capabilities including business systems thinking, relationship building, architecture
planning, leadership, informed buying, contract facilitation, vendor development,
contract monitoring, and making technology work. Bharadwaj et al. (1998) suggested
and empirically validated six dimensions of IT capabilities including IT business
partnerships, external IT linkages, business IT strategic thinking, IT business process
integration, IT management, and IT infrastructure. In their review of the use of the RBV
in IS research, Wade and Hulland (2004) integrated this prior research and identified
three main categories (outside-in, spanning, and inside-out) of resources across a total of
eight dimensions including manage external resources, market responsiveness, IT-
business partnerships, IT planning and change management, IT infrastructure, IT
technical skills, IT development, and cost effective IT operations. Outside-in resources
influence the organization's outward-facing external relationship management and
- 50 -
market responsiveness. Spanning resources include IT-business partnerships and
planning/change management. Inside-out resources include IT technical skills, IT
development, and cost-effective IT operations. A summary of the dimensions found in
competing frameworks is found in Table 2.1.
Resource and Capability Measurement
For empirical studies investigating IT and utilizing the RBV, difficulties in
measurement have caused theoretical questions of what constitutes resources and
capabilities to be addressed directly alongside empirical questions of how to measure
them. Several researchers have utilized overall reputation as indicated by an Information
Week list of IT innovation leaders by industry as a measure of IT capabilities (Bharadwaj
2000; Santhanam and Hartono 2003). This type of measurement has the advantage of
being externally developed by a panel of industry experts. However, the use of a binary
variable to represent IT capabilities does not allow for the analysis of incremental
capabilities, is not consistent with the multidimensional capabilities specified by the RBV
(Santhanam and Hartono 2003), and thus may provide limited insight into the specific
mechanisms through which IT capabilities may influence organizational performance.
As an alternative, numerous studies have measured responses from key
informants within organizations through a survey methodology. For example, Armstrong
and Sambamurthy (1999) found that CIO capabilities and IT infrastructure each lead to
IT assimilation. Ray et al. (2005) found that managerial IT knowledge and service
climate positively influence customer outcomes. An advantage of this technique is that it
enables the measurement of constructs not accessible through objective measures. While
the key informant survey methodology has been extensively used throughout
- 51 -
organizational research, objections to this methodology are that measures typically reflect
the opinion of one individual and ignore the possibility that IT-related capabilities may
occur throughout various parts of the firm.
An alternative to survey measures is the use of metrics related to IT
functionalities, or specific system features/functions that organizations develop.
Although IT related capabilities are the constructs of theoretical interest, IT
functionalities are argued to be enabled by and thus reflective of underlying capabilities.
This approach has been used to measure capabilities related to electronic commerce (Zhu
2004; Zhu and Kraemer 2002) and net-enabled business processes (Barua et al. 2004).
The use of metrics or IT functionalities has the advantage that they are objective
measures which typically can be determined either through survey methodology or
through direct observation—i.e., from examining public press releases and websites.
Unlike typical survey measures, however, metrics have the disadvantage that they must
be updated as IT functionalities diffuse through an entire population of organizations and
new functionalities emerge.
Complementarities
In addition to the direct positive effect of IT resources and capabilities, the
complementarities among resources have also been of particular interest. Clemons and
Row (1991) argued that IT resources provide more value when paired with organizational
resources. In the context of electronic commerce, Zhu (2004) found that EC capabilities
were more valuable when combined with internal infrastructure resources. Similarly,
Barua et al. (2004) found that customer (supplier) focused capabilities were more
valuable when customers (suppliers) had a high degree of readiness for net-enabled
- 52 -
business processes. Overall, by examining ways in which the overall efficiencies of
business processes are improved through synergistic capabilities, the complementarities
lens provides an important framework to understand organizational performance.
Dynamic Capabilities
Due to the changing nature of e-commerce technologies, the organizations' ability
to develop systems related to net-enablement have been argued to be a dynamic
capability (Wheeler 2002; Zhu and Kraemer 2002). Dynamic capabilities refer to a
firm's ability to adapt and change in high velocity environments that require new
capabilities on an ongoing basis (Teece et al. 1997). A firm must continually adapt e-
commerce strategies, business practices, and technologies, and as a result the
measurement of e-commerce capabilities actually reflects the firm's ability to change
with the environment. Wheeler (2002) used the dynamic capabilities perspective to
develop a process model of the specific mechanisms through which firms develop IT
capabilities and learn from both actions and the environment. He suggests that
organizations develop IT capabilities by a process of choosing technologies, matching
technologies with opportunities, and acting on those opportunities. Zahra and George
(2002) also point out the importance of dynamic capabilities in examining how IT relates
to organizations.
The RBV and IT Value
The resource-based view has frequently been used as a foundation for
understanding the mechanisms through which IT creates value (Bharadwaj 2000;
Santhanam and Hartono 2003). A recent review article by Melville et al (2004)
integrates prior research from each of these streams into a framework indicating how IT
- 53 -
creates value in organizations. This framework conceptualizes the process through which
IT generates value as one in which IT resources and complementary organizational
resources interact to influence the performance of business processes. The impact on
business process performance, in turn, influences organizational performance. This
entire process is affected by contextual factors such as the country and industry in which
the business process takes place and the other trading partners that may be involved with
the business process. Overall, this framework provides an effective way to begin to
understand how organizations create value through interacting with the online channel.
In sum, the RBV has provided a rich foundation through which to understand the
strategic importance of IT and how IT can influence firm outcomes. It is useful both as a
way to theoretically understand those characteristics which enable firms to obtain
sustained competitive advantage and empirically examine those characteristics which
provide firms with value.
2.4 RESEARCH MODEL AND HYPOTHESES
In developing the model, shown in Figure 2.1, we adopt a complementarities-
based theoretical framework to understand how electronic commerce (EC) capabilities
and business process change relate to organizational performance. Overall, the model
suggests that both the EC capabilities and business process change are important for
retailers to derive value from the Internet channel.
Internet Channel Performance
Understanding the ways through which information technologies influence the
performance of organizations is one the key themes of IS research. While numerous
organizational studies have identified those factors influencing overall business
- 54 -
performance (e.g., Brynjolfsson and Hitt 1996; Zhu and Kraemer 2002; Zhu and Kraemer
2005), there are several advantages to studying the performance of the Internet channel—
i.e., Internet channel performance—separately from overall organizational performance.
In an industry such as auto retailing, many of the existing business processes have been
significantly altered by the presence of the Internet. Measuring Internet channel
performance directly gives us a way to understand and examine empirically those factors
which enable organizations to be successful, as they incorporate a new model of sales
provided by the Internet. Measuring overall organizational performance, while
important, may not reveal the detailed process level factors which enable organizations to
succeed in utilizing the Internet channel. This incorporation of business process specific
outcomes has been a key theme within the IT value literature (e.g., Mukhodaphyay and
Kekre 2002; Mukhopadhyay and Kekre 1995). In essence, examining process level
performance enables us to determine how a given organization has been able to utilize the
Internet channel as a way to grow overall sales.
EC Capabilities
We argue that EC capabilities will lead to increased Internet channel performance.
EC capabilities refer directly to the ability of the organization to implement and manage
systems supporting electronic commerce, a more specific version of overall IT
capabilities. As outlined earlier, a rich literature has linked IT-related capabilities with
firm performance (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer
2002). IT-related capabilities benefit firms by making IT investment dollars more
effective though lowered IT costs (Feeny and Willcocks 1998; Ross et al. 1996), better
management of IT opportunities (Bharadwaj 2000; Bharadwaj et al. 1998; Mata et al.
- 55 -
1995), or increased impact of IT systems (Devaraj and Kohli 2003). Similarly, EC
capabilities are expected to enable retailers to effectively implement EC systems, take
advantage of EC partnerships, and make sales through the Internet channel. Thus, those
retailers with high levels of EC capabilities would be better able to attract consumer leads
and convert those leads to sales than those with low levels of EC capabilities.
Prior research has identified the multidimensional nature of EC capabilities. As
dimensions of EC capabilities are those which enable organizations to gain competitive
advantage within a particular context, the dimensions need to be adapted to the specific
context being evaluated. When examining manufacturing firms, Zhu and Kraemer
(2002) identified four dimensions of EC capabilities: (1) information; (2) transaction; (3)
interaction and customization; and (4) supplier connection. In the context of auto
retailing, we conceptualize EC capabilities across three dimensions: (1) informational, (2)
transactional, and (3) relational capabilities. Informational capabilities provide useful
information about products and services. Transactional capabilities enable online
purchases. Within this population and time period, auto retailers had little ability to
provide customers with the same types of customized options as the manufacturing sector
examined by Zhu and Kramer, making the interaction and customization dimension less
applicable. In addition, the use of online infomediaries represents a critical way in which
retailers interrelate with their customers online that is not adequately captured by the
supplier connection dimension. In this context, the relational capabilities dimension
captures both the integration with suppliers (original equipment manufacturers) and
online infomediaries.
- 56 -
Informational, transactional, and relational capabilities are each expected to
influence performance through different mechanisms. Informational capabilities enable
organizations to provide customers with the information they need to make decisions,
thus increasing the likelihood they will purchase through the Internet channel.
Transactional capabilities enable customers to easily obtain price quotes or make offers
on vehicles, making the overall process of buying a vehicle or related product easier.
Relational capabilities provide the dealership with leads to customers from trusted third
party firms, such as infomediaries and manufacturers. This provides the dealer with
access to customers that he or she may not have had access to otherwise. In addition,
research has shown that the during the process of linking from a third party firm to a
organization, institutional trust from the known third party is transferred to the unknown
dealership, making the individual more likely to purchase (Stewart 2003). For these
reasons, and the general benefits of EC capabilities discussed earlier, we expect:
H1A: Informational capabilities are positively associated with Internet channel
performance.
H1B: Transactional capabilities are positively associated with Internet channel
performance.
H1C: Relational capabilities are positively associated with Internet channel
performance.
Zhu and Kraemer (2002) viewed all dimensions of EC capabilities equally,
arguing that all dimensions of EC capabilities were reflective of an underlying
construct—i.e, EC capabilities. However, there is reason to believe that different
dimensions of EC capabilities may have different effects on performance, as each
- 57 -
dimension of EC capabilities may differ in how easily specific advantages can be
imitated and competed away. Technologies emerge and are adopted by a population of
organizations, and as all organizations adopt a technology the competitive benefits
transferred to the organization are reduced or eliminated. As a result, those EC
capabilities which cannot be easily competed away would be expected to provide the
most value to organizations.
We argue that performance benefits from relational capabilities are less likely to
be competed away and are therefore more likely to provide lasting value to retailers than
informational or transactional capabilities. The development of informational and
transactional capabilities may require the retailer to engage outside expertise or develop
internal capabilities to actively manage the systems involved. These capabilities,
however, offer the retailer no degree of exclusivity with respect to competitors. Even
highly advanced web functionality can be relatively easily replicated or purchased from
technology vendors. Thus, over the long run advantages related to informational and
transactional capabilities are likely to be competed away. Relational capabilities, in
contrast, typically involve exclusive relationships with an infomediary or manufacturer
for a geographical area. As demonstrated analytically by Chen et al. (2002), both the
informediaries and the dealers themselves benefit from the exclusive nature of these
agreements. This is expected to result in relational capabilities having a greater impact
on performance than informational or transactional capabilities. Thus, we hypothesize:
H2: The relationship between relational capabilities and performance is stronger
than the relationship between informational or transactional capabilities and
performance.
- 58 -
Business Process Change
Prior research has highlighted the importance of the organizational changes which
are often associated with technology investments (e.g., Broadbent and Weill 1999;
Grover et al. 1998; Kettinger and Teng 1997). This research, however, has placed much
of the emphasis on the business processes of enacting change (e.g., Datta 1998; El Sawy
2001; Kettinger and Teng 1997; Nissen 1998) rather than the outcome of the change
itself. While procedures for business process change are quite important operationally,
they do not provide information for decision makers regarding the likely benefits of
business process change or how business process changes may influence the value
derived from technology investments.
In this research, we argue that business process change makes EC capabilities
more effective in influencing the performance of the Internet channel. This relationship
is similar to that between EC capabilities and IT infrastructure examined by Zhu (2004),
who found that EC capabilities were more effective in the presence of IT infrastructure.
EC capabilities enable firms to take advantage of the Internet as a medium for the rich
exchange of information with partners (Bakos 1998; Bakos and Brynjolfsson 1993). IT
infrastructure improves the effectiveness of EC capabilities by bridging the Internet-
enabled communications to the internal systems of the organization (Zhu 2004).
Following a similar line of reasoning, complementary business process change makes EC
capabilities more effective by aligning the external EC process with the internal firm
resources. This may include providing a bridge to the human resources within the
organization or ensuring that EC investments are adequately monitored and optimized.
The development of these EC capabilities by themselves does not ensure that the
- 59 -
organization will have the internal resources necessary to take advantage of them. When
organizations undergo business process change, they optimize business and workflow
processes (Barua et al. 1996; El Sawy 2001), thus ensuring that technologies are
adequately integrated into operations.
In the case of auto retailing, when dealerships become net-enabled, the Internet
may be viewed either as an external function that provides a new way of obtaining leads
or as a significantly new business process. Dealers treating the Internet as an external
function may handle the leads in the same way in which they handle normal leads—
allowing salesperson availability at the time the leads come in to determine who obtains
the leads—making little change to the overall business process. Alternately, the
dealership may redesign the business process to match the specific demands of the
Internet channel, dedicating individuals with specific goals related to interacting with and
tracking customers electronically. The choice made by the dealership of how to handle
the consumer interactions initiated via the Internet, therefore, may have an important
influence on Internet channel performance.
One reason for the importance of this type of business process change is the
increased use of email and handheld devices, as consumers now may have a lower
tolerance for delays in communication than they once did. Salespersons splitting time
between online enquires and traditional walk-in customers may be much slower in
responding to electronic communication. A dedicated group, however, would be able to
effectively manage electronic communications with a large group of potential customers.
Second, managing communications electronically may require extensive use of
communication technology and with it significantly different skills than face-to-face
- 60 -
interaction. Third, in the technology adoption literature, social aspects have been found
to be among the strongest reasons for technology adoption (for a review, see Venkatesh
et al. 2003). Introducing a new technology and positively influencing perceptions of
usefulness may be difficult in a group with established work processes. Instituting a
dedicated group with the objective of handling Internet leads would lead the organization
to develop its own social climate toward usage. Without a history of work processes, the
social factors that influence the use of the technology would not have to be overcome—
likely leading to more complete adoption of the technologies. The argument that the
organizations which change business processes will be more effective is also consistent
with the suggestion by El Sawy and Bowles (1997) that firms should reexamine customer
processes that interact with the Internet. As the overall impact of complementary
business process change on performance requires the existence of identified customers
that have the ability and willingness to purchase, this suggests that business process
change will moderate the relationship between each of the three dimensions of EC
capabilities and Internet channel performance. Therefore, we hypothesize:
H3A: Complementary business process change positively moderates the
relationship between the informational capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
H3B: Complementary business process change positively moderates the
relationship between transactional capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
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H3C: Complementary business process change positively moderates the
relationship between the relational capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
2.5 RESEARCH METHODOLOGY
Sampling and Data Collection
The data for this study is drawn from the ongoing work of a leading market
research organization. The organization contacted 17,160 dealerships and asked them to
participate in a 30-minute online survey detailing aspects of their use of the online
channel. As the overall population of dealerships in the US is nearly 23,000, this sample
represented nearly 75% of the retail auto industry and included nearly all medium to large
dealerships. From the initial email, 1,016 retailers completed the survey, representing a
6% response rate. The average number of vehicles sales per for the organizations
responding to the survey was 990. This was higher than the average dealership sales
reported during this timeframe, which was equal to 730 vehicles per year (NADA 2002).
As the survey was primarily targeted at organizations use of the Internet, it is possible
that larger organizations with established Internet operations may have been more likely
to complete the survey. In addition, as the survey was conducted online, smaller firms
may not have had the connectivity to be reached by the market research organization.
Among the respondents, 283 did not report profit levels per vehicle. Additional listwise
deletion of missing variables yielded 639 usable responses.. While the response rate is
low, the sample size is overall very large and represents nearly 3% of the total population
of dealerships. In addition, it is estimated that based on past experiences of the market
- 62 -
research organization, approximately 20% of the contact emails were not valid.
Adjusting the number of organizations contacted downward by 20% results in an overall
response rate for valid contacts of 5%.
Measures
Informational capabilities were measured through four binary indicators of
specific information-related functionality, such as "Lists all photos on websites."
Similarly, transactional capabilities were measured through four binary indicators related
to purchasing or furthering the purchase process online, such as "Price quote request
form." Finally, relational capabilities were measured using number of total
infomediaries and manufacturers in which the dealership has established relationships to
receive customer leads, the total number of leads obtained, and the total invested in
establishing relationships.
The measure for business process change indicates whether the retailer has
established separate organizational resources to handle Internet-generated leads within
the organization. Specifically, the retailer was asked "Are leads distributed to specific
sales people who don't take regular floor traffic?" As discussed earlier, the designation
of a separate business process is an important way for organizations to indicate that the
Internet is not an external feed for leads but rather requires a new way of interacting with
customers—i.e., it represents substantial business process change.
In order to determine the overall performance of the Internet channel, managers
were asked to indicate the total number of sales originating in the Internet channel and
the average profit per sale. This question was asked iteratively, where the respondent
entered all of the sources of her Internet sales and then had the opportunity to adjust her
- 63 -
response after seeing the totals. Internet channel performance was measured by total
Internet profit, which was calculated by multiplying the total Internet sales by the average
profit per Internet sale.
Several other variables were included to control for other factors which may
influence the overall Internet channel performance but where not hypothesized in the
model. We controlled for the number of luxury brands (luxury), the number of domestic
brands carried (domestic), and the number of imported brands (import) carried by the
retailer. The overall sales volume as measured by the number of sales per month (sales
volume) and the discount offered from the online channel (online discount) were also
utilized as controls.
Validity of EC Capabilities Measures
As this research involved the use of EC capabilities measures specific to retailing,
multiple tests of construct validity were completed according to established procedures.
Measures for EC capabilities were relatively highly correlated (0.20-0.40) but exhibited
adequate discriminant and convergent validity. We utilized exploratory factor analysis
using the principal components approach with Varimax rotation to access discriminant
validity. Entering each of the items for the EC capabilities yielded three factors.
Loadings for the informational, transactional, and relational capabilities were each as
expected, with all loadings >0.6 for all measures and <0.3 for cross loadings. The factor
loading matrix is found in Table 2.3. In order to further confirm the factor structure,
CFA with EQS 6.1 was used to examine the informational, transactional, and relational
capabilities. Factor loadings for each measure were greater than 0.4, thereby exceeding
the cutoff criteria of 0.4 set by Gefen et al. (2000). The overall fit for the measurement
- 64 -
model was as follows X
2
(DF=32, N=1016) = 42.490 (N.S.); RMSEA =0.021
(0.000,0.036); CFI = 0.996; SRMR= .029 NFI= 0.985; NNFI = .994; IFI = .996. The
level of fit met the criteria provided by past research. The RMSEA was less than 0.06,
the CFI greater than 0.96, and the SRMR less than 0.10, meeting all three of the criteria
suggested by Hu and Bentler (1999). Overall, this suggests that the three dimensions of
EC capabilities exhibit adequate discriminant and convergent validity, as indicated by
both the principal component analysis and the CFA.
Analysis and Results
Our data analysis was completed using ordinary least squares (OLS) regression
to examine the impact of EC capabilities and complementary business process change on
Internet channel performance. We centered the variable in order to reduce the possibility
of multicollinearity due to inclusion of interaction terms for testing the moderation
hypothesis.
Overall, the analyses indicated support for a model involving complementarities
between EC capabilities and business process change in influencing Internet channel
performance. Direct effects for transactional and relational EC capabilities were found to
be associated with higher levels of Internet channel performance, but the effect for
informational EC capabilities was not significant. In addition, the direct effect of
business process change on the resulting performance level was negative, indicating that
there may be some additional coordination costs which may reduce the benefits from
business process change. The interaction terms between information/relational EC
capabilities and business process change were positive in influencing Internet channel
performance. A graph of the interaction of relational capabilities and business process
- 65 -
change, shown in Figure 2.2, indicates that the overall effect of business process change
is positive only when relational capabilities are high. The interaction term between
transactional EC capabilities and business process change was not significant.
Effect sizes for the relationships observed were well within the range where they
represent significant financial incentives for the organizations within this industry. For
the direct relationship between EC capabilities and profits, a one standard deviation
increase in transactional capabilities was associated with an increase in monthly profits of
nearly $1.5K while a one standard deviation increase in relational capabilities was
associated with an increase in monthly profits of nearly $4.5K. The effects of
complementarities were also in a range expected to be significant to the organizations
studied. A one standard deviation change in both informational capabilities and business
process change were associated with an increase in profit of $3K per month. A one
standard deviation change in both relational capabilities and business process change
were associated with an increase in profit of $5K per month. Statistics from NADA
indicate that the average dealer had over $1.6 million in monthly new car sales but only
$6.5K in net profits (NADA 2005). While the data reported in our analysis of the role of
capabilities are gross profits—i.e., excluding the cost of goods sold but not including
operational costs—it is still likely that they represent a sizable percentage of the
dealerships' bottom-line. Overall, the effect sizes related to the models examined are of
sufficient size that they are likely to have an important influence on the performance of
the organization.
- 66 -
2.6 DISCUSSION
The RBV has recently received a great deal of focus within the IS community, as
both IT-related capabilities and organizational characteristics complementary to IT-
related capabilities have been identified. This research extends this work by
demonstrating the differing effects of different EC capabilities, integrating the role of
business process change, and identifying the complementarities between business process
change and EC capabilities in influencing Internet channel performance. By directly
assessing Internet channel performance within a single industry, we are also able to show
those factors which help organizations to thrive within the context of an IT-enabled
transformation.
Implications for Theory
A key contribution of this research is that it integrates the capabilities framework
of the RBV literature with the concept of business process change from the business
process reengineering (BPR) literature. In doing so, it extends the literature in both areas
and provides additional depth to the knowledge of complementarities in predicting
performance. This also suggests that additional linkages between existing literatures may
be possible through a complementarities lens—allowing a more nuanced understanding
of the process of IT value creation.
An additional contribution is related to the identification of the negative direct
effect of business process change on performance. This finding suggests that overall
efforts to significantly changing business processes can be costly to the organization if
not paired with the development of EC capabilities. This finding suggests the need to
understand the role of mindful practices (Weick et al. 1999) as they relate to business
- 67 -
process change. In studying IT, mindfulness provides a way of capturing how
organizations address adoption decisions related to new technologies (Butler and Gray
2006; Fichman 2004; Swanson and Ramiller 2004). Here, a lack of mindfulness—i.e.,
mindlessness—may help to explain the negative outcome from business process change
for some organizations. Additional work should further explore the theoretical
relationships between IT, business process change, mindfulness, and IT value.
The lack of significance of specific relationships in the model should also be
acknowledged. The direct relationship between informational capabilities and Internet
channel performance was found to not be significant. A key aspect of the RBV that is
rarely acknowledged empirically is that some capabilities can be expected to loose
competitive value over time as they are competed away. This lack of significance
suggests that informational capabilities may have reached this status. As numerous
technologies make it easier for organizations to place information on the web, it is likely
that just the presence of information may not provide a competitive advantage, while
information along with dedicated individuals with the incentives to ensure the
information is correct and up-to-date—i.e., with the presence of business process
change—still offers a performance benefit.
We also found that the interaction between transactional capabilities and business
process change was not significant. This suggests that transactional capabilities may not
benefit from the same type of synergies with the rest of the organization that were found
for informational and relational capabilities. Transactional capabilities typically provide
a specific business function, acting as a substitute for interactions with other
organizational processes rather than as a compliment to other organizational processes, as
- 68 -
with informational and relational capabilities. More work is needed to further determine
whether the results here may be generalizable or a result of the limited interactions
between transactions and other organizational processes specific to the retail auto
industry.
Implications for Practice
For organizations, this research provides insights into the performance benefits
resulting from the development of EC capabilities. Transactional and relational
capabilities had a direct performance benefit, while informational capabilities did not.
Informational EC capabilities represent the most basic and imitable of the EC
capabilities. This suggests that organizations should maximize investments in developing
capabilities that will provide lasting organizational value.
This research also indicates both the potential benefits and the potential pitfalls of
complementary business process change. Those organizations which are able to develop
capabilities to execute electronic commerce are able to make those capabilities more
valuable through business process change. This change does not come without a cost,
however, and organizations must therefore assess both their level of commitment to
electronic commerce and their ability to develop electronic commerce capabilities. These
findings support much of the general disappointment associated with the returns from
many business process reengineering efforts in the late 1990s.
It is also important to note that there is a potential negative impact of Internet
sales on overall profits. While the Internet and the use of infomediaries enables
organizations to gain market share by locating new customers, it also can cannibalize
existing profits, as customers who would have purchased from the retailer at the higher
- 69 -
offline price obtain the online discount. Thus, organizations must pursue a strategy
which attempts to maximize overall profits, setting online prices such that the negative
effects of channel cannibalization do not outweigh the positive benefits of increased
market share.
Limitations
A limitation of this research is that it takes place in a single industry and therefore
may not be generalizable to other contexts. While this is a limitation of this work, the use
of metrics and the effects from detailed organizational characteristics related to
technology may only be detectible in single-industry studies, as industry-level effects
may overshadow organizational-level effects for many types of technology impacts
(Hawawini et al. 2003). As a result, future research should confirm the role of
complementarities between EC capabilities and business process change within different
industries.
A second potential limitation of this work is that the measures were collected
using a single survey and thus were susceptible to common method bias. This is
somewhat mitigated by the objective nature of the performance measures, and a
Harmon's single factor test indicated that less that 25% of the variance was explained by
any single factor. However, future work should consider the impact of complementarities
between EC capabilities, business process change, and other measures of organizational
performance which can be obtained from third party sources.
A third limitation of this work is related to the data being collected from a single
source. Collecting data from a single individual within the organization increases the
- 70 -
likelihood of measurement error. While this is a frequent limitation of organizational
studies, future work should attempt to obtain average values from multiple respondents.
A fourth limitation of this work is that single item binary measures were used to
capture business process change. In reality, organizations may undergo several different
types of business process change that may vary both in scope and degree. Different
scopes and degrees of business process change may result in different resulting levels of
value. Future work should access business process change using measures capturing
specific changes made, as it may be that there are only certain aspects of changes that
influence resulting performance outcomes.
2.7 SUMMARY
This research has provided a more complete understanding of the drivers of firm
performance in the Internet channel. Specifically, we identify complementarities
between EC capabilities and complementary business process change. In doing so, we
provide guidance to firms making decisions related to investing in the Internet channel
and optimizing existing Internet channel investments.
- 71 -
2.8 FIGURES
FIGURE 2.1 - RESEARCH MODEL
- 72 -
FIGURE 2.2 - GRAPH OF PROFIT VS. NUMBER OF LEADS
2.5
2
1.5
1
0.5
0
No BPC
BPC
0 100 200 300 400
-0.5
-1
Leads
- 73 -
l
n
(
P
r
o
f
i
t
)
2.9 TABLES
TABLE 2.1 - SUMMARY OF TYPOLOGIES OF IT CAPABILITIES
Framework Dimensions
Wade and Hulland (2004)
Bharadwaj et al. (1998)
Feeny and Willcocks
(1998)
1.
2.
3.
4.
5.
6.
7.
8.
1.
2.
3.
4.
5.
6.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Manage external resources
Market responsiveness
IT-business partnerships
IT planning and change management
IT infrastructure
IT technical skills
IT development
Cost effective IT operations
IT business partnerships
External IT linkages
Business IT strategic thinking
IT business process integration
IT management IT
infrastructure
Business systems thinking
Relationship building
Architecture planning
Leadership
Informed buying
Contract facilitation
Vendor development
Contract monitoring
Making technology work
- 74 -
TABLE 2.2 - DESCRIPTIVE STATISTICS
Online Discount
(Offline profit - Online Profit)/1000
Size (Total # sales/100)
Domestic (# of domestic brands)
Import (# of imported brands)
Luxury (# luxury brands)
Informational (Metrics, see measures)
Transactional (Metrics, see measures)
Relational (Metrics, see measures)
Business Process Change (BPC)
ln(profit)
Profit
Mean
0.394
0.865
0.535
0.142
0.076
2.510
2.598
0.914
0.684
8.700
15,930.160
Std
0.465
0.802
0.499
0.349
0.265
1.630
1.370
1.346
0.465
1.619
30,061.780
- 75 -
TABLE 2.3 - PRINCIPAL COMPONENT ANALYSIS
Informational1
Informational2
Informational3
Informational4
Transactional1
Transactional2
Transactional3
Transactional4
Relational1
Relational2
Relational3
N = 1017
1
0.887
0.871
0.831
0.733
2
0.742
0.727
0.711
0.636
3
0.830
0.765
0.693
*Cross loadings less than 0.3 are not shown.
- 76 -
TABLE 2.4 - CORRELATION TABLE
Variable 1 2 3 4 5 6 7 8 9
1. Informational 1.000
2. Transactional 0.446 1.000
3. Relational 0.290 0.196 1.000
4. Business Process Change -0.216 -0.115 -0.337 1.000
5. Performance 0.210 0.090 0.694 -0.221 1.000
6. Online Discount 0.001 -0.066 -0.033 0.059 -0.049 1.000
7. Size 0.297 0.143 0.585 -0.319 0.523 0.000 1.000
8. Domestic 0.115 0.075 0.141 -0.076 0.019 0.042 0.107 1.000
9. Import -0.143 -0.042 -0.277 0.157 -0.256 -0.088 -0.251 -0.436 1.000
10. Luxury 0.013 -0.110 0.054 -0.037 0.141 0.120 0.016 -0.116 -0.307
- 77 -
TABLE 2.5 - REGRESSION ANALYSIS RESULTS PREDICTING LN(PROFIT)
Intercept
Controls
Online Discount
(Offline profit - Online Profit)/1000
Size (Total # sales/100)
Domestic (# of domestic brands)
Import (# of imported brands)
Luxury (# luxury brands)
Direct Effects
Informational (Metrics, see measures)
Transactional (Metrics, see measures)
Relational (Leads/1000)
Business Process Change (BPC)
Interactions
Informational*BPC
Transactional*BPC
Relational*BPC
Adjusted R
2
F-value
N
Note: * p<0.05 **P<0.001 ***p<0.0001
Direct
7.935***
-0.472***
0.328***
-0.498***
-0.152
0.504**
0.027
0.175***
0.476***
-0.330**
0.469
61.79
639
- 78 -
Interaction
8.429***
-0.475***
0.339***
-0.446***
-0.118
0.520**
-0.081
0.162*
0.348***
-1.052***
0.152*
-0.013
0.335***
0.491
50.25
639
CHAPTER 3: UNDERSTANDING RETAILER USE OF ONLINE AUCTION
CHANNELS: STRATEGIES IN REPEATED SEARCH PROCESSES
3.1 ABSTRACT
We examine sellers' use of the online auctions for vehicles at eBay Motors
through the theoretical lens of search theory, viewing sellers' interaction with online
auctions as a process of searching for high-valuation customers. The vehicle market on
eBay motors has three distinguishing characteristics that make it ideal as a context to
examine seller behavior. First, more than half of the auctions on the eBay motors website
fail to lead to a completed transaction. Second, automobiles are somewhat unique in that
their vehicle identification number (VIN) enables a tracking of outcomes for a single item
across multiple auctions and even across multiple channels. Third, because over 70% of
sellers that sell through the eBay Motors channel are auto dealerships, auction outcomes
can be observed alongside sellers' activities in other channels. We find that the
characteristics for the market for a particular product type—particularly the price
variance and mean of the prior offer distribution—along with the extent that a seller
searches in other channels influence the resulting price premium the seller obtains. In
addition, we find that the total duration of search (number of days in which an item is
offered through the online channel) is negatively related to the resulting price premium
while the number of auctions in which an item appears is positively related to the
resulting price premium. This suggests that sellers discount their reserve price with time
but benefit as the number of auctions in which an item appears increases. In sum, this
research provides an understanding of how sellers use online auctions as part of an
overall business process of selling an item.
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3.2 INTRODUCTION
The Internet, along with the rapid adoption of electronic markets, has changed the
way in which buyers and sellers locate each other and transact in many industries.
Buyers and sellers now have access to many new channels—including web-based stores,
infomediaries, and electronic auctions—and may utilize several of these channels as part
of the sales process. Online auctions are one of the most widely adopted of these new
channels, with eBay in 2005 alone attracting 68 million users, 1.8 billion items, and $43.2
billion in gross merchandise volume (eBay 2005). As sellers may utilize auctions in
different ways as part of their overall sales strategy, understanding how sellers use and
obtain benefits from these online auctions is an important area for researchers to pursue.
There have been a number of studies of online auctions, examining topics such as
the validity of analytical model predictions (Lucking-Reiley 1999), the role of new
auction mechanisms (Budish and Takeyama 2001; Stephen 2001), and bidder behavior
(Bapna et al. 2004; Roth and Ockenfels 2000). While existing studies have greatly
increased our understanding of outcomes from individual online auctions, a notable gap
in the literature acknowledged by Wood (2004) is that there are "few articles on seller
strategies (other than reputation) that can be employed to gain a larger price paid." For
example, very little is known about how external factors related to the market, the
product, or the seller may influence the seller's reserve price—the key lever that the
seller uses to control the minimum price obtained from a sale. By better understanding
the underlying motivations and resulting impact related to seller strategies and behaviors,
we can better understand how sellers can optimize profits and market makers can best
serve both buyers and sellers.
- 80 -
This research examines seller behavior in the use of the electronic markets for
vehicles at eBay Motors, conceptualizing a seller's use of the auction channel as a
process of searching for high-valuation customers (Genesove 1995). A basic tenet of
search theory is that individuals optimize their search behavior based on the tradeoffs
between the expected benefits and the expected costs from search (Diamond 1985).
Consumer search behavior has frequently used duration of search as a key way to
understand shopping behavior in the retail channel (e.g., Banks and Moorthy 1999;
Dellaert and Haubl 2004; Johnson et al. 2004). Here, we conceptualize seller search
behavior through the duration of search and the number of auctions. These highly
related aspects of search behavior more fully capture the seller search process than
duration of search alone. However, duration of search is modeled as the key optimizing
variable by which sellers make decisions, as it is related to the seller's inventory holding
costs. In the context of high-valuation goods such as vehicles, inventory holding costs
can be quite large when compared to overall profit, and metrics such as the number of
days vehicles have been on the lot are frequently used to drive operational decisions.
In understanding sellers' motivations, we incorporate an understanding of how
external factors outside of the individual auction—namely, the sellers' search in other
channels (SOC) and the product offer distribution—influence the duration of search and
the resulting premium of the seller. SOC represents sellers' use of other channels for the
same product—captured through a search for the VIN number using a popular search
engine. By identifying sellers' activities in listing a particular item through other higher
valuation channels, we develop an understanding of how these activities influence the
sellers' activities in the auction channel. Similarly, characteristics of the product offer
- 81 -
distribution—that is the distribution of high bids for both those auctions which ended in a
sale and those that did not—are expected to influence the willingness of the seller to
search in the online channel. In sum, by identifying these key external factors related to
the potential benefits that a seller obtains from search, we can understand both the
duration of search and the resulting price premium obtained by the seller, thereby
obtaining a more complete picture of the causes and consequences of seller search
behavior. A framework describing the proposed relationships is found in Figure 3.1.
The vehicle market on eBay motors has three distinguishing characteristics that
make it ideal as a context to examine seller behavior. First, more than half of the
auctions on the eBay motors website fail to lead to a completed transaction. This
suggests that sellers may employ complex strategies as they set and reset their reserve
price in subsequent auctions for the same product. Second, automobiles are somewhat
unique in that their vehicle identification number (VIN) enables a tracking of outcomes
for a single item across multiple auctions and even across multiple channels. With the
capability to observe how sellers use online auctions over time, we are able to obtain
insights into the strategies employed by these sellers as part of the sales process. Third,
because over 70% of sellers in the eBay Motors channel are auto dealerships which also
have access to an offline channel, it gives us a way to understand auction outcomes in a
context in which sellers have access to more than one channel. In sum, the eBay Motors
channel provides a rich context in which to develop and empirically test a model of seller
search behaviors and outcomes in the business process of selling a high-valuation
product. We tested our model, shown in Figure 3.2, using a selection of 31,445 auctions
for new and used vehicles.
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There are two primary contributions of this paper. First, as the role of the lower
search costs of the Internet has primarily been viewed from the customers' perspective
(e.g., Bakos 1997), viewing sellers' use of online auctions as a process of searching for a
high-valuation customer provides a greater understanding of the role of low search costs
in influencing stakeholder behaviors in electronic markets. Second, as the value
implications of technology use is an important theme in the IS literature (e.g., Devaraj
and Kohli 2003; Zhu and Kraemer 2004), this research identifies how different aspects of
use—namely, the seller strategies for re-listing items and searching other online
channels—drive the value the organization obtains from the use of online auctions.
The rest of the paper is organized as follows. First, we provide a description of
the online auction mechanism and discuss relevant past research. We then provide a brief
overview of the search literature as it relates to electronic markets and explains the
behavior of sellers. This is followed by specific hypotheses related to the sellers' search
in online auctions, the data and analysis techniques used, the results, and a discussion of
the implications of this work.
3.3 LITERATURE REVIEW
In this section, we first discuss the context of this study—the online auto market
at eBay motors—and provide background on other work which has examined online
auctions. Next, we provide a basic overview of the concepts governing search theory,
with specific reference to how search theory has been applied to electronic markets and
seller behavior. This foundation then enables us to build a model predicting the causes
and consequences of seller search behavior in the next section.
The Online Auction
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Beginning primarily as a market for collectibles and used consumer goods, the
eBay site has continued to expand in the breadth of products offered and the number of
individual items available. In 2000, eBay recognized the large number of autos being
sold on its site and set up the eBay motors site—the sub-unit of eBay specializing in
automobiles and accessories. The number of vehicles sold through eBay has continued to
grow rapidly, and eBay is used by retailers to sell both new and used vehicles, though
used vehicles make up the vast majority of sales.
The auctions on eBay Motors follow the same rules as other eBay auctions, which
have been described as second-price ascending proxy-bid auctions. The seller selects the
reserve price, the starting bid, and the period of time over which the auction will occur
(i.e., 3-10 days). Buyers search for items online and may choose to enter a "proxy bid,"
or the maximum amount that they are willing to pay for a vehicle. After a bid is placed
the auction mechanism lists the highest bidder at an increment above the second highest
bid. For example, a bid increment of $1.00 would result in a (proxy) bid of $15.00 for
bidder A and a bid of $8.00 for bidder B being displayed as a high bid of $9.00. A more
complete description of the auction mechanism used by eBay can be found in prior
research (Aldridge 2005; Bajari and Hortacsu 2002).
For some, the existence of an online market for primarily used vehicles may be
surprising in itself. Vehicles are typically considered to be high-involvement purchases,
in which buyers often search extensively before making a purchase decision (Ratchford et
al. 2003). As a result, purchasing a used vehicle without physically inspecting it initially
appears to be something that would not appeal to many consumers. Akerlof's (1970)
seminal paper examining aspects of used vehicles further casts doubt on the feasibility of
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a market with lower levels of product quality information, as is the case with online
auctions. He suggested that as information asymmetry increases, markets are more likely
to fail as bad vehicles or "lemons" drive out the good vehicles through a process of
adverse selection. Given that when inspecting automobiles through electronic markets
individuals have less information than when inspecting automobiles in person, it would
follow that that those sellers with lemons would be more likely to pick the online auction
channel.
There are several associated findings, however, which may help to explain the
thriving online auction market for used vehicles on eBay Motors. First, the presence of a
feedback mechanism is one important design feature of online auctions that discourages
opportunistic behavior (Dellarocas 2003). Feedback mechanisms work by enabling
buyers to provide feedback on sellers and sellers to provide feedback on buyers. Since
sellers obtain a price premium from high quality reputations (Ba and Pavlou 2002;
Dellarocas 2005; Jeffrey 2005; Mikhail and James 2002), the mechanism discourages
sellers from inaccurately representing the quality of goods sold. Second, tests of
Ackerlof's hypothesis have indicated little empirical support in real world situations. An
examination of repair records for the truck market suggest that buyers of used trucks less
than 10 years old were no more likely than buyers of new trucks to experience repairs
(Bond 1982, 1984). Engers et al. (2005) similarly found no greater incidence of
problems for used vehicles when compared with new vehicles. A final factor driving the
success of the online auction market for used vehicles is that online channels typically
offer significantly lower prices and increased convenience for consumers. Scott-Morton
et al. (2001) found that new vehicles sold through online infomediaries were discounted
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an average of $450. As a result, consumers may take advantage of the ability to purchase
autos at a significant discount from offline channels if those benefits outweigh the risks
posed by increased information asymmetry. Overall, with the use of reputation
mechanisms to control information asymmetry and the advantages of cost savings,
consumers have moved in significant numbers to obtain the benefits offered by online
auctions for vehicles.
Search Theory
It is well known that extensive streams of literature exist in economics and
marketing investigating both search and auction theory. Diamond (1985) and Stiglitz
(1989) provide reviews of the search literature as it relates to consumer search for
commodity goods, and Klemperer (1999) and Milgrom (1987) provide excellent
overviews of auction theory. This research draws directly from the extensive literature
on search theory, but a full review of the search literature is outside of the scope of this
article. Instead, it is important to provide an overview of search theory as it has been
applied to the understanding of electronic markets, and seller behavior..
Search Theory, Electronic Markets, and Seller Behavior
Search theory provides a way through which to understand the behaviors of
individuals and organizations as they optimize the costs and benefits of the search
process. It has been particularly useful as a way of understanding electronic markets, as
the Internet has lowered the cost of searching for and providing information. Reviews
outlining the impact of the Internet have specifically focused on the importance of lower
consumer search costs (e.g., Bailey et al. 1999; Bakos 1998; Bakos 2001; Smith et al.
2000). Stahl (1989) formalized the impact of decreased search costs, which result in
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lowered prices and improved consumer welfare. Bakos (1997) subsequently examined
the incentives sellers, buyers, and infomediaries have to invest in online markets, as well
as the differing role of lowered search costs for product and price information.
While the majority of research has focused on the benefits accruing to consumers
from the lower search costs associated with the use of electronic markets, recently several
papers have identified the benefits accruing to sellers. Campbell et al. (2005)
demonstrated that lower consumer search costs may lead to collusive behavior among
retailers. When search costs are low, it is much easier for retailers to monitor the prices
of competitors—reducing the benefits from "cheating" by lowering prices. As a result of
the collusion, prices increase and overall consumer welfare decreases. Lynch and Ariely
(2000) experimentally examined the impact of lowered search costs for product and price
information on both differentiated and undifferentiated products. They found that when
search costs were low for quality information, individuals were less price-sensitive. They
also found that the ability to compare prices across stores had no impact on price
sensitivity for unique products. In a related study, Kuksov (2004) found that when
product design is treated as something that can be acted upon by firms, product
differentiation can actually counteract the increased competition resulting from lower
search costs. Firms choose to differentiate their products more when search costs are
low. With increased differentiation, product fit with consumer preferences provides a
differentiating factor which limits price competition. Together, these three articles
suggest that sellers will employ strategies which offset the decline in profit resulting from
the lowered search costs of the Internet.
Search Theory and Seller Search
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Seller search is defined as the activities engaged in by a seller in her search for a
customer to purchase a specific item. For many contexts, this process is not individually
observable, as sellers set prices and advertise as a way of searching for customers.
Auctions, however, provide a situation in which seller search is individually observable.
While the study of auctions using a framework of search theory has been quite limited, an
important related study by Genesove (1995) examined seller search in the context of
wholesale auto auctions, a context that corresponds very closely to that of the eBay
motors market. Because of this relationship, it is relevant to provide a more detailed
overview of the similarities and differences between Genesove (1995) and this work.
Genesove (1995) utilized the offer distribution characteristics—particularly the
mean and the variance of the offer distribution—to predict the likelihood of sale for a
particular auction. More specifically, in the basic search model proposed by Genesove
(1995), a seller has one vehicle, incurs a search cost for transporting the vehicle to the
auction house, and knows the offer distribution for her vehicle type. She sets her reserve
price, above which she will accept any offer and below which she will continue to search,
discounting future revenue. The procedures used in this work for the calculation of the
mean and the variance from the offer distribution are adapted directly from Genesove
(1995). Key findings from Genesove (1995) are that both the mean and the variance of
the distribution of prior offers influence the probability of sale. This work extends the
work by Genesove (1995) by examining the longitudinal processes of seller use of online
auctions for the same product as well as viewing the simultaneous use of multiple online
channels. Genesove (1995) viewed single auction outcomes without taking into account
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whether the vehicle had been auctioned previously or whether the seller has offered the
vehicle in other channels.
The wholesale auction case examined by Genesove (1995) differs in two notable
ways from the eBay motors auction. First, in the wholesale auction, the seller receives a
bid and then makes a decision whether to continue searching or accept the offer. In the
eBay motors case, the seller selects a reserve price before the auction and then receives
the high bid at the end of the auction that may or may not meet the reserve price. While
the time at which the seller sets her reserve price is different, when sellers ex-ante have
knowledge of the distribution of prior sales they adopt the same reserve price strategy
(Kohn and Shavell 1974), thus allowing us to adopt the same search-based framework.
A second difference is that the wholesale auction analysis was completely offline
and did not allow for the simultaneous offering of an item in more than one channel. The
separation of the informational and logistical components of a transaction is one of the
key differences between offline and online auctions (Kambil and van Heck 1998), and
thus exploring seller search behavior in the presence of the lower search costs of online
auctions is an important contribution. In the next section, we incorporate the drivers and
outcomes of search into a full model of seller search behavior and price outcome.
3.4 RESEARCH MODEL AND HYPOTHESES
In building our model of the seller search, we first discuss a key dependant
variable of interest, the price premium of the seller. Next, we address the search
behavior—with particular emphasis on the duration of search and the number of auctions.
Finally, we examine the role of two external factors related to the potential benefits the
seller may possibly expect from the sale of an item (1) the sellers' search in other
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channels and (2) the distribution of prior offers (final high bid) for products of the same
type.
Seller's Price Premium
In marketplaces such as eBay, there is no standard market clearing price for an
item of a particular type. Instead, the auction process enables a matching of buyers and
sellers through active bidding. To understand the benefits of different seller behaviors, it
is necessary to establish a reference point and then determine the performance of sellers
relative to that reference point—i.e., to determine the price premium obtained by the
seller. One possible reference point is the predicted price for a vehicle based on its
characteristics and other comparable sales. However, this would result in the price
premium for some sales being negative. In defining the price premium of the seller, we
first assume that each individual item (i) is of a particular type (j). We further assume
that for an item of type j, there exists a price P
MIN(j)
, below which no seller will sell the
item. The price premium that the seller obtains for an item i of type j is then the
difference between the price paid by the highest bidder (P
ij
) and P
MIN(j)
. This calculation
of premium is very similar to the procedure followed by Ratchford and Srinivasan (1993)
for the determination of the returns from consumer search.
Premium = P
ij
- P
MIN(j)
Duration of Search and Number of Auctions
As stated earlier, we follow prior research in marketing and labor economics to
use duration of search as a key focal variable to understand search behavior. However,
the duration of search and the number of auctions are two distinct yet highly related
components of the sellers' search behavior which may each independently influence the
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outcomes from search. For example, a seller may list an item in 3 seven-day auctions or
7 three-day auctions and have the same total search duration. While studies have
consistently found that longer auctions yield higher prices (Bajari and Hortacsu 2002;
Lucking-Reiley et al. 2000), these studies are cross-sectional in that they compare only
auctions in which the reserve price is met while ignoring auctions in which the reserve
price is not met. As a result, it is unclear from prior empirical findings whether sellers
are able, on average, to obtain a higher premium through a single long auction or multiple
shorter auctions. Thus, a further objective of this research is to provide practical advice
to sellers on the tradeoffs between these two options.
The relationship among the sellers' search behavior—i.e., the duration of search
and the number of auctions—and the price premium depends upon both the stochastic
aspects of the search process and the reserve price strategy employed by the seller. The
longer the duration that a seller searches for a buyer, the stochastic properties of search
suggest that it is more likely that a seller will locate a higher valuation buyer. Assuming
a constant arrival of potential bidders, the longer auction clearly has an advantage in
exposing the seller's product to more potential buyers and thus increasing the likelihood
of finding a high-valuation buyer. The stochastic properties of search also provide a key
explanation of why longer auctions have been found to yield higher prices (Bajari and
Hortacsu 2002; Lucking-Reiley et al. 2000). Thus, the stochastic aspects of the search
process suggest that duration of search will be positively related to the price obtained.
Whether the overall effect of the duration of search on the price obtained is
positive or negative, however, depends on the reserve price strategy adopted by the seller.
Two simple reserve price strategies involve (1) a constant reserve price or (2) a reserve
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price discounting strategy. Prior work by Ashfelter et al. (2002) in auctions for
impressionist and contemporary art—a market which has similarities to the online vehicle
market in that the sales rate is low and the overall price is high—developed an analytical
model which suggests that the optimal strategy is for sellers to set a reserve that is a
constant proportion of the expected high bid of the auction. This is also consistent with a
well known finding from labor economics that a worker's optimal strategy is to set a
reserve wage and accept only jobs above that wage (e.g., Lippman and McCall 1976). As
each time a seller lists an item on the online auction they receive a sample from the
overall distribution of potential buyers, on average sellers selecting a higher reserve price
would be less likely to find a buyer within a single auction (Genesove 1995), thereby
increasing the overall duration of search and the number of auctions. When a buyer is
found, the higher reserve can act to obtain a slightly higher price from the bidder with the
highest valuation (Samuelson 1981). Thus, we can expect that if sellers adopt a constant
reserve price strategy both duration of search and the number of auctions will be
positively related to the price premium.
The reserve discounting strategy is one in which sellers reduce their reserve price
with time. Sellers may be likely to adopt this strategy in order to incorporate additional
information learned as part of the sales process. In addition, as the overall value of items
is high relative to the cost of searching, sellers may be willing to start their reserve price
very high as they hope to find a "sucker." Lucking-Reiley (2000) found evidence of this
"sucker search" effect in the relationship between the fees charged by different auction
sites and the percentages of auctions that ended in a sale. He found that in 1999 when
auctions taking place on Yahoo.com did not charge a seller fee only 16% of transactions
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ended in a sale. For auctions taking place on eBay, which did charge a fee, 54% of all
auctions ended in a sale. In addition to online auctions providing the opportunity to
search for a "sucker," listing an item in subsequent auctions also provides sellers with an
opportunity to reassess their valuations downward as they obtain more information about
the current market conditions. Laboratory studies confirm this general behavior, finding
that sellers generally adjust reserve prices downward in subsequent auctions (Burdett and
Vishwanath 1988; Rosenfield and Shapiro 1981). In sum, we can expect that if sellers
engage in a reserve price discounting strategy duration of search will be negatively
related to the price obtained.
We suggest that the low cost of search provided by the online environment will
result in sellers adopting a strategy of discounting their reserve price with time. While
online services exist to provide the seller with the expected market value for a given item,
non-homogenous goods such as used vehicles can be expected to have a great deal of
variation in the price that buyers pay. Even among new vehicles, typical pricing and
negotiation is such that buyers may pay significantly different prices for the same make
and model vehicle. For example, work by Scott-Morton et al. (2001) found that buyers
negotiating prices through online infomediaries paid on average $425 less than buyers
negotiating price in person for the same make and model new vehicle. As the overall
cost of searching for a buyer using an online auction can be expected to be quite low
compared to the variation in price for high-valuation items such as vehicles, searching the
market for a high-valuation buyer ("suckers") and adjusting the reserve price downward
over time is likely to appeal to many sellers. Under this strategy, those vehicles which
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sell earlier will be the ones that yield the highest prices while those that sell in subsequent
auctions will yield a lower prices. As a result, we hypothesize:
H1a: For items sold in online auctions, seller's search duration is negatively
associated with seller's price premium.
H1b: For items sold in online auctions, the number of auctions is negatively
associated with the seller's price premium.
Determinants of Duration of Search
It is important to note that the relationship between the antecedents of search
behavior, the duration of search, and the resulting price paid is not fully mediated by the
duration of search, as is shown in Figure 3.1. In each individual auction, it is possible
that a seller will locate a high-valuation buyer. As a result, it is not necessary for a seller
to search across multiple auctions to locate a single high-valuation buyer. Instead, the
sellers' willingness to search—captured by the antecedents of search—influences both
the search duration and the resulting price premium.
Search in Other Channels
The Internet offers many different opportunities and mechanisms for sellers to
reach customers. These may include online infomediatries, online newspapers, or
individual websites. We define search in other channels (SOC) as the seller's efforts to
locate buyers in other online channels not including the eBay Motors channel. In most
cases, it would be impossible to observe a seller's activities in multiple channels for the
same product. However, in the context of vehicles, the unique VIN number along with
the established practice of listing the VIN number when advertising a vehicle online
enables us to measure SOC for each vehicle across different online channels.
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SOC is expected to increase both the duration of search and the resulting seller's
price premium. The justification for these relationships is related to the nature of the
auction channel as a discount channel and the costs incurred as a result of SOC. Similar
to electronic markets for coins (Bajari and Hortacsu 2002; Lucking-Reiley et al. 2000)
and books (Brynjolfsson et al. 2003), the prices for vehicles purchased through online
price negotiation mechanisms are lower than those purchased through the retail channel
(Scott-Morton et al. 2001). While most online channels offering vehicles—i.e., Yahoo
Autos, Autobytel, Cars Direct, or the dealers' website—may provide vehicle and price
listing services, the actual negotiation process occurs face-to-face in the retail channel.
As a result, other channels providing listing services without specific negotiation
functionally can be considered as a way for the retailer to advertise for leads to the retail
channel. An increase in the likelihood that a seller will sell an item through the higher-
valuation retail channel will increase the seller's reserve price in the discount channel
(Genesove 1995). In addition, when a seller experiences positive costs from advertising
through multiple online channels, the price at which the seller experiences zero profit
necessarily increases. In effect, a seller must set a higher reserve price in order to make
the same level of profit. As a result, we expect:
H2a: For items sold in online auctions, seller's search in other channels (SOC) is
positively associated with the seller's duration of search.
H2b: For items sold in online auctions, seller's search in other channels (SOC) is
positively associated with the seller's price premium.
Offer Distribution
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The online market for vehicles provides both buyers and sellers with a
tremendous amount of information. This information can come from both auctions that
end in a completed sale as well as those that do not. In other words, by placing a bid, an
individual is communicating to the seller a willingness to purchase an item at a given
price, and such additional information about the buyer's willingness to pay is helpful for
the seller in setting the optimal reserve price (McAfee and Vincent 1992). We define the
term offer to describe any auction that has at least one bid. The willingness of the seller
to search and the resulting price premium are expected to be influenced by the offer
distribution. We use both the expected offer mean and the expected offer variance as a
way to capture the influence of the offer distribution on these outcomes, describing each
effect individually.
The expected offer mean is predicted to be positively associated with the duration
of search and the resulting price premium obtained by the seller. Like SOC, this
relationship is also linked with the nature of online auctions as a discount channel. When
a seller places a vehicle for sale in a discount channel, the resulting price the seller can be
expected to obtain is significantly less than in the retail channel. Further, the amount of
the discount typically is dependant on the overall vehicle price, such that buyers of more
expensive vehicles obtain a greater discount (Scott-Morton et al. 2001). At the same
time, the cost of re-listing an item on the online auction is a constant and not dependant
on the price of the vehicle
1
. It follows from this that the opportunity costs for selling a
more expensive vehicle online are greater than for a less expensive vehicle relative to the
search costs, suggesting sellers will search longer and expect a greater price premium
when the expected offer mean is higher. Empirical work by Genesove (1995) also
1
At the time the data was collected, the fee for listing a vehicle was $50.
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supports a positive relationship between mean price and search duration, finding that an
increase in the mean price decreases the probability of sale in any given auction. Thus,
we hypothesize:
H3a: For items sold in online auctions, the expected offer mean is positively
associated with the seller's duration of search.
H3b: For items sold in online auctions, the expected offer mean is positively
associated with the seller's price premium.
An increase in the expected offer variance is also expected to increase the
duration of search. The general intuition for this relationship is that an increase in the
variance increases the potential benefits that a seller can hope to obtain from search. A
seller marketing a product in a market with very low variance in offer price would have
little incentive to search in more than one auction. The influence of the available benefits
on search behavior is a central tenant of search theory (Diamond 1985). Analytical work
by Balvers (1990) noted that an increase in this variance increases the willingness of an
individual to search. This has also been supported empirically, as perceptions of price
variance were found to influence the search effort of consumers (Duncan and Olshavsky
1982). In his study of seller search behavior in wholesale auctions, Genesove (1995)
found that an increase in the variance in the price of a product decreases the probability
of a sale while subsequently increasing the price obtained if the auction is successful. In
this context, it is similarly expected that the increase in variance will increase the
duration of search and the resulting price premium obtained by the seller. Therefore, we
hypothesize:
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H4a: For items sold in online auctions, the expected offer variance is positively
associated with the seller's duration of search.
H4b: For items sold in online auctions, the expected offer variance is positively
associated with the seller's price premium.
3.5 RESEARCH METHODOLOGY
We gathered data for 3 months of completed auctions from the eBay Motors
website using an automated agent. This agent captured information about the
characteristics of the vehicles, the sellers, and the auction itself. Vehicle characteristics
included the VIN number, the number of miles, the existence of a seller warranty, the
model year, and whether the vehicle was new or used. The first 9 digits of the VIN
number were used to establish the model and trim level of the vehicle. Seller
characteristics included the positive and negative feedback score of the seller. Auction
characteristics measured included the starting bid, the number of bids, the high bid, and
whether the auction ended with a completed sale. A second automated agent utilized a
popular search engine to estimate the extent to which the seller searched in other channels
(SOC) for the same vehicle, as identified by the unique 17-digit VIN number. We used
the filtered number of unique pages returned as a proxy for SOC.
The overall dataset consisted of 450,040 auctions. Several steps were then taken
to reduce the dataset to include only those vehicles in which the price premium could be
accurately predicted. Because older vehicles typically have more variance in their overall
condition, we first filtered our data to include only vehicles that were less than 5 years
old. In addition, because we were primarily interested in understanding the behavior of
dealers with access to more than one channel, we limited our analysis to only those
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sellers that had sold at least 2 vehicles through the online auction channel. Next, as 30 is
generally used as a rule of thumb as the number in which a distribution approximates the
normal distribution, we eliminated all those models in which there were less than 30
completed sales. This filtering resulted in a total of 31,445 auctions that were analyzed,
including 5,127 completed sales.
The analysis of the data was done in three stages. The first stage involved the
calculation of the price premium obtained by the seller. For the reference point P
min
we
used the lowest price that any seller would accept for a given model of vehicle
(controlling for all vehicle characteristics) over the period of time in which the data was
examined, controlling for vehicle and seller characteristics. To do this, we regressed
vehicle and seller characteristics on the actual sale price to calculate the expected sale
price based on the vehicle characteristics. Subtracting the actual sale price from the
predicted sale price yielded a price residual which controlled for vehicle and seller
characteristics. For each model of vehicle, as determined by the first 8 digits of the VIN
number, the vehicle which had the most negative value for the residual was used as the
reference point P
min
. The price premium obtained was then calculated by subtracting the
residual value for the vehicle designated as P
min
from the residual for all vehicles of the
same model, yielding a condition adjusted value for the price premium. This is similar to
the methodology used by Ratchford and Srinivasan (1993) in their calculation of the
consumer price premium obtained from search, except in that work the dealer invoice
price was used as a reference point.
The second step in the analysis involved calculating the expected offer mean and
the expected offer variance using all auctions in which a single bid was placed—i.e., both
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those that had ended in a sale and those that had not. In calculating the expected offer
mean (m
i
) for a given vehicle, the auction characteristics x
i
, seller characteristics y
i
, and
vehicle characteristics z
i
were used as indicated in the equation below:
E[m
i
| x
i
, y
i
, z
i
] = |
0
+ x
i
|
1
+ y
i
|
2
+ z
i
|
3
(2)
The expected offer variance for a given vehicle can be calculated from the
squared difference between the price p
i
and the expected mean offer E[M
i
| x
i
, y
i
, z
i
]. By
regressing the auction characteristics x
i
, seller characteristics y
i
, and vehicle
characteristics z
i
on the log of the squared residuals we can calculate the expected offer
variance for each vehicle as indicated in the equation below.
E[(M
i
- (|
0
+ x
i
|
1
+ y
i
|
2
+ z
i
|
3
, x
i
, y
i
, z
i
))
2
| = exp (o
0
+ x
i
o
1
+ y
i
o
2
+ z
i
o
3
) (3)
We calculated the values for the expected offer mean and the expected offer
variance using the equations above. As mentioned earlier, the procedure used follows
that in related work, and addition details can be found in Genesove (1995). We first
regressed the auction, seller, and vehicle characteristics on the offer price. This allowed
us to calculate the expected offer mean for each vehicle. A second regression was then
completed on the log of the squared difference between the expected offer mean and the
actual high bid using the same auction, seller and vehicle predictors. The beta
coefficients resulting from this second analysis were used to calculate the expected offer
variance.
The final stage of the analysis was to examine the full system of equations used to
predict the duration of search and the price premium obtained by the seller. Two
instrumental variables were used in the analysis to enable the full system to meet the
necessary rank and order criteria for identification. Appropriate instruments are highly
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correlated with the dependant variables of the equation and not related to the other
independent variables. For the case of duration of search, the total number of vehicles
listed by the seller would be expected to influence the efficiency in which a seller
interacts with online auctions. This efficiency could be expected to be positively related
to the duration of search but not related to the price premium. In predicting the overall
price premium obtained by the seller, the number of bids can be expected to be positively
related to the overall premium but not to the duration of search. Other auction, vehicle,
and seller characteristics were omitted from this analysis because they were already
included in the calculation of the seller's price premium. The full system of equations
examined is shown below.
log(Premium) = |
0
+ |
1
log(A) + |
2
log(SOC) + |
3
E(M
ij
) + |
4
log(E(Var
ij
)) + |
5
log(Dur) + c
log(Dur) = |
6
+ |
7
log(A) + |
8
log(OCS) + |
9
E(M
i
j
) + |
10
log(E(Var
ij
)) + |
11
log(Sold) + c
A = The number of auctions in which a vehicle has been offered
SOC = Search in Other Channels (# of unique sites returned from search of VIN)
E(M) = The estimated value for the offer mean, from equation 2
E(Var) = The estimated offer variance, from equation 3
Dur = The total duration of search (days)
Sold = The total number of vehicles sold by the seller during the study
The analysis of these equations was done using Three Stage Least Squares (3SLS)
estimation. The 3SLS systems estimator is used to calculate the full system of equations,
taking into account the endogenous relationships between the equations while controlling
for the potential correlation among the error terms. 3SLS combines 2SLS and SUR
methods to take into account both dependent repressors and cross-equation correlation of
- 101 -
errors, and is the recommended approach for triangular structural systems (Lahiri and
Schmidt 1978), as in this study.
Results
The first stage of the analysis involved calculating the price premium obtained by
the seller. The regression analysis predicting the final sale price yielded an overall R
2
of
0.898, indicating that the characteristics of the vehicle, seller, and the auction provided a
high degree of explanatory power. Positive feedback, a high starting bid, and the
presence of a warranty each were associated with a higher price premium while negative
feedback and the number of miles were each associated with a lower price premium. The
results of this analysis are shown in Table 3.3. The predicted values calculated using the
beta coefficients from Table 3.3 were used in order to calculate the price premium using
the method described earlier.
The second stage of the analysis predicting the expected offer mean and the
expected offer variance using the vehicle characteristics also indicated a high level of
overall explanatory power, with an overall R
2
of 0.662 for the prediction of the expected
offer mean and an overall R
2
of 0.226 for the expected variance. The results of the
regression analysis are shown in Table 3.4. These analyses indicate that both the
expected offer mean and the expected offer variance are influenced by the characteristics
of the vehicles. Positive feedback had a positive influence on expected offer price and
expected offer variance. A higher starting bid, a warranty, and a new vehicle each led to
a higher offer and lower offer variance.
The results of the 3SLS analysis of the systems of equations indicate support for
the hypothesized predictors, with the exception of the relationship between the number of
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auctions and the price premium. The results of the 3SLS analysis are shown in Table 3.5.
As hypothesized, the expected offer mean and the expected offer variance were positively
associated with both the duration of search and the resulting price premium. The SOC
was positively associated with both the search duration and the resulting price premium
of the seller. The duration of search was negatively associated with the price premium of
the seller, indicating that sellers engaged in a reserve discounting strategy.
Unexpectedly, the number of auctions was positively associated with the price premium,
indicating that there were benefits to having multiple auctions.
Effect sizes for the relationships observed were well within the range where they
represent significant financial incentives for the organizations within this industry. A one
standard deviation change in the duration decreased the price premium by $150 while a
one standard deviation increase in the number of auctions was associated with a $99
increase in the price premium. SOC is associated with a 0.1 day increase in the duration
and a $117 price premium. Statistics from NADA indicate that the average dealer had
over $9 million in sales, but only $90 thousand in profits, with an average profit of $141
on 638 vehicles. Using a discount rate of 8%, the inventory costs of holding each of the
638 vehicles for an additional week results in nearly 14 thousand dollars in inventory cost
and represents 15% of the overall profit. Overall, the effect sizes related to models
examined are of sufficient size that they are likely to have an important influence on the
decision making and sales strategy related to online auctions.
3.6 DISCUSSION
This research provides a new way of viewing the outcome of subsequent auctions
for the same individual product that has not been studied empirically by researchers.
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While a great deal of research has identified factors influencing the outcomes of auctions,
we extend prior work by showing how individual auctions can be influenced by external
factors related to sellers' willingness to search. In reviewing the implications of these
findings, we first discuss the findings related to SOC and then the findings related to the
market characteristics of the mean and the variance of the offer price. We then further
discuss the relationship between duration of search, number of auctions, and the price
premium of the seller.
Search in Other Channels
The results of the analysis also suggest that the extent to which sellers search in
other channels (SOC) influences both the duration of search and the resulting price
premium of the seller. These findings have implications for both buyers and market
makers. Buyers may be able to use the SOC information as a way of identifying sellers
likely to have lower reserve prices—i.e., those exclusively utilizing the online auction
channel. The Internet and electronic markets make it easier for sellers to search for
buyers using the Internet and online markets. However, this search process is not costless
and sellers demand a price premium from buyers in the online auction channel when they
have also listed a vehicle in other online sites.
Market makers may be able to provide greater value and charge additional fees
for the bundling of services with other online channels. This service bundle may allow
sellers to even more easily sell across multiple online channels, providing a method of
price discrimination. Though selling in multiple channels online may not require a great
degree of technical sophistication, sellers still have to interact with multiple interfaces to
list a vehicle on multiple websites. An integration of these sites or automated tools which
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enabled a seller to list a vehicle on multiple different sites using a single interface may
lower the search costs of the seller and the buyer, increasing the overall efficiency of the
market.
Market Characteristics
We found that the market characteristics, as captured by the expected offer mean
and the expected offer variance, influence both the search duration and the price premium
of the seller. The influence of the expected offer mean and the expected offer variance
was identified by Genesove (1995) in a cross sectional study of wholesale auto auctions
which examined whether the reserve price would be met as an auction outcome. This
research extends that work by examining the use of the auction channel across
subsequent auctions for the same unique product, relating the expected offer mean and
the expected offer variance to the price premium of the seller and the duration of search.
Search Behavior
Our results indicated a negative relationship between the duration of search and
the price premium obtained by the seller, suggesting a reserve discounting strategy. This
results in a situation in which the seller's price premium decreases as the overall duration
of search increases, as shown in Figure 3.3. One question that logically follows from this
finding is Do sellers search too much? While a full normative analysis of the seller
search process is outside of the scope of this work, a further examination of the data
shows that in a sufficient number of cases they do. In examining all of the auctions in
which the vehicle appeared in more than one auction, 6% of the time sellers accepted a
price that was lower in the final auction (in which a sale occurred) than was offered in a
prior auction in which the reserve price was not met. For these cases, the average
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difference between the price offered in a prior auction and the final price accepted by the
dealer was $879 (standard deviation = $1500), an amount over four times the average
profit for used vehicles (NADA 2005). This loss is in addition to the inventory holding
costs resulting from the extended search effort.
One way to understand the relationship between the use of electronic auctions
observed here and duration of search is through the metaphor of the Icarus Paradox.
Icarus was given the power to fly when his father Daedalus glued wings to his back.
Once given the power fly, Icarus was too excited by his new ability and flew too close to
the sun, melting his wings and falling to his doom. Within the IS literature, the Icarus
Paradox has been used to understand the negative consequences resulting from managers
having access to extensive information on business processes and operations as a result of
advance enterprise systems. Pinsonneault and Rivard (1998) found that managers with
access to these systems tended to overinvest in the informational component of their role
as managers while neglecting other managerial roles (Pinsonneault and Rivard 1998).
Our data suggests that sellers with access to the online auctions at eBay motors may tend
to overinvest in the search for a high-valuation buyer while neglecting their key goal of
optimizing the overall sales process. As a result, while the channel remains useful as a
way for sellers to identify buyers, the overall efficiency of the channel is much lower than
it could be if sellers engaged in a reservation price strategy as suggested by Ashenfelter et
al. (2002).
The relationship between the number of auctions and the resulting price paid was
unexpectedly positive. This can also be understood by graphing the same search duration
for auctions of different lengths—a shorter auction length will yield a larger number of
- 106 -
auctions for the same auction length, as shown in Figure 3.4. One possible explanation
for this effect is the widely observed bidding pattern known as sniping—in which greater
than 80% of the bids occur in the final minute of the auction (Bapna 2003; Ockenfels and
Roth 2001; Roth and Ockenfels 2000). The positive relationship between the number of
auctions and the resulting price premium suggests that sellers may obtain more benefits
from these additional periods of sniping activity than from additional auction days,
making shorter auctions preferable. A second possible explanation is that because the
seller can be shown to reduce her reserve price with time, those sellers utilizing shorter
auctions make smaller adjustments in their reserve price. These small, targeted
reductions thereby enable sellers to extract additional surplus from buyers. In sum,
though we cannot provide a definitive reason for the relationship between price premium
and the number of auctions without an experiment, this research suggests that sellers
wishing to optimize inventory management should consider multiple shorter auctions.
An alternative explanation of the relationships observed can be considered from
the perspective of the buyer. The buyer also has access to information related to the
completion of prior auctions. This could result in learning whereby buyers access the
price they are willing to pay based on the outcomes of prior auctions. Observing prior
auctions which did not end in a sale may lead the buyer to fear winners curse (Bajari and
Hortacsu 2002; Kagel and Levin 2002; Mehta and Lee 1999) or to expect that a seller's
motivations to sell an item may increase. The interpretation of the results in this fashion,
however, does not influence the overall understanding of the relationships as interpreted
from the perspective of the seller.
Implications for Theory
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Numerous papers have examined the design characteristics and outcomes of
auctions, but relatively few have expanded the theoretical lens to include marketplace
characteristics outside of the focal unit of the individual auction. This research suggests
that there are important extensions to prior work which can be made by examining how
sellers actually use online auction channels, and how auctions are influenced by both the
overall sales process in which they occur and by external factors. In addition, this
research has identified basic strategies that sellers employ in the use of the online auction
channel. In doing so, it opens the door for researchers to examine the complex strategies
sellers.
Implications for Practice
Our findings indicate that the low search costs of the Internet may entice sellers to
search as a way of gathering information that actually may be freely—though not
conveniently—available. Sellers have numerous resources available for pricing vehicles
in the offline and wholesale market. Blue book and black book pricing guides have been
computerized to enable the seller to have nearly instant access to the expected market
value of vehicles. The same is not true for eBay Motors. A quick analysis of the eBay
Motors market indicates that the clearing price for vehicles is frequently somewhere
between the wholesale price and the trade-in value. Although dealers using eBay motors
may have access to prior closed auctions, there is no widely utilized way of determining
the expected clearing price for a given auction or for individual electronic channels.
This suggests that there may be a need for tools which help sellers to understand
the expected market clearing price depending on the channel used to attract the buyer.
Rather than general valuations, these channel-specific valuations systems would help the
- 108 -
seller to optimize investments in different online and offline channels. In addition, the
tool could help the seller to be realistic about pricing decisions by informing the seller of
the probability of sale at different reserve/listing prices for different channels. With a
more complete understanding of the likelihood of sale at different prices, the seller may
be less likely to overinvest in search, thus making the overall market more efficient.
An additional implication for market makers is related to the listing fees. This
research indicates that many sellers tend to use multiple auctions as a way to gather
information about buyers' willingness to pay. Currently, leading market makers such as
eBay offer fixed listing fees for sellers independent of the length of the auction or
whether the item has been offered previously. It appears from the findings here that
retailers may desire additional flexibility to match their specific search preferences. For
instance, some sellers may prefer an "auction bundle" of three three-day auctions to a
single ten-day auction. Thus, the market maker could extract additional surplus by giving
sellers the option to relist their product if it does not sell. An auction bundle may
increase the ability of sellers to adjust their reserve price rapidly to the level that matches
buyers' willingness to pay while increasing the amount of bidding activity.
Limitations
Before concluding it is important to acknowledge the limitations of this work.
There were several limitations specifically associated with the data collected. First, as a
measure of SOC we collected the number of records return by a search from a leading
search engine. It is possible that this method may systematically omit results from
specific types of sites—i.e., those that do not list the VIN number or have implemented
methods which prevent indexing. Further, it is also possible that the values captured for
- 109 -
SOC could represent other sellers listing the vehicle. For example, it is possible that a
dealer could list a vehicle online, sell the vehicle at a wholesale auction, and then a
second seller could offer the vehicle over eBay. This situation, however, would likely
only reduce overall effect of SOC on performance. Thus, the presence of a significant
effect suggests that this may happen infrequently.
A second limitation in measurement is related to the fact that we were unable to
capture transactions initiated via the online auction but are completed offline. For
example, when a reservation price is not met for a sale, a seller could contact a buyer
offline in an effort to negotiate a price verbally. As this analysis primarily focused on
those vehicles which had been sold through the online channel, these vehicles would have
been omitted from the statistical analysis. It is expected that the strategies of sellers
would hold in this situation.
Finally, because our analysis captured auctions over a period of three months, it is
possible that vehicles which where eventually sold were omitted from the analysis. This
selection effect is necessary—the data analysis must end at some point. While the
selection of vehicles sold within a specific time period could slightly reduce the mean
level of search, as sellers searching for an extended period of time would be more likely
to be eliminated from the actions, it is unlikely that this would have an influence on the
relationships observed. Overall, the number of vehicles omitted because of timing would
be quite small compared to the number of vehicles sold.
3.7 SUMMARY
In sum, this research provides a framework for understanding how sellers actually
use the online auction channel as part of the overall sales process. In doing so, we both
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identify relevant antecedents to search duration and the resulting relationship between
search duration and the price premium obtained by the seller. By the incorporation of a
longitudinal component to the study of auction outcomes for the same product, we obtain
surprising results which can directly inform both theory and practice.
- 111 -
3.8 FIGURES
FIGURE 3.1 - CONCEPTUAL FRAMEWORK
FIGURE 3.2 - RESEARCH MODEL
- 112 -
FIGURE 3.3 - PLOT OF PRICE PREMIUM VS. DURATION
750
500
4 5 6 7 8 9 10 11 12 13 14 15 16 17
Duration
Premium
FIGURE 3.4 - PLOT OF PRICE PREMIUM VS. DURATION FOR DIFFERENT
AUCTION LENGTHS
900
700
500
4 5 6 7 8 9 10
Duration
10 Day Auctions 3 Day Auctions
- 113 -
P
r
e
m
i
u
m
P
r
e
m
i
u
m
3.9 TABLES
TABLE 3.1 - DESCRIPTIVE STATISTICS (ALL AUCTIONS ENDING IN SALE)
Variable
Starting Bid (thousand dollars)
log (Positive Feedback) (count)
log (Negative Feedback) (count)
New (yes/no)
log (Miles)
Existing Warranty (yes/no)
Seller Warranty (yes/no)
log (Total Days)
log (Auctions) (count)
Price (thousand dollars)
Expected Offer Variance (thousand dollars)
Expected Offer Mean (thousand dollars)
log (Search Other Channels) (count)
log (Vehicles Sold) (count)
log (Bids) (count)
log (Price Premium) (thousand dollars)
Mean
3.965
4.469
0.906
0.011
10.731
0.263
0.600
2.189
0.232
16.462
0.985
14.618
0.195
0.201
2.846
1.595
Std
8.067
1.765
0.969
0.106
1.287
0.441
0.490
0.562
0.420
10.970
1.080
10.337
0.413
0.406
0.892
0.603
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TABLE 3.2 - CORRELATION MATRIX (ALL AUCTIONS ENDING IN SALE)
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Starting Bid 1.000
log (Positive
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Feedback)
log (Negative
Feedback)
New
log (Miles)
Existing Warranty
Seller Warranty
log (Total Days)
log (Auctions)
Price
EO Mean
EO Variance
log (SOC)
log (Vehicles Sold)
log (Bids)
log (Price Premium)
-0.204 1.000
-0.119 0.647 1.000
0.142 -0.130-0.081 1.000
-0.306 0.195 0.153 -0.614 1.000
0.259 -0.160-0.162 0.175 -0.484 1.000
-0.186 0.162 0.113 -0.131 0.306 -0.732 1.000
-0.016 0.145 0.025 -0.031 0.036 0.022 -0.012 1.000
0.010 0.117 0.039 -0.020 0.042 -0.004-0.003 0.712 1.000
0.479 -0.097-0.107 0.158 -0.460 0.475 -0.336 0.048 0.017 1.000
0.356 -0.099-0.115 0.239 -0.559 0.456 -0.385 0.038 0.015 0.793 1.000
0.641 -0.109-0.105 0.257 -0.522 0.451 -0.323 0.044 0.027 0.934 0.814 1.000
0.061 0.000 -0.020-0.023-0.083 0.147 -0.065 0.069 0.038 0.202 0.153 0.169 1.000
-0.135 0.210 0.102 -0.038 0.136 -0.127 0.111 0.582 0.476 -0.118-0.104-0.109-0.019 1.000
-0.579 0.213 0.110 -0.046 0.100 -0.069 0.066 0.000 -0.026-0.055-0.046-0.186-0.003 0.125 1.000
0.081 -0.038-0.002 0.157 -0.220 0.051 -0.051 0.002 -0.032 0.433 0.316 0.249 0.079 -0.098 0.068 1.000
- 115 -
TABLE 3.3 - REGRESSION ANALYSIS OF FINAL SALE PRICE (USED IN
CALCULATION OF PRICE PREMIUM)
Variable
1
Sale Price
Dependant
log (Positive Feedback) 0.159***(0.039)
log (Negative Feedback) -0.282***(0.070)
Starting bid
log(Miles)
Existing Warranty
Seller Warranty
New
R-Squared
N
0.086**(0.007) -
0.652**(0.059)
2.990***(0.202)
1.097***(0.160)
-1.806*(0.821)
0.898
5,127
1
Note: *1p<0.05 **P<0.001 ***p<0.0001
Dummy variables for vehicle model not shown.
2
Analysis includes all auctions ending in sale (i.e., the reserve price met)
TABLE 3.4 - REGRESSION ANALYSIS OF EO MEAN AND EO VARIANCE
Variable
1
EO Mean EO Variance
Dependant Dependant
log (Positive Feedback) 0.261***(0.036) 0.023**(0.008)
log (Negative Feedback) -0.159*(0.081) -0.033 (0.019)
Starting bid
log(Miles)
Existing Warranty
Seller Warranty
New
0.332***(0.005) -0.002*(0.001) -
1.507***(0.065) -0.307***(0.015)
0.723**(0.213) -0.186**(0.049)
0.652**(0.202) -0.199***(0.047)
8.703***(0.615) -0.570***(0.142)
R-Squared 0.662 0.226
N 31,445
2
31,445
2
Note: *1p<0.05 **P<0.001 ***p<0.0001
Dummy variables for vehicle model not shown
2
Analysis includes all auctions (i.e., those in which the reserve price was met and
those in which it was not met)
- 116 -
TABLE 3.5 - 3SLS ANALYSIS OF DURATION OF SEARCH AND PRICE
PREMIUM
ln (Price
ln Duration
Variable
Vehicles Sold
Dependant
0.494***(0.015)
Premium)
Dependant
Search in Other Channels 0.065***(0.013) 0.073** (0.020)
Expected Offer Mean
0.002 (0.001)
+
0.001 (0.001)
Expected Offer Variance
Number of Auctions
Duration of Search
R-Squared
N
Note: * p<0.05 **P<0.001
0.017*(0.008) 0.170***(0.013)
0.651***(0.013) 0.137**(0.043)
-0.233***(0.045)
0.423
5127
***p<0.0001
- 117 -
APPENDICES
APPENDIX A - ESSAY 1 MEASURES
Strategic Change Orientation
Technological Opportunism (Srinivasan et al. 2002)
(1 = strongly disagree; 7 = strongly agree)
1. We are often one of the first in our industry to find new technology that may
potentially affect our business.
2. We are always on the lookout for information on new technology for our
business.
3. We periodically measure how changes in technology affect our business.
4. The top management of the dealership has a strong emphasis on technological
innovation.
Market Orientation (Navar and Slater 1990)
(1 = strongly disagree; 7 = strongly agree)
1. Our competitive advantage is based on understanding and meeting our customers'
needs.
2. Our managers understand how employees can provide value to customers.
3. We frequently measure customer satisfaction.
4. We pay close attention to after-sales service and maintenance.
Entrepreneurial Orientation (Covin and Slevin 1989)
(1 = strongly disagree; 7 = strongly agree)
1. We are quick to respond to significant changes in our competitors' pricing
structures.
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2. We are very often the first business to introduce new services to customers.
3. In dealing with competitors, we typically adopt a very competitive "undo the
competitors" position.
4. Top management regularly discusses competitors' strength and weaknesses.
IT Capabilities
IT Infrastructure (Bharadwaj et al. 1998)
(1 = strongly disagree; 7 = strongly agree)
Our dealership's information technology (i.e., computers, networks, etc.)...
1. ...meets the business needs.
2. ...has an adequate number of computers with sufficient performance to meet user
needs.
3. ...is reliable and efficient.
4. ...is flexible enough to meet the business needs.
IT Management (Bharadwaj et al. 1998)
(1 = strongly disagree; 7 = strongly agree)
Our dealership's technology manager(s)...
1. ...has specifically explained our technology management practices.
2. ...effectively plans for security control, standards compliance, and disaster
recovery (loss of information, etc.).
3. ...employs the same technology policies throughout the dealership.
4. ...has established effective partnerships with technology providers (such as lead
management systems)
Climate for IT Use (Adopted from Schneider et al. (1998)
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(1 = low; 7 = high)
How would you rate.
1. The efforts to ensure employees use customer management system(s)?
2. The recognition and rewards employees receive for using the customer
management system(s)?
3. The leadership shown by management in supporting the use of customer
management system(s)?
4. The effectiveness of technology hardware, training, and other resources provided
to promote the use of customer management system(s)?
Mindfulness of IT Adoption (Adopted from Knight (2004))
(1 = strongly disagree; 7 = strongly agree)
Our senior managers...
1. ...believe technology will help the dealership to serve customers better.
2. ...believe technology will create a significant competitive advantage to our
dealership.
3. ...take choices of whether we adopt a new technology very seriously.
4. ...show great care in identifying and selecting technologies to adopt.
5. ...are results-focused with deciding whether to adopt new technologies.
6. ...seek outside expertise whenever making decisions related to types of
technology they are not familiar with.
Assimilation of Customer Relationship Management
Automation
Scale adopted from Ray et al. (2005)
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Please describe the extent of adoption of automated sales processes
(0=Don't Intend to Implement, 1= Not yet begun, 3 = Standard Implementation, 5 =
Advanced Implementation)
Automation - Sales Business Unit
1. Automated regular email communications with leads.
2. Automated scheduling of tasks for members of the sales force.
3. Automated assignment of incoming leads to the appropriate person.
4. Automated tracking of responses to marketing promotions (from mailings and
email).
Automation - Service Business Unit
1. Automated regular email communications with customers.
2. Automated creation of personalized service reminders.
3. Automated marketing and follow-up with customers across multiple channels
(mailing, email, etc.).
4. Automated tracking of responses to marketing promotions (from mailings and
email).
CRM System Use
(1=Not used at all; 7=Used very extensively)
CRM System Use - Sales Area
Our dealership uses customer management system(s) to...
1. ...record interactions with customers (i.e., phone calls, customer needs).
2. ...schedule follow-up with customers (through phone calls, personal email).
3. ...understand the overall state of the sales process (i.e., total leads, lead status).
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4. ...review and report ROI (return on investment) by lead source.
CRM System Use - Service Area
Our dealership uses customer management system(s) to...
1. ...record interactions with customers (i.e., service visits, direct marketing
materials).
2. ...categorize customers to identify those which are most valuable to the
dealership.
3. ...measure response rates to marketing promotions.
4. ...assess the lifetime value of our customers.
Process Performance
(Please describe the extent customer management systems have affected.)
(1 = created no value; 7 = created significant value)
Process Performance - Sales Area
1. .the level of service provided to customers.
2. .the productivity of salespersons.
3. .the effective management of inventory.
Process Performance - Service Area
1. .the utilization of the service area (i.e., the percentage of capacity used).
2. .the effectiveness of your service and maintenance promotions.
3. .the value we obtain from existing customer relationships.
Financial Performance (Wall et al. 2004)
(1 = Much worse than competitors; 7 = Much better than competitors)
Sales Area Performance - CONTROL
- 122 -
1992-1994—Vehicle sales—Sales growth
1992-1994—Vehicle sales—Profit Level and ROI
Service Area Performance - CONTROL
1992-1994—Parts and Service Area—Sales growth
1992-1994—Parts and Service Area—Profit Level and ROI
Sales Area Performance
2002-2004—Vehicle sales—Sales growth
2002-2004—Vehicle sales—Profit Level and ROI
Service Area Performance
2002-2004—Parts and Service Area—Sales growth
2002-2004—Parts and Service Area—Profit Level and ROI
- 123 -
APPENDIX B - ESSAY 2 MEASURES
Informational Capabilities
1. Lists price on website
2. Lists options on website
3. Lists all new vehicles on website
4. Lists photo on website
Transactional Capabilities
1. Links to independent sites
2. Tool for credit application
3. Price quote request form
4. Online service/appointment scheduling
Relational Capabilities
1. Count of the number of infomediary partnerships
2. Investment in infomediary partnerships ($/month)
3. Leads from Infomediary partners
Sales Volume
1. Total vehicle sales per month
Complementary Business Process Change
1. Are leads distributed to specific sales people who don't take regular floor traffic?
(yes/no)
Performance
1. Monthly Profit (Total sales from Internet Channel x average profit for Internet
channel)
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Controls
1. Online discount (Offline profit - online profit)
2. Sales volume (total vehicle sales per month)
3. Domestic (count of number of domestic brands carried)
4. Luxury (count of number of luxury brands carried)
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doc_810036134.docx
Study Reports on Electronic Mediation, Transformation, and Business Value: Three Essays in the Retail Auto Industry:- In management, business value is an informal term that includes all forms of value that determine the health and well-being of the firm in the long run. Business value expands concept of value of the firm beyond economic value (also known as economic profit, economic value added, and shareholder value) to include other forms of value such as employee value, customer value, supplier value, channel partner value, alliance partner value, managerial value, and societal value. Many of these forms of value are not directly measured in monetary terms.
STUDY REPORTS ON ELECTRONIC MEDIATION,
TRANSFORMATION, AND BUSINESS VALUE:
THREE ESSAYS IN THE RETAIL AUTO
INDUSTRY
ABSTRACT
This dissertation seeks to answer the following research questions: (1) what properties
enable some organizations to generate more value from information technology (IT) than
others? and (2) through what mechanisms do organizations generate value through IT?
It examines the role of technology in value creation through three essays using three
different aspects of organizational performance.
Chapter 1: Responding to Technology-Enabled Organizational Transformation: The Role
of Strategic Change Orientation
Essay one examines the role of strategic change orientation and three change enablers—
IT capabilities, climate for IT use, and mindfulness of IT adoption—in influencing
business process performance during a period of IT-enabled transformation. The data
source for this essay is a survey of auto retailers facilitated by a leading online
infomediary.
Chapter 2: Profiting From the Internet Channel: The Complementarity of Electronic
Commerce Capabilities and Business Process Change
Essay two accesses the joint role of electronic commerce capabilities and business
process change in a model that examines the value firms derive from the Internet channel.
The data source for this essay is a survey of auto retailers conducted by a leading market
research firm.
Chapter 3: Understanding Retailer Use of Online Auction Channels: Strategies In
Repeated Search Processes
Essay three examines sellers' use of the online auction market and the resulting value
obtained for a given product through the theoretical lens of search theory. We model
sellers' repeated listing of unsold products and adjustment of reserve price as a process of
searching for high valuation customers. The data source for this essay is transactional
data from a leading online auction site specializing in automobiles.
ELECTRONIC MEDIATION, TRANSFORMATION, AND BUSINESS VALUE:
THREE ESSAYS IN THE RETAIL AUTO INDUSTRY
by
Jason Nicholas Kuruzovich
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
2006
Advisory Committee:
Professor Ritu Agarwal, Chair
Professor Paul Hanges
Professor Sanjay Gosain
Professor P.K. Kannan
Professor Henry C. Lucas
Professor Sivakumar Viswanathan
© Copyright by
Jason Nicholas Kuruzovich
TABLE OF CONTENTS
LIST OF TABLES............................................................................................................. iii
LIST OF FIGURES ........................................................................................................... iv
OVERVIEW: ELECTRONIC MEDIATION, TRANSFORMATION, AND BUSINESS
VALUE: THREE ESSAYS IN THE RETAIL AUTO INDUSTRY.................................. 1
CHAPTER 1: RESPONDING TO TECHNOLOGY-ENABLED ORGANIZATIONAL
TRANSFORMATION: THE ROLE OF STRATEGIC CHANGE ORIENTATION ....... 2
1.1 ABSTRACT.............................................................................................................. 2
1.2 INTRODUCTION .................................................................................................... 3
1.3 LITERATURE REVIEW ......................................................................................... 5
1.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 20
1.5 RESEARCH METHODOLOGY............................................................................ 26
1.6 DISCUSSION ......................................................................................................... 33
1.7 SUMMARY............................................................................................................ 37
1.8 FIGURES................................................................................................................ 38
1.9 TABLES ................................................................................................................. 40
CHAPTER 2: PROFITING FROM THE INTERNET CHANNEL: THE
COMPLEMENTARITY OF ELECTRONIC COMMERCE CAPABILITIES AND
BUSINESS PROCESS CHANGE.................................................................................... 44
2.1 ABSTRACT............................................................................................................ 44
2.2 INTRODUCTION .................................................................................................. 45
2.3 LITERATURE REVIEW ....................................................................................... 48
2.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 54
2.5 RESEARCH METHODOLOGY............................................................................ 62
2.6 DISCUSSION ......................................................................................................... 67
2.7 SUMMARY............................................................................................................ 71
2.8 FIGURES................................................................................................................ 72
2.9 TABLES ................................................................................................................. 74
CHAPTER 3: UNDERSTANDING RETAILER USE OF ONLINE AUCTION
CHANNELS: STRATEGIES IN REPEATED SEARCH PROCESSES......................... 79
3.1 ABSTRACT............................................................................................................ 79
3.2 INTRODUCTION .................................................................................................. 80
3.3 LITERATURE REVIEW ....................................................................................... 83
3.4 RESEARCH MODEL AND HYPOTHESES ........................................................ 89
3.5 RESEARCH METHODOLOGY............................................................................ 98
3.6 DISCUSSION ....................................................................................................... 103
3.7 SUMMARY.......................................................................................................... 110
3.8 FIGURES.............................................................................................................. 112
3.9 TABLES ............................................................................................................... 114
APPENDICES ................................................................................................................ 118
APPENDIX A - ESSAY 1 MEASURES................................................................... 118
APPENDIX B - ESSAY 2 MEASURES.................................................................... 124
REFERENCES ............................................................................................................... 126
ii
LIST OF TABLES
CHAPTER 1
TABLE 1.1 - DESCRIPTIVE STATISTICS FOR SURVEY MEASURES
TABLE 1.2 - CORRELATION MATRIX
TABLE 1.3 - ITEM CORRELATION TABLE
TABLE 1.4 - PLS OUTER MODEL LOADINGS
CHAPTER 2
TABLE 2.1 - SUMMARY OF TYPOLOGIES OF IT CAPABILITIES
TABLE 2.2 - DESCRIPTIVE STATISTICS
TABLE 2.3 - PRINCIPAL COMPONENT ANALYSIS
TABLE 2.4 - CORRELATION TABLE
TABLE 2.5 - REGRESSION ANALYSIS RESULTS
CHAPTER 3
40
41
42
43
74
75
76
77
78
TABLE 3.1 - DESCRIPTIVE STATISTICS (ALL AUCTIONS ENDING IN SALE) 114
TABLE 3.2 - CORRELATION MATRIX (ALL AUCTIONS ENDING IN SALE)
TABLE 3.3 - REGRESSION ANALYSIS OF FINAL SALE PRICE (USED IN
CALCULATION OF PRICE PREMIUM)
TABLE 3.4 - REGRESSION ANALYSIS OF EO MEAN AND EO VARIANCE
TABLE 3.5 - 3SLS ANALYSIS OF DURATION OF SEARCH AND PRICE
PREMIUM
115
116
116
117
iii
LIST OF FIGURES
CHAPTER 1
FIGURE 1.1 - CONCEPTUAL FRAMEWORK
FIGURE 1.2 - RESEARCH MODEL
FIGURE 1.3 - PLS RESULTS
CHAPTER 2
FIGURE 2.1 - RESEARCH MODEL
FIGURE 2.2 - GRAPH OF PROFIT VS. NUMBER OF LEADS
CHAPTER 3
FIGURE 3.1 - CONCEPTUAL FRAMEWORK
FIGURE 3.2 - RESEARCH MODEL
FIGURE 3.3 - PLOT OF PRICE PREMIUM VS. DURATION
FIGURE 3.4 - PLOT OF PRICE PREMIUM VS. DURATION FOR DIFFERENT
AUCTION LENGTHS
38
38
39
72
73
112
112
113
113
iv
OVERVIEW: ELECTRONIC MEDIATION, TRANSFORMATION, AND
BUSINESS VALUE: THREE ESSAYS IN THE RETAIL AUTO INDUSTRY
The study of the value generated through the use of information technology (IT)
has been a central theme within the information systems (IS) literature nearly since its
inception. This dissertation adds to this stream by seeking to answer the following
research questions: (1) what properties enable some organizations to generate more
value from IT than others? and (2) through what mechanisms do organizations generate
value through IT? We study the process of value creation in the context of the retail auto
industry using three different aspects of organizational performance. Essay one takes the
broadest view, examining the role of strategic change orientation and three change
enablers—IT capabilities, climate for IT use, and mindfulness of IT adoption—in
influencing organizational performance during a period of IT-enabled transformation.
Essay two focuses more specifically on the Internet channel, examining the
complementarities between electronic commerce capabilities and business process
change in influencing the business value obtained from the Internet channel. Essay three
examines sellers' use of the online auction market and the resulting value obtained for a
product through the theoretical lens of search theory—modeling sellers' repeated listing
of unsold products and adjustment of reserve price as a process of searching for high
valuation customers. Collectively, these three essays represent an important contribution
to the IS field by identifying critical organizational characteristics that influence the value
creation process. In doing so, they also provide a detailed examination of the specific
mediating mechanisms through which this value creation process occurs.
-1-
CHAPTER 1: RESPONDING TO TECHNOLOGY-ENABLED
ORGANIZATIONAL TRANSFORMATION: THE ROLE OF STRATEGIC
CHANGE ORIENTATION
1.1 ABSTRACT
As a result of the emergence of the Internet and net-enabled business processes,
many industries have experienced a period of IT-enabled transformation in which
organizations and business operations changed very rapidly. A natural question that
arises is how can firms survive and even thrive during such transformations? In
addressing this question, we show how a firm's strategic change orientation—a meta-
construct consisting of technological opportunism, market orientation, and
entrepreneurial orientation—can influence the assimilation of IT and the resulting
performance of business processes. We identify and examine three separate change
enablers through which this influence occurs: (1) through the development of IT
capabilities; (2) through the creation of a positive climate for IT use; and (3) through
mindfulness of IT adoption. These three change enablers influence the assimilation of
technology within the organization and the resulting business process performance. We
test the proposed model using a survey capturing the level of assimilation and the benefits
from customer-focused information systems for 153 organizations in the retail auto
industry, a compelling example of an industry that has undergone an IT-enabled
transformation. Results provide significant support for the proposed relationships,
explaining nearly 47% of the variance in IT assimilation, 34% of the variance in process
performance, and 31% of the variance in financial performance.
-2-
1.2 INTRODUCTION
"The information superhighway, it turns out, goes right past a car dealership. A long-
overdue revolution in auto retailing has arrived."
-Fortune Magazine, March 4, 1996
The emergence of the Internet and net-enhanced business processes has had a
transformational impact on many industries, meaning that they have substantially altered
business processes and the nature of competition. In response to these IT-enabled
transformations, organizations have had to develop new ways to interact with and provide
value to customers while facing both new online competitors and new arenas for
competition. These transformations have thus created opportunities and challenges for
organizations, placing otherwise stable industries into periods of extensive operational
change and intense competition (Crowston and Myers 2004). The concept of technology
as a transforming force in the competitive relationships among firms is by no means new,
but rather can be traced back to the Schumpeterian idea of creative destruction
(Shumpeter 1942). However, with the increasing prominence of IT in firm business
processes and as a force in many industries, there is a need for researchers to better
understand how organizations can effectively navigate an IT-enabled industry
transformation (Agarwal and Lucas 2005).
Existing research has identified the transformational aspects of radical IT
innovations on individuals (e.g., Barrett and Walsham 1999; Robey and Sahay 1996;
Winter and Taylor 1996), organizations (e.g., Cross and Earl 1997; Markus and Benjamin
1997; Straub and Watson 2001; Yates and VanMaanen 1996), and society (e.g., Aupperle
-3-
1996; Campbell-Kelly 1996; Davenport and Stoddard 1994; El Sawy et al. 1999).
Although this research provides useful insights into the characteristics and impacts of
transformational technologies, much less is known about the general organizational
properties that enable effective responses to the competitive challenges posed by IT-
enabled transformations. In addition, as effective responses to IT-enabled
transformations are those which create value for organizations, there is a need to
incorporate our understanding of how organizations respond to IT-enabled
transformations into the broader IT-value literature while providing actionable
recommendations to managers and organizations.
This research examines the role of strategic change orientation (SCO)—a meta-
construct consisting of entrepreneurial orientation, technological opportunism, and
market orientation—in influencing the ability of the organization to respond to periods of
IT-enabled transformation. Specifically, we argue that strategic change orientation
influences IT assimilation and the resulting value the organization obtains through three
different change enablers. These three change enablers—IT capabilities, climate for IT
use, and mindfulness of IT adoption—provide a way to understand the theoretical
mechanisms through which SCO can enable an effective response to the challenges posed
by IT-enabled transformations. The overall conceptual framework describing the link
between SCO and performance is shown in Figure 1.1.
We test the proposed model in the context of the retail auto industry, a compelling
example of an industry that has undergone an IT-enabled transformation, examining the
assimilation of customer management systems across 153 dealerships and two business
units (sales and service). While the transformation of the industry has been initiated by a
-4-
variety of technologies—including the Internet, online infomediaries, and dealer
websites—we argue the customer management systems represent a key technology which
helps organizations to manage the transformation. Overall, findings indicate that an
organization's strategic change orientation can be an important determinant of how the
organization assimilates technology into business processes and generates value. In
developing this understanding, we integrate several different streams of research to
explain differences in organizational assimilation and benefits from customer-focused
information systems.
The rest of the paper is organized as follows. The next section reviews the
literature associated with transformation and the other constructs associated with the
model, providing the primary theoretical foundation for this work. We then argue for the
specific hypotheses shown in Figure 1.2. Next, we describe the details related to the
testing of the model, including the measures and analysis techniques used. Finally we
discuss both the limitations and the implications of the findings.
1.3 LITERATURE REVIEW
In this section, we first fully define and review the relevant research on IT-
enabled transformation and then substantiate our claim that the retail auto industry in
particular has experienced such a transformation. Next, we provide a foundation for the
different streams of research integrated through this work. In doing this, we first examine
three types of strategic orientation—technological opportunism, market orientation, and
entrepreneurial orientation—and discuss how each influences an organization's
willingness to respond to technological change. We then examine the literature related to
three change enablers—IT capabilities, climate for IT use, and mindfulness of IT
-5-
adoption—which provide organizations with the ability to effectively deal with
technological change. Finally, we discuss the role of IT assimilation as an important
mediator to value creation. With this as a foundation, in the following section we discuss
the specific hypotheses related to the model shown in Figure 1.2.
IT-Enabled Transformation
Change and IT innovations are necessarily very closely linked, and have been
since Leavitt and Whisler's (1958) seminal article "Management in the 1980's" projected
an overall change in the size of middle management as a result of IT innovations. The
term "transformation" has been used frequently to describe substantial organizational
changes resulting from the presence of a radical IT innovation (e.g., Cross and Earl 1997;
Crowston and Myers 2004; Daniel and Wilson 2003; Jarvenpaa and Ives 1996; King
1996; Robey and Sahay 1996; Scott Morton 1991; Uhlenbruck 2003; Yates and
VanMaanen 1996). Although IT is an important initiator of organizational
transformation, the stream of research examining organizational transformation is much
broader than just that related to IT innovation and draws from the fields of economics,
strategy, and sociology (for reviews, see Pettigrew 1985; Wilson 1992).
Two frequently cited theoretical foundations addressing transformation include
creative destruction (Shumpeter 1942) and punctuated equilibrium (Gersick 1991;
Tushman and Romanelli 1985). Creative destruction refers to the process through which
organizations attempt to gain competitive advantage through innovation. This
competitive advantage, however, is necessarily temporary as market entrance or imitation
by competitors erodes (destroys) profits and forces the firm to continue to innovate.
-6-
The paradigm of punctuated equilibrium provides a separate though closely
related perspective of change, suggesting that there are longer periods of incremental
changes that are punctuated with periods of radical change (Gersick 1991; Tushman and
Romanelli 1985). These periods of radical change may alter the nature of competition,
the domain of direct competitors, the value of firm assets, or the nature of interactions
between customers and suppliers. Thus, the emergence of new technology and the
radical changes associated with it have important implications for organizations.
A third view of transformation has emerged directly from the IS literature. The
situated change perspective (Orlikowski 1996) provides an alternative lens for
transformation that stresses the ongoing and incremental nature of organizational change.
In addition, this perspective identifies the joint role of both social actors and technology
in determining the organizational outcomes from transformation. Another characteristic
of this perspective is that substantial transformations may actually be composed of a
series of smaller changes—suggesting that several incremental IT innovations and social
actors may interact over a period of time to lead to a more discernible transformation.
In order to further our understanding of the characteristics which enable
organizations to thrive during an IT-enabled transformation, we do not have to adopt a
specific perspective on transformation which is exclusive of any one of the three
perspectives reviewed above. Organizational responses to these transformations are
likely to be concentrated in particular periods of more and less change (as in the creative
destruction and punctuated equilibrium models) and also involve ongoing change (as in
the situated change perspective). We do, however, need to argue that the specific context
of transformation meets the basic criteria for a transformation set forth in prior research.
-7-
Prior research has defined transformation in different ways. In their discussion of
general organizational transformation, Romanelli and Tushman (1994) argued that a
transformation occurred when firms had substantial changes in strategy, structure, and
power over a period of two years. In the context of IT-specific investments, Dehning et
al. (2003) argued that transformational technology investments: (1) redefine business
processes or relationships; (2) involve acquisitions or entry into new markets; or (3)
dramatically change how tasks are carried out. While the specific criteria for determining
whether a transformation has occurred may be difficult to fully justify, the general theme
is that there is a substantial change in the competitive environment to which the firm
must respond.
IT-Enabled Transformation in the Retail Auto Industry
In examining the context of the retail auto industry, we adopt similar criteria as
Dehning et al. (2003) and argue that a number of technologies have emerged over time to
constitute an IT-enabled transformation. We describe three closely related but distinct
changes specifically impacting the business processes, relationships with customers, and
the creation of new markets—which together, we suggest, contribute to the overall
industry transformation.
Business Processes: The process of purchasing vehicles has changed substantially in the
last decade (Ratchford et al. 2003). The presence of the Internet and online infomediaries
enable individuals to communicate with multiple dealers, placing significant new
demands on traditional salespeople in terms of their workload and work tasks. While in
the past salespeople may have only interacted with individuals face-to-face, many
customers are now likely to expect to be able to negotiate through electronic channels.
-8-
This substantially changes the business process through which dealerships initiate,
manage, and close transactions. In addition, as individuals wanting a particular vehicle
can more easily contact and negotiate with multiple dealerships, dealerships must adjust
their business process to actively manage a larger number of relationships.
Relationships with Consumers: Both the lowered search costs provided by the Internet
and the emergence of online infomediaries offering information and transactional
services have contributed to changes in the negotiations with customers. With the
emergence of the Internet, consumers rapidly began utilizing the web as a source of
information, and in 2004 over 64 percent of new vehicle purchasers used the web in some
way as part of the purchase process (Power 2004). This has significantly reduced the
level of information asymmetry, shifting the advantage in negotiations away from the
dealership. Purchasing through an online infomediary has been shown to result in a
savings of approximately 2% when compared to individuals who negotiate directly with
the dealership (Scott-Morton et al. 2001). This demonstrates the important financial
impact resulting from this change in the relationships with customers.
Creation of New Markets: By reducing the search costs of obtaining a price quote, the
lowered search costs of the Internet have reduced the overall importance of location for
competing dealerships (Bakos 1997). Dealerships have the opportunity to compete for
customers that they would have had little access to before the emergence of the Internet,
online infomediaries, and online auctions. As a large scale and vivid example of this, the
eBay Motors market represents a national auction market where dealers can sell to
consumers from around the country. This has been facilitated by the emergence of
shipping services which facilitate the transport of vehicles from dealerships to consumers
-9-
residing in different areas of the country. Overall, this and other online markets for
vehicles provide dealers with substantial new opportunities to reach customers.
Customer Management Systems
Customer management systems have features which provide auto-retailers with
the ability to manage many of the challenges of IT-enabled transformation described
above. These features can be generally categorized into three types:
workflow/messaging, information management, and analysis.
Workflow/Messaging: A key feature of customer focused systems is workflow
automation. As dealers may receive leads from many different sources. Customer
management systems provided an integrated way of centralizing these leads and routing
them to the individuals responsible. This can also enable such things as automated
scheduling of follow-up calls and messaging features which maintain contact with a
customer both before and after the purchase. Maintaining constant contact through email
may help the sales and service area to improve overall performance by increasing
customer loyalty.
Information Management: As the Internet enables dealerships to more easily access
customers from a variety of sources, managing information related to the preferences and
status of customers is more important than ever. Following-up with customers with
appropriate information allows the salesperson to meet the needs of the customer more
quickly and appropriately. Much of the usefulness of customer management systems as
an information tool requires that employees actually use the system to record interactions
with customers
- 10 -
Analysis: Customer management systems also enable dealerships to conduct detailed
analyses on work processes and customers. Customer management systems enable
dealerships to obtain detailed information on outcomes such as the conversion rate of
leads and the overall return on the investment of infomediary partnerships. Detailed
analyses may also be directly linked with marketing, providing a way to target offers and
mailings to meet the needs of specific customer profiles. In the face of increased
competition, targeted actions can help dealerships to increase the efficiency of their
advertising spending for both sales an service functions.
Strategic Orientation
The ability to identify and respond to changes initiated by technology is critical to
the performance and often even to the survival of firms—especially for firms located in
industries undergoing IT-enabled transformation. While the dominant paradigm of IT
investment suggests that IT adoption is necessarily a positive thing (Fichman 2004), we
acknowledge that this is not always the case with all technologies and situations.
However, we argue that when a transforming technology emerges, it becomes necessary
for firms to take action. Often, this action may involve the direct adoption of technology
associated with the transformation or technology which helps the firm to manage the
challenges of the transformation. In this case, as each of the three transformational
factors related to the used car industry involves some change in their interactions with
customers, we argue that customer-focused information systems represents a technology
likely to make an important difference in overall performance by enabling firms to deal
with the challenges created by the Internet and infomediaries. The willingness of a firm
- 11 -
to implement and use customer-focused systems is therefore expected to be associated
with performance outcomes of the related business processes.
The strategic orientation of the firm is one way to capture the willingness of an
organization to adopt technologies. The characterization of strategic orientation has
taken two different general strategies: (1) the characterization of different strategic types
and (2) the identification of strategic characteristics. The first method argues that there
are general ways of characterizing organizations which are essentially value neutral—i.e.,
neither good nor bad—and performance results from the alignment between the different
parts of the organization or between the organization and the environment. Miles and
Snow's (1978) topology of Defenders, Analyzers, and Prospectors is an example of this
method of characterizing strategic orientation. This view of strategic orientation has been
used as a way to understand the alignment with respect to both the IT (Sabherwal and
Chan 2001) and the marketing function (Vorhies and Morgan 2003).
The second method of conceptualizing strategic orientation is to measure it across
different specific constructs. In this case, rather than being of a strategic type (i.e.,
defender, prospector, etc.) organizations are either high or low in a trait used to
conceptualize aspects of the strategy of the organization. Work by Venkatraman (1989)
developed six general measures of strategic orientation capturing dimensions: (1)
aggressiveness, (2) analysis, (3) defensiveness, (4) futurity, (5) proactiveness, and (6)
riskiness. These general dimensions of strategic orientation, however, are limited in
some cases because of their generality. By attempting to capture all aspects of an
organization's strategy, they may be unduly complex in addressing specific contextual
challenges the organization may face. As an alternative, researchers have developed
- 12 -
constructs capturing specific organizational traits that lead to performance for such things
as innovation (Hurley and Hult 1998; Lukas and Ferrell 2000) and entrepreneurship
(Dess and Lumpkin 2005; Jambulingam et al. 2005; Wiklund and Shepherd 2005).
As the objective of this work is to identify those characteristics which enable
organizations to perform well during an IT-enabled transformation, we chose to capture
the strategic orientation of the organization using specific rather than general dimensions
of strategic orientation. In reviewing the extant literature on the various concepts
researchers have used to describe an organization's orientation toward the types of
challenges most prominent in IT-enabled transformations, we identified three relevant
constructs: (1) technological opportunism, (2) market orientation, and (3) entrepreneurial
orientation. Each of these constructs represent organizational characteristics which
enable a response to challenges faced by the organization during an IT-enabled
transformation, as are further discussed below.
Technological Opportunism
Technological opportunism refers to the willingness of the organization to
identify, understand, evaluate, and respond to new technologies (Srinivasan et al. 2002).
Technological opportunism is consistent with several other streams of research
examining firm processes as they relate to new technologies, such as Wheeler's (2002)
Net-Enabled Business Innovation Cycle (NEBIC). From a strategic perspective,
technological opportunism is an organizational characteristic similar to what is identified
in the Miles and Snow (1978) typology as a prospector firm, but with specific orientation
toward technology (Srinivasan et al. 2002). Thus, technological opportunism is expected
- 13 -
to be an important factor in capturing a firm's willingness to respond to the technological
changes associated with an IT-enabled transformation.
Market Orientation
Within the marketing field there has been a long history of focus on the
importance of the customer relationship (Kotler 1967), and market orientation is a critical
construct which researchers have extensively used to understand a firm's strategic
orientation toward its customers. Market orientation identifies the degree to which a firm
engages in behaviors associated with the identifying and satisfying of customer needs,
behaviors found to have many positive outcomes for firms (for a review, see Kirca et al.
2005). Research has identified the positive relationship between market orientation and
customer and employee outcomes, innovation, and organizational performance (Jaworski
and Kohli 1993; Navar and Slater 1990). As a result, market orientation provides a way
of assessing the willingness of the firms to respond to the needs of their customers
through the application of technology.
Entrepreneurial Orientation
The organizational literature has addressed the overall importance of taking risks
and making the changes necessary to take advantage of new opportunities.
Entrepreneurial orientation captures the degree to which firms engage in processes,
practices, and decision-making styles that involve risk taking and may lead to entry in
new markets (Covin and Slevin 1989; Lumpkin and Dess 1996; Miller and Barbosa
1983). Entrepreneurial orientation has also been found to be an importation factor in firm
performance and innovation (Attuahene-Gima and Ko 2001; Lee et al. 2001; Lumpkin
and Dess 1996; Zhou et al. 2005). In the context of an IT-enabled transformation,
- 14 -
entrepreneurial orientation captures important characteristics which may cause firms to
have a willingness to enter newly-created markets provided by the Internet and electronic
commerce.
Change Enablers
In this section we integrate several streams of literature to identify firm
characteristics which facilitate and enable change. These include IT capabilities, climate
for IT use, and mindfulness of IT adoption.
IT Capabilities
IT capabilities refer to the ability of the organization to implement and manage
technologies and are based on the resource based view (RBV) of the firm. Originating in
the field of strategic management, the RBV of the firm (Barney 1986; Barney 1991;
Penrose 1959; Wernerfelt 1984) has become a critical lens through which researchers
have examined the drivers of firm performance and competitive advantage. The RBV
suggests that firms compete on the basis of VRIN resources—those that are valuable,
rare, inimitable, and non-substitutable—and argues that the possession of VRIN
resources can lead to a strategic competitive advantage (Barney 1991; Conner 1991).
While some have noted that defining a resource as something which leads to competitive
advantage is tautological (Priem and Butler 2001a, b), the RBV has been a useful lens for
organizational researchers from strategy (Brush and Artz 1999; Mahoney and Pandian
1992; Peteraf 1993; Tippins and Sohi 2003), marketing (Hunt and Morgan 1996; Slater
1997; Slotegraaf et al. 2003; Vorhies and Morgan 2005), human resources (Becker and
Gerhart 1996; Bowen and Ostroff 2004; Colbert 2004; Delaney and Huselid 1996), and
- 15 -
IS (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer 2002) to explain
both organizational performance and business process performance.
Although there are numerous dimensions of IT capabilities that have been
identified (for a review, see Wade and Hulland 2004), IT management capabilities and IT
infrastructure represent two of the most critical. IT management capabilities reflect an
organization's ability to manage IT implementation projects and implement new systems,
and they have been argued to be the only IT-related capabilities which lead to sustained
competitive advantage (Clemons and Row 1991). IT infrastructure is a more
fundamental enabler, providing organizations with a foundation of technology on which
to build new IT innovations (Zhu 2004). Together the ability to manage technology and
the infrastructure on which to build new technology applications provide an important
way to characterize the overall IT capabilities of an organization.
Climate for IT Use
Numerous individual-level studies have identified the importance of social
influence on IT adoption and use (for a review, see Venkatesh et al. 2003). In this
research we introduce climate for IT use as employees' shared perceptions of the
importance of IT use within the organization. Climate has been defined generally as the
message employees get about the values which are important to the organization
(Schneider and Bowen 1995), and climate has been used to understand technology
implementations (Klein et al. 2001), service outcomes (Glisson and James 2002;
Schneider et al. 1998) and product innovation (Wei and Morgan 2004). In its essence,
climate provides a way to understand how the collective policies and practices of the
organization influence the willingness of individuals to behave in specific ways—here,
- 16 -
related to the use of new IT. Climate for IT use is expected to be an important factor in
understanding how organizations implement necessary change related to the assimilation
of IT innovations.
Mindfulness of IT adoption
The dominant paradigm within the IT literature has emphasized the positive
aspects of IT adoption and investment. However, recent work has highlighted the
important role of mindfulness of IT adoption in making IT-related decisions (Butler and
Gray 2006; Fichman 2004; Swanson and Ramiller 2004). Organizational mindfulness
originates in the study of high reliability organizations, such as aircraft carriers and
nuclear power-generation stations, where failure and experimentation are untenable
(Weick et al. 1999). In studying IT adoption, mindfulness provides a way of
understanding how organizations address adoption decisions related to new technologies
(Butler and Gray 2006; Fichman 2004; Swanson and Ramiller 2004). Not all
technologies may be valuable, and the mindful organization is cognizant of that. Thus,
mindfulness influences the resulting value gained from technology adoption through the
improvement of decisions related to the adoption itself—i.e., mindful organizations adopt
IT only when it makes business sense and fits the needs of the organization within the
specific competitive environment. In addition to influencing technology adoption
choices, mindfulness is also expected to play an important role in what an organization
does with a technology once it is adopted.
Assimilation, Use, and IT Value
An extensive literature exists on the adoption, assimilation, and use of IT
innovations, and a thorough review is beyond the scope of this work. However, in
- 17 -
reviewing relevant points from this literature, two important observations can be made:
(1) technology assimilation is a critical mediator in generating value from technology,
and (2) understanding the specific conceptualization of assimilation is important to
understanding the value created by a given technology.
The assimilation of IT has been characterized as a series of key events, the most
significant being the acquisition of the system, (i.e., the technology is purchased by the
organization) and the deployment of the technology throughout the organization (i.e., the
organization implements and begins to use the system). Information technology cannot
create value for an organization unless it is both acquired and deployed in the
organization. The conceptualization of assimilation as an event or series of events,
however, is very limited in helping to develop an understanding of how organizations
obtain value from technology. In other words, the treatment of assimilation as binary
(yes/no) provides very little insight into how characteristics of the implementation itself
may influence performance outcomes. As an alternative, many researchers have instead
opted for technology use as a key way to capture the assimilation of information
technology within organizations (e.g., Armstrong and Sambamurthy 1999; Devaraj and
Kohli 2003; Massetti and Zmud 1996; Zhu and Kraemer 2005). Generally, those
organizations which use a system more extensively are argued to obtain more value from
it, and empirical studies have largely supported this assertion (Armstrong and
Sambamurthy 1999; Devaraj and Kohli 2003). This is the same underlying assumption
that has driven much of the focus on use as a key measure of success at the individual
level (DeLone and McLean 2003; DeLone and McLean 1992).
- 18 -
While use provides a way of characterizing how a technology has been
assimilated within an organization, it has some limitations. The one-dimensional
characterization loses potential richness in how the assimilation occurs. For example, use
as characterized by employee interactions with a technology (e.g., Zhu and Kraemer
2002) may be much different than measures of actual use (e.g., Devaraj and Kohli 2003).
Although issues related to differences in the objective and subjective measures of use
have been extensively examined in the study of use on the individual level (Ettema 1985;
Straub et al. 1995), these differences are likely to be exacerbated in the study of use at the
organizational level, as organizational level use may include both the employees which
use the technology and the automated processes which are configured as part of the
technology implementation.
As an alternative to the single dimensional conceptualization of use, in this
research we conceptualize assimilation as a second-order construct consisting of the use
of employees and the automation of the technology implementation itself. Use captures
the extent to which individuals within the organization look to the technology as an
effective solution to tasks. Automation instead captures the extent to which different
parts of the business process have been automated as part of the IT implementation. By
capturing separately use originating from the employees (use) and the implemented
technology itself (automation) through a second-order construct, we provide a richer
treatment of the understanding of assimilation and thereby extend theory focusing
directly on the technology artifact (Benbasat and Zmud 2003; Orlikowski and Iacono
2001).
- 19 -
1.4 RESEARCH MODEL AND HYPOTHESES
This section provides the theoretical background and justification for the model
outlined in Figure 1.2. In supporting this model, we integrate the streams of research
outlined earlier to predict both the overall level of assimilation and the resulting business
process performance.
Strategic Change Orientation
Each of the three dimensions of SCO—technological opportunism, market
orientation, and entrepreneurial orientation—are associated with specific organizational
motivations and have associated organizational processes involved in the fulfillment of
these motivations. Organizations high in technological opportunism have an underlying
motivation and the processes to identify technologies relevant to business (Srinivasan et
al. 2002). Likewise, organizations high in market orientation have underlying
motivations to meet the needs of customers and the processes in which to identify when
actions are necessary in order to meet those customer needs (Day 1994; Kirca et al.
2005). Finally, organizations high in entrepreneurship orientation have motivation to
identify new markets in which to compete and the processes in place to identify those
market opportunities (Attuahene-Gima and Ko 2001; Zhou et al. 2005). Thus, though
three organizations, each high in only one of the dimensions of SCO, may react to
different stimuli of an IT-enabled transformation, they may each enable a comparable
response.
SCO and IT Capabilities
SCO is expected to positively influence the level of IT capabilities. The primary
theoretical understanding of how organizations develop capabilities is through the
- 20 -
concept of dynamic capabilities. Dynamic capabilities refer to those firm capabilities that
enable organizations to integrate, build, and reconfigure to address changes in the
competitive environment (Teece et al. 1997). This perspective has provided an important
way for researchers to understand how firms develop IT-related capabilities in the midst
of continually changing technologies (Daniel and Wilson 2003; Sambamurthy et al. 2003;
Wheeler 2002; Zhu and Kraemer 2002).
While the conceptualization of dynamic capabilities as a mechanism through
which organizations develop capabilities has been extensively utilized, limited research
has identified specific constructs which constitute dynamic capabilities. Here, we argue
that SCO constitutes a dynamic capability because of its role in initiating changes. The
presence of stimuli associated with the IT-enabled transformation will enable firms high
in SCO to develop IT capabilities. In other words, the strategic processes associated with
SCO will lead organizations to build the IT capabilities necessary to meet the challenges
and opportunities of the competitive environment. As a result, we hypothesize:
H1: Strategic change orientation is positively associated with IT-capabilities.
SCO and Climate for IT Use
Climate, as noted earlier, can be generally characterized as the message the
employees receive from the organization. When introducing IT to a particular situation,
research has found that contextual factors can make a tremendous difference in both how
the technology is perceived and how it is appropriated by members of the organization
(Barely 1990; DeSanctis and Poole 1994; Fulk 1993). As a result, when technology
becomes prevalent in a new context, the messages the members of the organization
receive about this technology, which form the climate for IT use, can be expected to
- 21 -
originate from other more fundamental organizational characteristics captured by SCO.
For example, if an organization high in market orientation had already established the
importance of meeting customer needs, a CRM software package identified as a way to
meet customer needs would more likely lead to a positive climate. Similar arguments
would hold for both technological opportunism and entrepreneurial orientation.
Therefore, we expect:
H2: Strategic change orientation is positively associated with the climate for IT
use.
SCO and Mindfulness of IT Adoption
Like the various components of SCO, mindfulness implies a distinct and
measured process of issue identification, consideration, and response. This process
involves matching the needs of the organization with the offerings of the technology.
The opposite of mindfulness—i.e., mindlessness—implies that decisions are made as a
result of trends and fads without consideration of the objective business case (Fiol and
O'Connor 2003; Krieger 2005). SCO is expected to increase mindfulness through the
increased sophistication of the decision making process. Thus, organizations high in
SCO may be expected to more easily adopt mindful practices. It follows that:
H3: Strategic change orientation is positively associated with the mindfulness of
IT adoption.
IT Capabilities and IT Assimilation
IT capabilities and the broader theoretical lens of the RBV have provided
researchers with an important way to understand how the effective management of
technology can lead to improved business performance (Bharadwaj 2000). IT
- 22 -
capabilities have been argued to be multidimensional (for a review, see Wade and
Hulland 2004), and the two dimensions examined here—IT management capabilities and
IT infrastructure—influence assimilation in different ways. IT management capabilities
may improve assimilation of the technology through improved system planning or
implementation (Clemons and Row 1991; Mata et al. 1995). These capabilities allow
organizations to more effectively bring IT-related innovations from the decision to adopt
the technology through to full organizational assimilation.
IT infrastructure has also been found to be a critical firm capability necessary to
fully take advantage of new technologies (Armstrong and Sambamurthy 1999; Keen
1991; Weill and Broadbent 1998). IT infrastructure can be categorized as a strategic
option or real option (Bowman and Hurry 1993). The options lens provides a way for
managers to view IT investments when the level of uncertainty is high, investments are
irreversible, and projects are flexible in nature (Dixit and Pindyck 1994). The option
enabled by infrastructure technology is the option to implement more complex
technologies in the future, such as customer-focused information systems. A strong
infrastructure may also enable the organizations to assimilate complex technologies more
rapidly and at lower cost. Infrastructure may enable greater integration between systems
and fewer user problems related to system failure and or unavailability. Together, IT
management capabilities and IT infrastructure provide an important way of characterizing
the IT capabilities of an organization and are expected to influence the level of IT
assimilation that occurs. As a result, we hypothesize:
H4: IT capabilities are positively associated with IT assimilation.
Climate for IT Use and Assimilation
- 23 -
As indicated, climate originated as a way to conceptualize the message those in
the organization receive relative to a specific action. A positive climate for IT use will
improve assimilation in two key ways. First, because the general attitude of the
organization toward a given technology improves with a more positive climate, the
degree to which the individuals within that organization will use the systems involved
will increase. Research on individual-level IT adoption has consistently shown
subjective norm to be an important influence of an individual's use of the technology
(Agarwal 2000; Karahanna and Limayem 2000; Venkatesh et al. 2003). This collective
effect, captured on the organizational level through climate for IT use, is expected to
influence overall assimilation levels. Second, as much of the investment in
configuration, integration, and automation will only provide payback to the organization
if associated systems are used by individuals, a positive climate for IT use will lead
organizations to increase their investment in automated processes with the expectations
of greater returns from use. In other words, organizations with more positive climates for
IT use will invest more in assimilation because they expect the technology to be used and
provide value. For these reasons, we hypothesize:
H5: Climate for IT use is positively associated with IT assimilation.
Mindfulness of IT Adoption and Assimilation
Mindfulness of IT adoption is also expected to be positively related to the level of
IT assimilation. The importance of mindfulness in the adoption and appropriation of
technology rests on the belief that engaging in systemic processes will lead to better
decisions in evaluating a technology (Swanson and Ramiller 2004). In other words,
sense-making processes involving the business implications of a new technology may be
- 24 -
necessary in order to effectively assimilate that technology into the organization (Anand
and Peterson 2000). Mindful organizations will adopt the technologies that will be the
most beneficial and pass on those technologies that involve a fad or bandwagon effect.
The overall effect is that for a given group of organizations which have adopted a
technology, those which engaged in mindful processes will be more likely to have a good
fit between the technology and the organization. This fit can be expected to improve the
overall assimilation within the organization, as the IT can be more easily integrated into
business processes. In addition, as the process of mindful adoption is likely to inform the
assimilation process more than mindless processes, mindful organizations will be more
likely to take the implementation steps necessary to promote assimilation. Thus, we
hypothesize:
H6: Mindfulness of IT adoption is positively associated with IT assimilation.
Assimilation and Performance
The extent of assimilation is also expected to be positively associated with
performance. This general point has been made in the study of individual use of IT
(Davis 1989; Venkatesh and Davis 2000; Venkatesh et al. 2003), IT appropriation or
structuration (DeSanctis and Poole 1994; Orlikowski 1992, 2000), and technology
assimilation (Armstrong and Sambamurthy 1999; Chatterjee et al. 2002; Fichman and
Kemerer 1999; Gallivan 2001). Individuals, groups, and organizations must use
technology in order for the technology to have an impact on organizations. As noted by
Orlikowski (1992, p. 410), "On its own technology is of no import; it plays no
meaningful role in human affairs. It is only through the appropriation of technology by
humans (whether for productive or symbolic ends) that it plays a significant role and
- 25 -
exerts influence." Further, empirical studies have found the extent of IT assimilation and
use to be an important mediator in the value generated by an IT system (Devaraj and
Kohli 2003; Zhu and Kraemer 2005). Therefore, we hypothesize:
H7: Assimilation is positively associated with process performance.
H8: Assimilation is positively associated with financial performance.
An additional relationship between business process performance and financial
performance is expected. Process performance benefits the organization in terms of
operational benefits associated with productivity and inventory management. These
intermediate process-related outcomes also have a relationship to financial performance,
as improved productivity and lower inventory costs are also likely to result in improved
financial performance. As a result, we hypothesize:
H9: Process performance is positively associated with financial performance.
1.5 RESEARCH METHODOLOGY
Sampling and Data Collection
To facilitate the execution of this study, we partnered with a large online
infomediary and CRM software provider for the retail auto industry, which we refer to
hereafter as NetAuto. This partnership allowed access to senior level individuals within
organizations which had implemented CRM software packages, and the organization had
an existing mechanism to distribute and collect surveys. The subject pool included the
sales, service, and general managers of auto retailers, identified from a listing of
NetAuto's customers and public listings of auto retailers. Analysis was conducted on the
level of the business unit, with sales and service business units measured separately. The
population of dealerships selected with validated contact information was 893. An email
- 26 -
was sent to each individual asking them to participate in the study. In the email, we
included a brief description of the study, informed them that the survey will take
approximately 15-20 minutes to complete, and provided the hyperlink to the online
survey. The first thing the respondent saw when directed to the survey was the informed
consent form. After reading the consent form and selecting "I Agree," the dealership
general manager, sales manager, or service manager began the survey. In an effort to
improve the response to the survey, participants were offered a chance to be randomly
selected for a prize of an IPOD Nano (4 were given away) and offered a summary of the
results of the survey when completed. In addition, reminder letters were sent 2 weeks
and 4 weeks after the initial contact, and follow-up phone calls were conducted during
the 3 months following the initial contact.
Measures
Constructs were measured using scales that have been validated in previous
research or developed in conjunction with industry professionals in order to ensure
content validity. Unless specifically indicated otherwise, we measured items on a 7-point
Likert scale with anchors 1 = "strongly disagree" and 7 = "strongly agree."
A full listing of the survey items is found in Appendix A.
Technological opportunism was measured using an abbreviated version of the
scale developed by Srinivasan et al. (2002). A sample item for technological
opportunism is "we actively seek intelligence on technological changes in the
environment that are likely to affect our business." Measures of market orientation
capture the degree to which the firm places customer needs at the top of its organizational
chart, but the debate of how to measure market orientation has been extensive in the
- 27 -
marketing literature (Homburg and Pflesser 2000). The main division in the
measurement of market orientation is whether market orientation is a series of
organizational processes (Kohli and Jaworski 1990) or whether it is a type of culture
(Navar and Slater 1990). The Navar and Slater scale was selected because it exhibited a
high level of efficiency—i.e., it used fewer items to obtain a consistent measurement
(Matsunoa et al. 2005). Because of the differing features of customer interactions in
different industries, market orientation scales are frequently updated and adapted to the
specific context in which they occur (Attuahene-Gima and Ko 2001; Dobni and Luffman
2003; Zhou et al. 2005). As a result, measures were adapted to conform to the retail auto
industry. A sample item from the market orientation scale includes "We closely monitor
and asses our level of commitment in serving customers' needs." Measures for
entrepreneurial orientation capture the degree to which the organization engages in risk-
taking and proactive behaviors and were based on Covin and Slevin (1989). A sample
item for entrepreneurship orientation is "We are very often the first business to introduce
new services to customers."
Measures of IT management capabilities and IT infrastructure were adopted from
Bharadwaj et al. (1998). IT management capabilities capture the extent to which the
organization effectively manages the IT function, including processes associated with the
delivery, operation, and maintenance of systems. A sample item for IT management
capabilities is "Our IT management team has established an effective system for IT
planning." IT infrastructure captures the extent to which the organization has a
foundation of networks, system architectures, and computers necessary for ensuring
adequate processing capabilities. A sample item for IT infrastructure is "Our corporate
- 28 -
infrastructure (computers, networks, etc.) has appropriate network architecture to meet
business needs."
Measures for climate for IT use were adopted from Schneider et al. (1998)'s measures
of service climate and capture the extent to which employees within the organization
receive a positive message about the use of IT. A sample item for climate for IT use is
"How would you rate.the recognition and rewards employees receive for using the
customer management system(s)?" The anchors low (1) to high (7) were used.
Measures for mindfulness of IT adoption were adapted from Knight's (2004)
development of measures of collective mindfulness. These measures incorporated
organizational qualities such as preoccupation with failure and reluctance to simplify
interpretations (Weick et al. 1999). A sample item for mindfulness is "Our senior
managers...take choices of whether we adopt a new technology very seriously."
Measures for the assimilation of systems are typically context-specific. As described
earlier, our assimilation measures separately captured the degree to which the systems
were used by individuals within the organization and the degree to which the systems
were automated. In addition, we developed separate measures for assimilation in sales
and service business units. These measures were developed through an examination of
customer-focused systems and extensive consultations with industry experts and actual
dealers. Measures for use captured the degree to which the systems were utilized by
individuals within the organization to both store and gather information and analyze
business processes. A sample item for use in the sales area included "Our dealership uses
customer management system(s) to...record interactions with customers (i.e., phone calls,
customer needs)." A sample item for use in the service area included "Our dealership
- 29 -
uses customer management system(s) to...record interactions with customers (i.e., service
visits, direct marketing materials)." The measure for automation captured the degree to
which systems had business and work tasks configured to run automatically. A sample
item for automation in the sales area was, "Automated scheduling of tasks for members
of the sales force." A sample item for automation in the service area was, "Automated
creation of personalized service reminders." Scales for automation were adopted from
Ray et al. (2005) and included the anchors: 0=Don't Intend to Implement, 1= Not yet
begun, 3 = Standard Implementation, 5 = Advanced Implementation.
Objective performance data is often very difficult to obtain for privately-held
firms and when data is needed on the level of the strategic business unit (SBU). As a
result, researchers frequently collect performance data through survey measures. Prior
research has found a high degree of correlation between subjective and objective
measures of performance (Dess and Robinson 1984; Gerhart et al. 2000; Wall et al.
2004), and subjective measures of performance have frequently been utilized in
organizational research (Barua et al. 2004; Guthrie 2001; Youndt et al. 1996; Zhou et al.
2005). With regard to the performance of firms, we are interested in both the process and
financial measurements of performance at the level of the business unit. In addition, as
different business units have different performance measures, we utilized business unit
specific measures of process performance to capture operational improvements. A
sample item for process performance in the sales area was "Please describe the extent
customer management systems have affected.the level of service provided to
customers." A sample item for process performance in the service area was "Please
describe.the utilization of the service area (i.e., the percentage of capacity used)."
- 30 -
Financial performance captured the sales growth and profit level for both the period
1994-1996 (i.e., before the transformation) as well as the period 2002-2004 (i.e., after the
transformation). Answers were captured using a 7-point Likert scale with anchors 1 =
"much worse than competitors" and 7 = "much better than competitors."
From the original 893 individuals contacted, 153 useable responses were received,
representing a response rate of 17%. Of the respondents, 106 answered questions related
to the performance of the sales area and 47 related to the service area. The average
number of employees among the respondent organization was 70.1 (SD=58.9). To
address concerns of non-response bias (Armstrong and Overton 1977), we compared the
responses of those individuals who responded after the initial email to those who had
responded after the phone follow-up. We did not find a statistical difference between
these two groups.
Analysis
To establish the convergent and divergent validity of the constructs and to
examine the statistical significance of the proposed relationships, we used PLS. PLS has
advantages over traditional regression-based analysis and covariance-based structural
models because it has minimal requirements for sample size and makes few normality
assumptions (Chin 1998). Our analysis involved two stages. We first examined the
convergent and discriminant validity of the constructs through the measurement model
and then examined the full set of structural relationships.
Measurement Model
Descriptive statistics for the constructs are shown in Table 1.1. Construct validity
analysis with PLS was completed in accordance with the recommendations of Gefen and
- 31 -
Straub (2005). Convergent validity accesses the degree to which the item measures
represent a single construct. Outer model loadings greater than 0.70 are considered to
indicate adequate convergent validity (Fornell and Larcker 1981). As shown in Table
1.4, outer model loadings for each item were greater than 0.7, with most greater than 0.8.
Additional measures of convergent validity shown in Table 1.1 include Cronbach's alpha
and the reliability coefficient (P
c
), each further supporting the convergent validity of the
item measures.
Discriminant validity assesses the degree to which item measures represent
unique constructs. Using PLS, discriminant validity is assessed through two criteria
(Chin 1998; Gefen and Straub 2005): (1) cross loadings for the item-factor correlation
table should be small and (2) the square root of the average variance extracted should be
larger than the inter-construct correlations. As is shown by the item-factor correlations in
Table 1.2 and the correlations matrix in Table 1.3, the measures show a high level of
discriminant validity. In sum, the results of the measurement model display an adequate
level of convergent and discriminant validity.
Structural Model
In the PLS structural model, path coefficients can be interpreted in the same way
as beta coefficients for regression analysis. As SCO, IT capabilities, and assimilation
each included second order constructs, the overall structural model was completed using
the factor scores from the confirmatory factor analysis, and all first order factors were
modeled as reflective. SCO was measured as a formative construct while IT capabilities
and assimilation were each measured with reflective indicators. As mentioned in the
theory, SCO can originate in any of the three strategic orientations, leading to a
- 32 -
conceptualization that is formative in nature. Dimensions of IT capabilities and
assimilation, on the other hand, are expected to capture underlying qualities of the
organization and are thus modeled as reflective.
All path coefficients and significance levels are shown in Figure 1.3. Strategic
change orientation was highly related to IT capabilities (48% of variance explained),
climate for IT use (47% of variance explained), and mindfulness of IT adoption (51% of
variance explained). IT capabilities, climate, and mindfulness of IT adoption were
slightly more weakly related to assimilation, jointly explaining nearly 47% of the
variance in IT assimilation. IT assimilation was more strongly related to process
performance (34% of variance explained) than financial performance (31% of variance
explained), though both relationships were significant. The relationship between process
performance and financial performance was in the hypothesized direction but not
significant. Controls for sales vs. service were significant for the prediction of
assimilation but not for performance. Overall, with the exception of H6 all hypotheses
were supported.
1.6 DISCUSSION
A great deal of research has established the positive link between IT investment
and firm performance (for a review, see Kohli and Devaraj 2003). However, the
mechanisms through which value gets created and the organizational characteristics
which enable some organizations to obtain more value than others are still topics of open
interest for researchers and practitioners alike. In this work, we have integrated research
from multiple theoretical foundations as a way to understand how organizations
effectively respond to an IT-enabled transformation. Within a general theoretical
- 33 -
framework linking strategic orientation, change enablers, assimilation, and performance,
we are able to identify theoretical mechanisms through which strategic orientation can
have a performance impact. In doing so, we also identify and empirically measure
organizational traits which improve the assimilation and resulting benefits from a given
technology investment.
Strategic change orientation has provided a useful construct through which to
understand how strategic characteristics of the organization may influence performance
during an IT-enabled transformation. Technological opportunism, market orientation,
and entrepreneurial orientations are highly related constructs that capture aspects of how
organizations respond to change. Though organizations may respond for different
reasons when faced with a competitive challenge, jointly understanding those
characteristics which provide the underlying motivation to change is important to both
manage change and understand the relevant benefits and challenges.
Implications for Theory
In addition to providing a look into those organizational characteristics which
promote performance during an IT-enabled transformation, this work may also provide
an integrated way of understanding the assimilation of different technologies within
organizations. This can also be understood as an organizational-level model of IT use.
The likely progression of an organizational-level model of IT use can be understood
through the extensive work on technology adoption and use at the individual level. The
technology acceptance mode (TAM) was integrated with social aspects of the theory of
reasoned action (TRA), the performance aspects of social cognitive theory (SCT), and
other contributing research to eventually emerge as the Unified Theory of Acceptance
- 34 -
and Use of Technology (UTAUT, Venkatesh et al. 2003). Similarly, a unified model
explaining why organizations use IT—i.e., an organizational level model of use—will
likely incorporate many of the constructs identified here. In sum, we argue that this
research provides an important extension to the understanding of IT use as an
organizational-level construct.
Implications for Practice
A key implication for practice is that organizations may influence the benefits
they obtain from IT investments as a result of their strategic orientation. This could have
both positive and negative feedback loops which may accentuate the effects on the
organization. Organizations with high levels of strategic change orientation may obtain
benefits which further support the ability to both use technology and to change
organizational practices. Similarly, organizations with low levels of strategic change
orientation may have little success with the assimilation of technology, further pushing
them towards both the avoidance of technology and change. In addition, this research
stresses the importance of establishing both technology use and automation as part of any
IT implementation.
Limitations
Prior to concluding, the limitations of the work must be acknowledged. External
validity can be assessed by considering the context in which the research took place. In
this research, we surveyed managers within a single industry—auto retailing. One
concern is that a single industry study may result in theory which is not generalizable to
other industries and contexts. While the fact that this work incorporated data from two
business units (sales and service) helps to mitigate this concern, future work should
- 35 -
examine the identified relationships within additional industries and technologies to
access to what extent the findings are generalizable.
An additional limitation of the work is that independent and dependant measures
were each gathered through survey techniques, thereby introducing the possibility of
common method bias. While a Harmon-single factor test for common method bias
indicated that no more than 34% of the variance could be explained by any one factor,
consistent with other works utilizing surveys, future work should assess to what extent
the model adequately predicts objective financial performance measures.
A third potential source of bias is related to the use of a single key informant to
obtain information about the organization. While key informants are frequently used
within the organizational literature, there is a great potential of measurement error when
organizational properties are identified by only one person. Individuals may answer
questions in which they have limited knowledge of the specific domain, leading to
assessments which do not specifically relate to the dependant variables.
A fourth potential source of bias is related to the use of the customers from a
single organization—i.e., NetAuto. While this enabled us to examine the role of
organization factors in a situation in which the technology is held constant, this
introduces a selection bias. If there were specific organizational traits which caused the
organization to adopt the CRM system in the first place, it may be that this research could
under or over estimate the magnitude of the relationships for the industry in general.
However, in conversations which numbers dealerships we found that the there were no
reasons to believe that the dealers surveyed differed systematically from the overall
population of dealerships.
- 36 -
1.7 SUMMARY
Information technology can have an important influence on competition in many
industries, and effectively responding to IT-enabled transformations can be critical to
firm performance. By understanding better how organizations can respond to IT-enabled
transformations, we can provide both practical recommendations to managers and
technology vendors while improving the value organizations derive from IT. This
research integrates theoretical perspectives which capture both the willingness and the
ability of the organization to respond to change.
- 37 -
1.8 FIGURES
FIGURE 1.1 - CONCEPTUAL FRAMEWORK
FIGURE 1.2 - RESEARCH MODEL
- 38 -
1.8.3 FIGURE 1.3 - PLS RESULTS
Note: 1. *** significant at 0.001, **significant at 0.01, *significant at 0.05.
- 39 -
1.9 TABLES
TABLE 1.1 - DESCRIPTIVE STATISTICS FOR SURVEY MEASURES
Variable Mean Std Chronbach P
c
Alpha
SCO: Technological Opportunism (TO) 4.976 1.485 0.930 0.944
SCO: Market Orientation (MO) 6.215 0.941 0.824 0.876
SCO: Entrepreneurial Orientation (EO) 5.125 1.307 0.801 0.872
ITCAP: IT Management Capabilities
(ITMAN) 5.186 1.342 0.907 0.934
ITCAP: IT Infrastructure Capabilities
(ITIN) 5.567 1.324 0.943 0.958
Climate for IT Use (CLIM) 5.077 1.496 0.915 0.940
Mindfulness of IT Adoption (MIND) 5.622 1.205 0.947 0.956
Assimilation: Automation (AUTO) 3.394 1.187 0.846 0.897
Assimilation: Use (USE) 5.269 1.724 0.891 0.926
Process Performance (PP) 5.347 1.151 0.828 0.970
Financial Performance Control (FPC) 4.631 1.362 0.936 0.956
Financial Performance (FP) 5.308 1.289 0.910 0.901
Note: P
c
= Composite Reliability = (Eì
i
)
2
/[(Eì )
2
+E(1-ì
2
)] where ì is the factor loading.
i i i
- 40 -
1.9.2 TABLE 1.2 - CORRELATION MATRIX
Variable 1 2 3 4 5 6 7 8 9 10 11 12
1 SCO: TO 0.837
2 SCO: MO 0.518 0.721
3 SCO: EO 0.621 0.519 0.717
4 ITCAP: ITMAN 0.607 0.462 0.505 0.818
5 ITCAP: ITFRA 0.372 0.389 0.435 0.367 0.869
6 CLIM 0.578 0.570 0.544 0.497 0.621 0.830
7 MIND 0.634 0.580 0.497 0.616 0.484 0.654 0.799
8 ASSIM: AUTO 0.371 0.369 0.278 0.383 0.321 0.445 0.386 0.752
9 ASSIM: USE 0.511 0.471 0.392 0.529 0.460 0.562 0.578 0.610 0.802
10 FPC2 0.179 0.142 0.154 0.207 0.028 0.041 0.167 0.091 0.118 0.944
11 FP2 0.389 0.290 0.298 0.387 0.226 0.255 0.298 0.201 0.353 0.469 0.922
12 PP 0.363 0.358 0.350 0.543 0.378 0.500 0.376 0.497 0.539 0.186 0.307 0.803
Note: The bold values along the diagonal are the square root of the AVE (Average Variance Extracted). The off-diagonal variables
are the correlations among the constructs. For discriminant validity, the square root of the AVE should be larger than the correlations
(Gefen and Straub 2005).
- 41 -
TABLE 1.3 - ITEM CORRELATION TABLE
1 2 3 4 5 6 7 8 9 10 11 12 TO1 0.92 0.49 0.55 0.53
0.35 0.54 0.63 0.37 0.48 0.21 0.35 0.31 TO2 0.90 0.45 0.54 0.56
0.32 0.50 0.63 0.33 0.46 0.14 0.29 0.33 TO3 0.87 0.48 0.58 0.49
0.33 0.51 0.53 0.30 0.50 0.11 0.39 0.27 TO4 0.91 0.44 0.56 0.60
0.34 0.53 0.49 0.33 0.40 0.18 0.36 0.39 MO1 0.44 0.80 0.36
0.45 0.37 0.46 0.50 0.24 0.46 0.14 0.29 0.25 MO2 0.44 0.82
0.55 0.31 0.46 0.59 0.53 0.43 0.46 0.07 0.21 0.38 MO3 0.40
0.77 0.27 0.40 0.09 0.31 0.42 0.24 0.34 0.21 0.30 0.23 MO4
0.37 0.80 0.45 0.33 0.28 0.44 0.39 0.25 0.23 0.04 0.14 0.27
EO1 0.53 0.47 0.84 0.46 0.35 0.41 0.44 0.24 0.29 0.16 0.28 0.32
EO2 0.60 0.42 0.83 0.52 0.42 0.48 0.49 0.21 0.32 0.11 0.22 0.33
EO3 0.32 0.37 0.74 0.23 0.34 0.40 0.26 0.17 0.31 0.13 0.20 0.16
EO4 0.48 0.38 0.76 0.36 0.27 0.44 0.35 0.27 0.33 0.08 0.25 0.28
ITMAN1 0.64 0.38 0.51 0.87 0.26 0.41 0.55 0.33 0.45 0.21 0.40 0.40
ITMAN2 0.48 0.34 0.40 0.87 0.30 0.44 0.46 0.29 0.45 0.20 0.27 0.54
ITMAN3 0.51 0.42 0.45 0.93 0.40 0.43 0.58 0.32 0.44 0.18 0.36 0.47
ITMAN4 0.53 0.49 0.44 0.86 0.32 0.47 0.58 0.41 0.54 0.15 0.33 0.50
INFRA1 0.38 0.34 0.48 0.37 0.91 0.60 0.48 0.25 0.41 0.08 0.22 0.34
INFRA2 0.34 0.37 0.33 0.31 0.90 0.54 0.44 0.30 0.41 -0.01 0.16 0.36
INFRA3 0.31 0.40 0.36 0.37 0.93 0.59 0.44 0.35 0.45 0.02 0.22 0.37
INFRA4 0.34 0.33 0.43 0.30 0.94 0.56 0.43 0.28 0.43 0.01 0.24 0.32
CLIM1 0.54 0.54 0.42 0.44 0.53 0.91 0.67 0.43 0.49 -0.03 0.20 0.42
CLIM2 0.46 0.43 0.58 0.37 0.52 0.84 0.45 0.33 0.44 0.07 0.20 0.43
CLIM3 0.55 0.56 0.46 0.48 0.59 0.93 0.69 0.42 0.54 0.02 0.23 0.45
CLIM4 0.50 0.50 0.49 0.48 0.57 0.88 0.52 0.40 0.54 0.09 0.28 0.48
MIND1 0.48 0.51 0.28 0.57 0.34 0.54 0.85 0.36 0.56 0.18 0.25 0.40
MIND2 0.55 0.48 0.34 0.57 0.34 0.56 0.87 0.37 0.56 0.18 0.22 0.37
MIND3 0.59 0.51 0.51 0.52 0.40 0.56 0.88 0.34 0.45 0.12 0.29 0.30
MIND4 0.60 0.57 0.49 0.53 0.46 0.64 0.93 0.39 0.56 0.12 0.26 0.33
MIND5 0.55 0.43 0.40 0.56 0.43 0.52 0.87 0.36 0.53 0.19 0.25 0.30
MIND6 0.58 0.55 0.54 0.57 0.48 0.60 0.86 0.27 0.44 0.22 0.32 0.33
MIND7 0.49 0.47 0.47 0.43 0.50 0.53 0.81 0.25 0.40 -0.01 0.21 0.27
AUTO1 0.30 0.24 0.18 0.25 0.14 0.24 0.22 0.78 0.46 0.16 0.14 0.38
AUTO2 0.30 0.40 0.24 0.30 0.33 0.46 0.36 0.81 0.50 0.06 0.20 0.41
AUTO3 0.29 0.30 0.19 0.30 0.21 0.34 0.32 0.88 0.51 0.03 0.11 0.42
AUTO4 0.34 0.28 0.32 0.41 0.37 0.43 0.37 0.84 0.55 0.06 0.22 0.43
USE1 0.42 0.30 0.28 0.40 0.38 0.48 0.42 0.47 0.84 0.08 0.21 0.43
USE2 0.45 0.46 0.29 0.46 0.37 0.52 0.54 0.56 0.92 0.10 0.33 0.49
USE3 0.54 0.41 0.37 0.52 0.47 0.52 0.55 0.58 0.91 0.09 0.37 0.52
USE4 0.37 0.46 0.43 0.45 0.38 0.43 0.49 0.51 0.81 0.14 0.31 0.43
FPC1 0.17 0.11 0.10 0.14 0.00 0.04 0.15 0.06 0.07 0.96 0.40 0.13
FPC2 0.17 0.16 0.19 0.25 0.05 0.04 0.17 0.11 0.15 0.98 0.50 0.22
FP1 0.38 0.27 0.25 0.35 0.20 0.23 0.28 0.16 0.29 0.49 0.96 0.28
FP2 0.37 0.29 0.32 0.39 0.23 0.26 0.29 0.22 0.39 0.41 0.96 0.31
PP1 0.30 0.28 0.31 0.50 0.32 0.41 0.32 0.38 0.47 0.14 0.26 0.90
PP2 0.36 0.35 0.31 0.53 0.36 0.48 0.38 0.51 0.56 0.20 0.36 0.94
PP3 0.29 0.30 0.31 0.36 0.30 0.42 0.26 0.38 0.33 0.13 0.13 0.76
Note: Factor numbers refer to the appropriate factors as indicated in bold.
- 42 -
TABLE 1.4 - PLS OUTER MODEL LOADINGS
PLS Outer
Item
TO1
TO2
TO3
TO4
MO1
MO2
MO3
MO4
EO1
EO2
EO3
EO4
ITMAN1
ITMAN2
ITMAN3
ITMAN4
INFRA1
INFRA2
INFRA3
INFRA4
CLIM1
CLIM2
CLIM3
CLIM4
MIND1
MIND2
MIND3
MIND4
MIND5
MIND6
MIND7
AUTO1
AUTO2
AUTO3
AUTO4
USE1
USE2
USE3
USE4
FPC1
FPC2 FP1
FP2 PP1
PP2 PP3
Weight
0.283
0.276
0.277
0.276
0.311
0.350
0.280
0.308
0.342
0.350
0.260
0.301
0.263
0.275
0.313
0.280
0.276
0.261
0.280
0.267
0.282
0.257
0.296
0.284
0.157
0.164
0.169
0.185
0.162
0.166
0.146
0.279
0.298
0.313
0.318
0.269
0.302
0.305
0.271
0.462
0.568
0.532
0.513
0.374
0.482
0.280
Model
Loading
0.915
0.905
0.871
0.906
0.800
0.820
0.775
0.802
0.842
0.831
0.740
0.760
0.866
0.874
0.932
0.863
0.914
0.899
0.935
0.940
0.912
0.844
0.932
0.882
0.846
0.874
0.879
0.931
0.874
0.863
0.811
0.781
0.809
0.881
0.838
0.843
0.917
0.907
0.813
0.964
0.976
0.958
0.955
0.897
0.939
0.758
Note: All loadings significant at p< 0.001.
- 43 -
CHAPTER 2: PROFITING FROM THE INTERNET CHANNEL: THE
COMPLEMENTARITY OF ELECTRONIC COMMERCE CAPABILITIES AND
BUSINESS PROCESS CHANGE
2.1 ABSTRACT
The emergence of the Internet channel presents opportunities and challenges for
traditional retailers. As a result, understanding those factors that enable retailers to
benefit from the Internet channel can aid in management decision making as well as
contribute to the stream of research addressing electronic commerce. In this essay, we
develop and empirically test a model which examines the complementarities between
electronic commerce capabilities and business process change in influencing Internet
channel performance. A survey of over 639 organizations in the retail auto industry
enables us to empirically test the proposed model of Internet channel performance. We
find that electronic commerce capabilities are made more effective when accompanied by
business process change. These complementarities, however, are accompanied by a
direct negative relationship between business process change and Internet channel
performance that makes the net effect positive only for organizations with high levels of
EC capabilities. By identifying the benefits and hazards of business process change, this
research provides practical advice to practitioners while integrating theory from the
resource based view (RBV) of the firm and business process reengineering (BPR) under a
common complementarities framework.
- 44 -
2.2 INTRODUCTION
Information technology and the Internet have changed the way that many retail
organizations interact with consumers, providing both an efficient transaction mechanism
for retailers (Smith et al. 2000) and an efficient price comparison mechanism for
consumers (Bakos 1997). While the Internet channel typically offers retailers lower
prices compared to traditional offline channels (Brynjolfsson and Smith 2000), efficiency
gains from electronic communications offer the promise of lower costs (Barua et al.
1995), thereby creating the opportunity for retailers to provide increased value to
consumers. In order to benefit from the Internet channel, however, retailers must develop
the capabilities necessary to interact via the Web (Amit and Zott 2001). As a result, the
value proposition for selling online may be unclear for some retailers. Retailers must
justify the expenses which result from channel cannibalization (Viswanathan 2005), in
which individuals who would have purchased through the higher-price offline channel
select the lower-price online channel, and the expenses necessary for building the
capabilities to utilize the Internet channel. Therefore, understanding the factors that
influence a retailer's ability to create value through the Internet channel is an area of
importance for both practitioners and researchers alike.
As the Internet channel represents an important way for organizations to interact
with consumers, it has not surprisingly generated a great deal of interest from researchers.
A growing stream of research has addressed consumers' use of the Internet, examining
consumer web satisfaction (e.g., McKinney et al. 2002), price paid (e.g., Brynjolfsson
and Smith 2000), infomediary use (e.g., Scott-Morton et al. 2001), value (e.g., Keeney
1999), and welfare impacts (e.g., Brynjolfsson et al. 2003). Similarly, a second stream of
- 45 -
research has addressed organizations' use of the Internet, examining infomediary use
(e.g., Chen et al. 2002), net-enablement (e.g., Wheeler 2002) and net-enabled business
transformation (e.g., Barua et al. 2004; Straub and Watson 2001). While empirical
examinations of outcomes from consumers' use of the Internet—i.e., price paid—have
been common (e.g., Brynjolfsson and Smith 2000; Scott-Morton et al. 2001), empirical
examinations of outcomes from organizations' use of the Internet—i.e., Internet channel
performance—are lacking. One possible explanation for this may be that overall
financial metrics do not typically capture outcomes by channel. As a result, few studies
have specifically examined the drivers of Internet channel performance outcomes.
This paper seeks to fill this gap in the literature by examining the performance
outcomes of business processes through which retailers interact with the Internet channel.
Drawing from the resource-based view (RBV) of the firm (for a review, see Wade and
Hulland 2004) and framing the research within the IT value stream (for a review, see
Melville et al. 2004), we examine relationships among electronic commerce (EC)
capabilities, business process change, and Internet channel performance. The RBV has
given researchers a theoretical foundation to understand how organizations interact with
the Internet channel (Amit and Zott 2001; Barua et al. 2004; Zhu and Kraemer 2002) and
researchers have argued that capabilities are important to the generation of value from a
production economics perspective (Bharadwaj 2000; Santhanam and Hartono 2003).
While EC capabilities have been found to be an important driver of firm performance,
little research has identified ways that existing EC capabilities can be made more
valuable through the existence of other firm resources (for an exception, see Zhu 2004).
- 46 -
We test our model in the context of the auto retailing industry. Auto retailing
represents the biggest retail sector in the US, with annual sales estimated at around a
trillion dollars. In addition to being an important part of the US economy by itself, recent
research and commentaries have pointed to the important contribution of industry-level
studies. Findings by Hawawini et al (2003) reveal that industry-level effects may
overshadow firm-level effects when examining the influence of radical IT innovation
such as the Internet on organizational outcomes, suggesting that within-industry studies
may be better able to examine the role of specific organizational characteristics in
influencing organizational performance. Industry-level studies are also more likely to
yield actionable outcomes managers will believe, increasing the relevance and impact of
IS research (Agarwal and Lucas 2005; Chiasson and Davidson 2006).
The retail auto industry is also a compelling example of an industry that has
undergone an IT-enabled transformation, as the emergence of online infomediaries and
the web have lowered search costs and fundamentally altered the way that firms compete
(Chen et al. 2002; Zettelmeyer 2000). Unlike most retail sectors, there is no national
brand and the ten largest dealerships enjoy less than 6% of overall sales, resulting in
significant variance in how organizations deal with the effects of industry transformation.
Thus, an industry-level study of detailed organizational characteristics may inform
practitioners of how to navigate the difficult road of technology transformation, making
this research relevant, useful, and impactful (Agarwal and Lucas 2005). Our analysis of a
survey of 639 auto retailers conducted by a leading market research organization includes
both detailed measures of retailer characteristics and objective measures of Internet
channel performance.
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Through this research we make three main contributions. First, we provide
further support for the important role of electronic commerce capabilities in influencing
Internet channel performance, demonstrating the dimensions of EC capabilities which are
most likely to offer sustained benefits to retailers. Second, by identifying the
complementarities between electronic commerce capabilities and business process
change, we are able to obtain a better understanding of the how organizations can make
capabilities more effective in generating value for the organization. Third, by identifying
a negative relationship between business process change and Internet channel
performance, we demonstrate that change efforts can have a negative consequence when
not paired with the development of associated capabilities. These contributions add to
the growing literature utilizing complementarities as a theoretical lens through which to
understand the role of IT-related capabilities in predicting important firm outcomes.
The rest of the paper is organized as follows. In section one we review the
relevant research related to the RBV and IT value—two streams that have been critical to
the understanding of the relationship between technology and firm performance.
Following this, we discuss the model of the retailers' interaction with the online channel,
making arguments for the determinants of Internet channel performance. We then review
the sample and analysis technique. We finally examine the implications of this research
for retailers' use of the Internet.
2.3 LITERATURE REVIEW
In this section we review significant findings for the RBV and explore how it has
been used in the IT value literature.
The Resource Based View of the Firm
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Originating in the field of strategic management (Barney 1986; 1991; Penrose
1959; Wernerfelt 1984), the resource-based view (RBV) of the firm has become a critical
lens through which researchers have examined the drivers of firm performance and
competitive advantage. The RBV suggests that firms compete on the basis of VRIN
resources—those that are valuable, rare, inimitable, and non-substitutable (Barney 1991;
Connor 1991). Possession of VRIN resources can lead to a temporary competitive
advantage and positively influence performance, as shown through empirical studies in a
variety of domains (Bharadwaj 2000; Dutta et al. 1999, 2005; Srivastava et al. 2001).
The RBV provides a useful framework for IS researchers to understand the
strategic value of IT and its influence on firm performance. Using the RBV, researchers
can characterize how specific technology-related capabilities influence firm outcomes,
adding greater detail to the value creation process. In addition, as the RBV has been
applied to numerous areas of organizational research, it provides a vehicle through which
to compare the magnitude of the effects from IT to the effects from other organizational
phenomena and a theoretical foundation through which to conduct cross-disciplinary
research (Wade and Hulland 2004).
Resources and Capability Identification
The identification of resources and capabilities is an important step towards the
use of the RBV to understand firm behavior and outcomes. Grant (1991) distinguished
between resources and capabilities, defining resources as tangible assets of the firm and
capabilities as a bundle of resources that has the potential for action. Wade and Hulland
(2004) adopt the definition of resources as assets and capabilities which are useful in
detecting or acting on threats or opportunities in the competitive environment (Sanchez et
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al. 1996). They further define assets as those resources associated with the development,
manufacture, or sale of products and capabilities as repeatable organizational actions
(Wade and Hulland 2004).
The identification of what constitutes a specific IT-related resource has created
some challenges for IT researchers in particular. Several studies have argued that IT
resources can lead to a strategic advantage only when they are combined with other
organizational resources (Clemons and Row 1991; Ross et al. 1996). In examining
specific types of IT resources, Mata et al. (1995) argued that of the variety of resources
related to IT, only IT management skills can lead to a lasting strategic competitive
advantage. Multidimensional typologies of core IT capabilities have been developed by
several researchers. Feeny and Willcocks (1998) identified nine core IT-related
capabilities including business systems thinking, relationship building, architecture
planning, leadership, informed buying, contract facilitation, vendor development,
contract monitoring, and making technology work. Bharadwaj et al. (1998) suggested
and empirically validated six dimensions of IT capabilities including IT business
partnerships, external IT linkages, business IT strategic thinking, IT business process
integration, IT management, and IT infrastructure. In their review of the use of the RBV
in IS research, Wade and Hulland (2004) integrated this prior research and identified
three main categories (outside-in, spanning, and inside-out) of resources across a total of
eight dimensions including manage external resources, market responsiveness, IT-
business partnerships, IT planning and change management, IT infrastructure, IT
technical skills, IT development, and cost effective IT operations. Outside-in resources
influence the organization's outward-facing external relationship management and
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market responsiveness. Spanning resources include IT-business partnerships and
planning/change management. Inside-out resources include IT technical skills, IT
development, and cost-effective IT operations. A summary of the dimensions found in
competing frameworks is found in Table 2.1.
Resource and Capability Measurement
For empirical studies investigating IT and utilizing the RBV, difficulties in
measurement have caused theoretical questions of what constitutes resources and
capabilities to be addressed directly alongside empirical questions of how to measure
them. Several researchers have utilized overall reputation as indicated by an Information
Week list of IT innovation leaders by industry as a measure of IT capabilities (Bharadwaj
2000; Santhanam and Hartono 2003). This type of measurement has the advantage of
being externally developed by a panel of industry experts. However, the use of a binary
variable to represent IT capabilities does not allow for the analysis of incremental
capabilities, is not consistent with the multidimensional capabilities specified by the RBV
(Santhanam and Hartono 2003), and thus may provide limited insight into the specific
mechanisms through which IT capabilities may influence organizational performance.
As an alternative, numerous studies have measured responses from key
informants within organizations through a survey methodology. For example, Armstrong
and Sambamurthy (1999) found that CIO capabilities and IT infrastructure each lead to
IT assimilation. Ray et al. (2005) found that managerial IT knowledge and service
climate positively influence customer outcomes. An advantage of this technique is that it
enables the measurement of constructs not accessible through objective measures. While
the key informant survey methodology has been extensively used throughout
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organizational research, objections to this methodology are that measures typically reflect
the opinion of one individual and ignore the possibility that IT-related capabilities may
occur throughout various parts of the firm.
An alternative to survey measures is the use of metrics related to IT
functionalities, or specific system features/functions that organizations develop.
Although IT related capabilities are the constructs of theoretical interest, IT
functionalities are argued to be enabled by and thus reflective of underlying capabilities.
This approach has been used to measure capabilities related to electronic commerce (Zhu
2004; Zhu and Kraemer 2002) and net-enabled business processes (Barua et al. 2004).
The use of metrics or IT functionalities has the advantage that they are objective
measures which typically can be determined either through survey methodology or
through direct observation—i.e., from examining public press releases and websites.
Unlike typical survey measures, however, metrics have the disadvantage that they must
be updated as IT functionalities diffuse through an entire population of organizations and
new functionalities emerge.
Complementarities
In addition to the direct positive effect of IT resources and capabilities, the
complementarities among resources have also been of particular interest. Clemons and
Row (1991) argued that IT resources provide more value when paired with organizational
resources. In the context of electronic commerce, Zhu (2004) found that EC capabilities
were more valuable when combined with internal infrastructure resources. Similarly,
Barua et al. (2004) found that customer (supplier) focused capabilities were more
valuable when customers (suppliers) had a high degree of readiness for net-enabled
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business processes. Overall, by examining ways in which the overall efficiencies of
business processes are improved through synergistic capabilities, the complementarities
lens provides an important framework to understand organizational performance.
Dynamic Capabilities
Due to the changing nature of e-commerce technologies, the organizations' ability
to develop systems related to net-enablement have been argued to be a dynamic
capability (Wheeler 2002; Zhu and Kraemer 2002). Dynamic capabilities refer to a
firm's ability to adapt and change in high velocity environments that require new
capabilities on an ongoing basis (Teece et al. 1997). A firm must continually adapt e-
commerce strategies, business practices, and technologies, and as a result the
measurement of e-commerce capabilities actually reflects the firm's ability to change
with the environment. Wheeler (2002) used the dynamic capabilities perspective to
develop a process model of the specific mechanisms through which firms develop IT
capabilities and learn from both actions and the environment. He suggests that
organizations develop IT capabilities by a process of choosing technologies, matching
technologies with opportunities, and acting on those opportunities. Zahra and George
(2002) also point out the importance of dynamic capabilities in examining how IT relates
to organizations.
The RBV and IT Value
The resource-based view has frequently been used as a foundation for
understanding the mechanisms through which IT creates value (Bharadwaj 2000;
Santhanam and Hartono 2003). A recent review article by Melville et al (2004)
integrates prior research from each of these streams into a framework indicating how IT
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creates value in organizations. This framework conceptualizes the process through which
IT generates value as one in which IT resources and complementary organizational
resources interact to influence the performance of business processes. The impact on
business process performance, in turn, influences organizational performance. This
entire process is affected by contextual factors such as the country and industry in which
the business process takes place and the other trading partners that may be involved with
the business process. Overall, this framework provides an effective way to begin to
understand how organizations create value through interacting with the online channel.
In sum, the RBV has provided a rich foundation through which to understand the
strategic importance of IT and how IT can influence firm outcomes. It is useful both as a
way to theoretically understand those characteristics which enable firms to obtain
sustained competitive advantage and empirically examine those characteristics which
provide firms with value.
2.4 RESEARCH MODEL AND HYPOTHESES
In developing the model, shown in Figure 2.1, we adopt a complementarities-
based theoretical framework to understand how electronic commerce (EC) capabilities
and business process change relate to organizational performance. Overall, the model
suggests that both the EC capabilities and business process change are important for
retailers to derive value from the Internet channel.
Internet Channel Performance
Understanding the ways through which information technologies influence the
performance of organizations is one the key themes of IS research. While numerous
organizational studies have identified those factors influencing overall business
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performance (e.g., Brynjolfsson and Hitt 1996; Zhu and Kraemer 2002; Zhu and Kraemer
2005), there are several advantages to studying the performance of the Internet channel—
i.e., Internet channel performance—separately from overall organizational performance.
In an industry such as auto retailing, many of the existing business processes have been
significantly altered by the presence of the Internet. Measuring Internet channel
performance directly gives us a way to understand and examine empirically those factors
which enable organizations to be successful, as they incorporate a new model of sales
provided by the Internet. Measuring overall organizational performance, while
important, may not reveal the detailed process level factors which enable organizations to
succeed in utilizing the Internet channel. This incorporation of business process specific
outcomes has been a key theme within the IT value literature (e.g., Mukhodaphyay and
Kekre 2002; Mukhopadhyay and Kekre 1995). In essence, examining process level
performance enables us to determine how a given organization has been able to utilize the
Internet channel as a way to grow overall sales.
EC Capabilities
We argue that EC capabilities will lead to increased Internet channel performance.
EC capabilities refer directly to the ability of the organization to implement and manage
systems supporting electronic commerce, a more specific version of overall IT
capabilities. As outlined earlier, a rich literature has linked IT-related capabilities with
firm performance (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer
2002). IT-related capabilities benefit firms by making IT investment dollars more
effective though lowered IT costs (Feeny and Willcocks 1998; Ross et al. 1996), better
management of IT opportunities (Bharadwaj 2000; Bharadwaj et al. 1998; Mata et al.
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1995), or increased impact of IT systems (Devaraj and Kohli 2003). Similarly, EC
capabilities are expected to enable retailers to effectively implement EC systems, take
advantage of EC partnerships, and make sales through the Internet channel. Thus, those
retailers with high levels of EC capabilities would be better able to attract consumer leads
and convert those leads to sales than those with low levels of EC capabilities.
Prior research has identified the multidimensional nature of EC capabilities. As
dimensions of EC capabilities are those which enable organizations to gain competitive
advantage within a particular context, the dimensions need to be adapted to the specific
context being evaluated. When examining manufacturing firms, Zhu and Kraemer
(2002) identified four dimensions of EC capabilities: (1) information; (2) transaction; (3)
interaction and customization; and (4) supplier connection. In the context of auto
retailing, we conceptualize EC capabilities across three dimensions: (1) informational, (2)
transactional, and (3) relational capabilities. Informational capabilities provide useful
information about products and services. Transactional capabilities enable online
purchases. Within this population and time period, auto retailers had little ability to
provide customers with the same types of customized options as the manufacturing sector
examined by Zhu and Kramer, making the interaction and customization dimension less
applicable. In addition, the use of online infomediaries represents a critical way in which
retailers interrelate with their customers online that is not adequately captured by the
supplier connection dimension. In this context, the relational capabilities dimension
captures both the integration with suppliers (original equipment manufacturers) and
online infomediaries.
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Informational, transactional, and relational capabilities are each expected to
influence performance through different mechanisms. Informational capabilities enable
organizations to provide customers with the information they need to make decisions,
thus increasing the likelihood they will purchase through the Internet channel.
Transactional capabilities enable customers to easily obtain price quotes or make offers
on vehicles, making the overall process of buying a vehicle or related product easier.
Relational capabilities provide the dealership with leads to customers from trusted third
party firms, such as infomediaries and manufacturers. This provides the dealer with
access to customers that he or she may not have had access to otherwise. In addition,
research has shown that the during the process of linking from a third party firm to a
organization, institutional trust from the known third party is transferred to the unknown
dealership, making the individual more likely to purchase (Stewart 2003). For these
reasons, and the general benefits of EC capabilities discussed earlier, we expect:
H1A: Informational capabilities are positively associated with Internet channel
performance.
H1B: Transactional capabilities are positively associated with Internet channel
performance.
H1C: Relational capabilities are positively associated with Internet channel
performance.
Zhu and Kraemer (2002) viewed all dimensions of EC capabilities equally,
arguing that all dimensions of EC capabilities were reflective of an underlying
construct—i.e, EC capabilities. However, there is reason to believe that different
dimensions of EC capabilities may have different effects on performance, as each
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dimension of EC capabilities may differ in how easily specific advantages can be
imitated and competed away. Technologies emerge and are adopted by a population of
organizations, and as all organizations adopt a technology the competitive benefits
transferred to the organization are reduced or eliminated. As a result, those EC
capabilities which cannot be easily competed away would be expected to provide the
most value to organizations.
We argue that performance benefits from relational capabilities are less likely to
be competed away and are therefore more likely to provide lasting value to retailers than
informational or transactional capabilities. The development of informational and
transactional capabilities may require the retailer to engage outside expertise or develop
internal capabilities to actively manage the systems involved. These capabilities,
however, offer the retailer no degree of exclusivity with respect to competitors. Even
highly advanced web functionality can be relatively easily replicated or purchased from
technology vendors. Thus, over the long run advantages related to informational and
transactional capabilities are likely to be competed away. Relational capabilities, in
contrast, typically involve exclusive relationships with an infomediary or manufacturer
for a geographical area. As demonstrated analytically by Chen et al. (2002), both the
informediaries and the dealers themselves benefit from the exclusive nature of these
agreements. This is expected to result in relational capabilities having a greater impact
on performance than informational or transactional capabilities. Thus, we hypothesize:
H2: The relationship between relational capabilities and performance is stronger
than the relationship between informational or transactional capabilities and
performance.
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Business Process Change
Prior research has highlighted the importance of the organizational changes which
are often associated with technology investments (e.g., Broadbent and Weill 1999;
Grover et al. 1998; Kettinger and Teng 1997). This research, however, has placed much
of the emphasis on the business processes of enacting change (e.g., Datta 1998; El Sawy
2001; Kettinger and Teng 1997; Nissen 1998) rather than the outcome of the change
itself. While procedures for business process change are quite important operationally,
they do not provide information for decision makers regarding the likely benefits of
business process change or how business process changes may influence the value
derived from technology investments.
In this research, we argue that business process change makes EC capabilities
more effective in influencing the performance of the Internet channel. This relationship
is similar to that between EC capabilities and IT infrastructure examined by Zhu (2004),
who found that EC capabilities were more effective in the presence of IT infrastructure.
EC capabilities enable firms to take advantage of the Internet as a medium for the rich
exchange of information with partners (Bakos 1998; Bakos and Brynjolfsson 1993). IT
infrastructure improves the effectiveness of EC capabilities by bridging the Internet-
enabled communications to the internal systems of the organization (Zhu 2004).
Following a similar line of reasoning, complementary business process change makes EC
capabilities more effective by aligning the external EC process with the internal firm
resources. This may include providing a bridge to the human resources within the
organization or ensuring that EC investments are adequately monitored and optimized.
The development of these EC capabilities by themselves does not ensure that the
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organization will have the internal resources necessary to take advantage of them. When
organizations undergo business process change, they optimize business and workflow
processes (Barua et al. 1996; El Sawy 2001), thus ensuring that technologies are
adequately integrated into operations.
In the case of auto retailing, when dealerships become net-enabled, the Internet
may be viewed either as an external function that provides a new way of obtaining leads
or as a significantly new business process. Dealers treating the Internet as an external
function may handle the leads in the same way in which they handle normal leads—
allowing salesperson availability at the time the leads come in to determine who obtains
the leads—making little change to the overall business process. Alternately, the
dealership may redesign the business process to match the specific demands of the
Internet channel, dedicating individuals with specific goals related to interacting with and
tracking customers electronically. The choice made by the dealership of how to handle
the consumer interactions initiated via the Internet, therefore, may have an important
influence on Internet channel performance.
One reason for the importance of this type of business process change is the
increased use of email and handheld devices, as consumers now may have a lower
tolerance for delays in communication than they once did. Salespersons splitting time
between online enquires and traditional walk-in customers may be much slower in
responding to electronic communication. A dedicated group, however, would be able to
effectively manage electronic communications with a large group of potential customers.
Second, managing communications electronically may require extensive use of
communication technology and with it significantly different skills than face-to-face
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interaction. Third, in the technology adoption literature, social aspects have been found
to be among the strongest reasons for technology adoption (for a review, see Venkatesh
et al. 2003). Introducing a new technology and positively influencing perceptions of
usefulness may be difficult in a group with established work processes. Instituting a
dedicated group with the objective of handling Internet leads would lead the organization
to develop its own social climate toward usage. Without a history of work processes, the
social factors that influence the use of the technology would not have to be overcome—
likely leading to more complete adoption of the technologies. The argument that the
organizations which change business processes will be more effective is also consistent
with the suggestion by El Sawy and Bowles (1997) that firms should reexamine customer
processes that interact with the Internet. As the overall impact of complementary
business process change on performance requires the existence of identified customers
that have the ability and willingness to purchase, this suggests that business process
change will moderate the relationship between each of the three dimensions of EC
capabilities and Internet channel performance. Therefore, we hypothesize:
H3A: Complementary business process change positively moderates the
relationship between the informational capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
H3B: Complementary business process change positively moderates the
relationship between transactional capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
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H3C: Complementary business process change positively moderates the
relationship between the relational capabilities and Internet channel
performance, such that the relationship is stronger when the organization has
undergone business process change.
2.5 RESEARCH METHODOLOGY
Sampling and Data Collection
The data for this study is drawn from the ongoing work of a leading market
research organization. The organization contacted 17,160 dealerships and asked them to
participate in a 30-minute online survey detailing aspects of their use of the online
channel. As the overall population of dealerships in the US is nearly 23,000, this sample
represented nearly 75% of the retail auto industry and included nearly all medium to large
dealerships. From the initial email, 1,016 retailers completed the survey, representing a
6% response rate. The average number of vehicles sales per for the organizations
responding to the survey was 990. This was higher than the average dealership sales
reported during this timeframe, which was equal to 730 vehicles per year (NADA 2002).
As the survey was primarily targeted at organizations use of the Internet, it is possible
that larger organizations with established Internet operations may have been more likely
to complete the survey. In addition, as the survey was conducted online, smaller firms
may not have had the connectivity to be reached by the market research organization.
Among the respondents, 283 did not report profit levels per vehicle. Additional listwise
deletion of missing variables yielded 639 usable responses.. While the response rate is
low, the sample size is overall very large and represents nearly 3% of the total population
of dealerships. In addition, it is estimated that based on past experiences of the market
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research organization, approximately 20% of the contact emails were not valid.
Adjusting the number of organizations contacted downward by 20% results in an overall
response rate for valid contacts of 5%.
Measures
Informational capabilities were measured through four binary indicators of
specific information-related functionality, such as "Lists all photos on websites."
Similarly, transactional capabilities were measured through four binary indicators related
to purchasing or furthering the purchase process online, such as "Price quote request
form." Finally, relational capabilities were measured using number of total
infomediaries and manufacturers in which the dealership has established relationships to
receive customer leads, the total number of leads obtained, and the total invested in
establishing relationships.
The measure for business process change indicates whether the retailer has
established separate organizational resources to handle Internet-generated leads within
the organization. Specifically, the retailer was asked "Are leads distributed to specific
sales people who don't take regular floor traffic?" As discussed earlier, the designation
of a separate business process is an important way for organizations to indicate that the
Internet is not an external feed for leads but rather requires a new way of interacting with
customers—i.e., it represents substantial business process change.
In order to determine the overall performance of the Internet channel, managers
were asked to indicate the total number of sales originating in the Internet channel and
the average profit per sale. This question was asked iteratively, where the respondent
entered all of the sources of her Internet sales and then had the opportunity to adjust her
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response after seeing the totals. Internet channel performance was measured by total
Internet profit, which was calculated by multiplying the total Internet sales by the average
profit per Internet sale.
Several other variables were included to control for other factors which may
influence the overall Internet channel performance but where not hypothesized in the
model. We controlled for the number of luxury brands (luxury), the number of domestic
brands carried (domestic), and the number of imported brands (import) carried by the
retailer. The overall sales volume as measured by the number of sales per month (sales
volume) and the discount offered from the online channel (online discount) were also
utilized as controls.
Validity of EC Capabilities Measures
As this research involved the use of EC capabilities measures specific to retailing,
multiple tests of construct validity were completed according to established procedures.
Measures for EC capabilities were relatively highly correlated (0.20-0.40) but exhibited
adequate discriminant and convergent validity. We utilized exploratory factor analysis
using the principal components approach with Varimax rotation to access discriminant
validity. Entering each of the items for the EC capabilities yielded three factors.
Loadings for the informational, transactional, and relational capabilities were each as
expected, with all loadings >0.6 for all measures and <0.3 for cross loadings. The factor
loading matrix is found in Table 2.3. In order to further confirm the factor structure,
CFA with EQS 6.1 was used to examine the informational, transactional, and relational
capabilities. Factor loadings for each measure were greater than 0.4, thereby exceeding
the cutoff criteria of 0.4 set by Gefen et al. (2000). The overall fit for the measurement
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model was as follows X
2
(DF=32, N=1016) = 42.490 (N.S.); RMSEA =0.021
(0.000,0.036); CFI = 0.996; SRMR= .029 NFI= 0.985; NNFI = .994; IFI = .996. The
level of fit met the criteria provided by past research. The RMSEA was less than 0.06,
the CFI greater than 0.96, and the SRMR less than 0.10, meeting all three of the criteria
suggested by Hu and Bentler (1999). Overall, this suggests that the three dimensions of
EC capabilities exhibit adequate discriminant and convergent validity, as indicated by
both the principal component analysis and the CFA.
Analysis and Results
Our data analysis was completed using ordinary least squares (OLS) regression
to examine the impact of EC capabilities and complementary business process change on
Internet channel performance. We centered the variable in order to reduce the possibility
of multicollinearity due to inclusion of interaction terms for testing the moderation
hypothesis.
Overall, the analyses indicated support for a model involving complementarities
between EC capabilities and business process change in influencing Internet channel
performance. Direct effects for transactional and relational EC capabilities were found to
be associated with higher levels of Internet channel performance, but the effect for
informational EC capabilities was not significant. In addition, the direct effect of
business process change on the resulting performance level was negative, indicating that
there may be some additional coordination costs which may reduce the benefits from
business process change. The interaction terms between information/relational EC
capabilities and business process change were positive in influencing Internet channel
performance. A graph of the interaction of relational capabilities and business process
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change, shown in Figure 2.2, indicates that the overall effect of business process change
is positive only when relational capabilities are high. The interaction term between
transactional EC capabilities and business process change was not significant.
Effect sizes for the relationships observed were well within the range where they
represent significant financial incentives for the organizations within this industry. For
the direct relationship between EC capabilities and profits, a one standard deviation
increase in transactional capabilities was associated with an increase in monthly profits of
nearly $1.5K while a one standard deviation increase in relational capabilities was
associated with an increase in monthly profits of nearly $4.5K. The effects of
complementarities were also in a range expected to be significant to the organizations
studied. A one standard deviation change in both informational capabilities and business
process change were associated with an increase in profit of $3K per month. A one
standard deviation change in both relational capabilities and business process change
were associated with an increase in profit of $5K per month. Statistics from NADA
indicate that the average dealer had over $1.6 million in monthly new car sales but only
$6.5K in net profits (NADA 2005). While the data reported in our analysis of the role of
capabilities are gross profits—i.e., excluding the cost of goods sold but not including
operational costs—it is still likely that they represent a sizable percentage of the
dealerships' bottom-line. Overall, the effect sizes related to the models examined are of
sufficient size that they are likely to have an important influence on the performance of
the organization.
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2.6 DISCUSSION
The RBV has recently received a great deal of focus within the IS community, as
both IT-related capabilities and organizational characteristics complementary to IT-
related capabilities have been identified. This research extends this work by
demonstrating the differing effects of different EC capabilities, integrating the role of
business process change, and identifying the complementarities between business process
change and EC capabilities in influencing Internet channel performance. By directly
assessing Internet channel performance within a single industry, we are also able to show
those factors which help organizations to thrive within the context of an IT-enabled
transformation.
Implications for Theory
A key contribution of this research is that it integrates the capabilities framework
of the RBV literature with the concept of business process change from the business
process reengineering (BPR) literature. In doing so, it extends the literature in both areas
and provides additional depth to the knowledge of complementarities in predicting
performance. This also suggests that additional linkages between existing literatures may
be possible through a complementarities lens—allowing a more nuanced understanding
of the process of IT value creation.
An additional contribution is related to the identification of the negative direct
effect of business process change on performance. This finding suggests that overall
efforts to significantly changing business processes can be costly to the organization if
not paired with the development of EC capabilities. This finding suggests the need to
understand the role of mindful practices (Weick et al. 1999) as they relate to business
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process change. In studying IT, mindfulness provides a way of capturing how
organizations address adoption decisions related to new technologies (Butler and Gray
2006; Fichman 2004; Swanson and Ramiller 2004). Here, a lack of mindfulness—i.e.,
mindlessness—may help to explain the negative outcome from business process change
for some organizations. Additional work should further explore the theoretical
relationships between IT, business process change, mindfulness, and IT value.
The lack of significance of specific relationships in the model should also be
acknowledged. The direct relationship between informational capabilities and Internet
channel performance was found to not be significant. A key aspect of the RBV that is
rarely acknowledged empirically is that some capabilities can be expected to loose
competitive value over time as they are competed away. This lack of significance
suggests that informational capabilities may have reached this status. As numerous
technologies make it easier for organizations to place information on the web, it is likely
that just the presence of information may not provide a competitive advantage, while
information along with dedicated individuals with the incentives to ensure the
information is correct and up-to-date—i.e., with the presence of business process
change—still offers a performance benefit.
We also found that the interaction between transactional capabilities and business
process change was not significant. This suggests that transactional capabilities may not
benefit from the same type of synergies with the rest of the organization that were found
for informational and relational capabilities. Transactional capabilities typically provide
a specific business function, acting as a substitute for interactions with other
organizational processes rather than as a compliment to other organizational processes, as
- 68 -
with informational and relational capabilities. More work is needed to further determine
whether the results here may be generalizable or a result of the limited interactions
between transactions and other organizational processes specific to the retail auto
industry.
Implications for Practice
For organizations, this research provides insights into the performance benefits
resulting from the development of EC capabilities. Transactional and relational
capabilities had a direct performance benefit, while informational capabilities did not.
Informational EC capabilities represent the most basic and imitable of the EC
capabilities. This suggests that organizations should maximize investments in developing
capabilities that will provide lasting organizational value.
This research also indicates both the potential benefits and the potential pitfalls of
complementary business process change. Those organizations which are able to develop
capabilities to execute electronic commerce are able to make those capabilities more
valuable through business process change. This change does not come without a cost,
however, and organizations must therefore assess both their level of commitment to
electronic commerce and their ability to develop electronic commerce capabilities. These
findings support much of the general disappointment associated with the returns from
many business process reengineering efforts in the late 1990s.
It is also important to note that there is a potential negative impact of Internet
sales on overall profits. While the Internet and the use of infomediaries enables
organizations to gain market share by locating new customers, it also can cannibalize
existing profits, as customers who would have purchased from the retailer at the higher
- 69 -
offline price obtain the online discount. Thus, organizations must pursue a strategy
which attempts to maximize overall profits, setting online prices such that the negative
effects of channel cannibalization do not outweigh the positive benefits of increased
market share.
Limitations
A limitation of this research is that it takes place in a single industry and therefore
may not be generalizable to other contexts. While this is a limitation of this work, the use
of metrics and the effects from detailed organizational characteristics related to
technology may only be detectible in single-industry studies, as industry-level effects
may overshadow organizational-level effects for many types of technology impacts
(Hawawini et al. 2003). As a result, future research should confirm the role of
complementarities between EC capabilities and business process change within different
industries.
A second potential limitation of this work is that the measures were collected
using a single survey and thus were susceptible to common method bias. This is
somewhat mitigated by the objective nature of the performance measures, and a
Harmon's single factor test indicated that less that 25% of the variance was explained by
any single factor. However, future work should consider the impact of complementarities
between EC capabilities, business process change, and other measures of organizational
performance which can be obtained from third party sources.
A third limitation of this work is related to the data being collected from a single
source. Collecting data from a single individual within the organization increases the
- 70 -
likelihood of measurement error. While this is a frequent limitation of organizational
studies, future work should attempt to obtain average values from multiple respondents.
A fourth limitation of this work is that single item binary measures were used to
capture business process change. In reality, organizations may undergo several different
types of business process change that may vary both in scope and degree. Different
scopes and degrees of business process change may result in different resulting levels of
value. Future work should access business process change using measures capturing
specific changes made, as it may be that there are only certain aspects of changes that
influence resulting performance outcomes.
2.7 SUMMARY
This research has provided a more complete understanding of the drivers of firm
performance in the Internet channel. Specifically, we identify complementarities
between EC capabilities and complementary business process change. In doing so, we
provide guidance to firms making decisions related to investing in the Internet channel
and optimizing existing Internet channel investments.
- 71 -
2.8 FIGURES
FIGURE 2.1 - RESEARCH MODEL
- 72 -
FIGURE 2.2 - GRAPH OF PROFIT VS. NUMBER OF LEADS
2.5
2
1.5
1
0.5
0
No BPC
BPC
0 100 200 300 400
-0.5
-1
Leads
- 73 -
l
n
(
P
r
o
f
i
t
)
2.9 TABLES
TABLE 2.1 - SUMMARY OF TYPOLOGIES OF IT CAPABILITIES
Framework Dimensions
Wade and Hulland (2004)
Bharadwaj et al. (1998)
Feeny and Willcocks
(1998)
1.
2.
3.
4.
5.
6.
7.
8.
1.
2.
3.
4.
5.
6.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Manage external resources
Market responsiveness
IT-business partnerships
IT planning and change management
IT infrastructure
IT technical skills
IT development
Cost effective IT operations
IT business partnerships
External IT linkages
Business IT strategic thinking
IT business process integration
IT management IT
infrastructure
Business systems thinking
Relationship building
Architecture planning
Leadership
Informed buying
Contract facilitation
Vendor development
Contract monitoring
Making technology work
- 74 -
TABLE 2.2 - DESCRIPTIVE STATISTICS
Online Discount
(Offline profit - Online Profit)/1000
Size (Total # sales/100)
Domestic (# of domestic brands)
Import (# of imported brands)
Luxury (# luxury brands)
Informational (Metrics, see measures)
Transactional (Metrics, see measures)
Relational (Metrics, see measures)
Business Process Change (BPC)
ln(profit)
Profit
Mean
0.394
0.865
0.535
0.142
0.076
2.510
2.598
0.914
0.684
8.700
15,930.160
Std
0.465
0.802
0.499
0.349
0.265
1.630
1.370
1.346
0.465
1.619
30,061.780
- 75 -
TABLE 2.3 - PRINCIPAL COMPONENT ANALYSIS
Informational1
Informational2
Informational3
Informational4
Transactional1
Transactional2
Transactional3
Transactional4
Relational1
Relational2
Relational3
N = 1017
1
0.887
0.871
0.831
0.733
2
0.742
0.727
0.711
0.636
3
0.830
0.765
0.693
*Cross loadings less than 0.3 are not shown.
- 76 -
TABLE 2.4 - CORRELATION TABLE
Variable 1 2 3 4 5 6 7 8 9
1. Informational 1.000
2. Transactional 0.446 1.000
3. Relational 0.290 0.196 1.000
4. Business Process Change -0.216 -0.115 -0.337 1.000
5. Performance 0.210 0.090 0.694 -0.221 1.000
6. Online Discount 0.001 -0.066 -0.033 0.059 -0.049 1.000
7. Size 0.297 0.143 0.585 -0.319 0.523 0.000 1.000
8. Domestic 0.115 0.075 0.141 -0.076 0.019 0.042 0.107 1.000
9. Import -0.143 -0.042 -0.277 0.157 -0.256 -0.088 -0.251 -0.436 1.000
10. Luxury 0.013 -0.110 0.054 -0.037 0.141 0.120 0.016 -0.116 -0.307
- 77 -
TABLE 2.5 - REGRESSION ANALYSIS RESULTS PREDICTING LN(PROFIT)
Intercept
Controls
Online Discount
(Offline profit - Online Profit)/1000
Size (Total # sales/100)
Domestic (# of domestic brands)
Import (# of imported brands)
Luxury (# luxury brands)
Direct Effects
Informational (Metrics, see measures)
Transactional (Metrics, see measures)
Relational (Leads/1000)
Business Process Change (BPC)
Interactions
Informational*BPC
Transactional*BPC
Relational*BPC
Adjusted R
2
F-value
N
Note: * p<0.05 **P<0.001 ***p<0.0001
Direct
7.935***
-0.472***
0.328***
-0.498***
-0.152
0.504**
0.027
0.175***
0.476***
-0.330**
0.469
61.79
639
- 78 -
Interaction
8.429***
-0.475***
0.339***
-0.446***
-0.118
0.520**
-0.081
0.162*
0.348***
-1.052***
0.152*
-0.013
0.335***
0.491
50.25
639
CHAPTER 3: UNDERSTANDING RETAILER USE OF ONLINE AUCTION
CHANNELS: STRATEGIES IN REPEATED SEARCH PROCESSES
3.1 ABSTRACT
We examine sellers' use of the online auctions for vehicles at eBay Motors
through the theoretical lens of search theory, viewing sellers' interaction with online
auctions as a process of searching for high-valuation customers. The vehicle market on
eBay motors has three distinguishing characteristics that make it ideal as a context to
examine seller behavior. First, more than half of the auctions on the eBay motors website
fail to lead to a completed transaction. Second, automobiles are somewhat unique in that
their vehicle identification number (VIN) enables a tracking of outcomes for a single item
across multiple auctions and even across multiple channels. Third, because over 70% of
sellers that sell through the eBay Motors channel are auto dealerships, auction outcomes
can be observed alongside sellers' activities in other channels. We find that the
characteristics for the market for a particular product type—particularly the price
variance and mean of the prior offer distribution—along with the extent that a seller
searches in other channels influence the resulting price premium the seller obtains. In
addition, we find that the total duration of search (number of days in which an item is
offered through the online channel) is negatively related to the resulting price premium
while the number of auctions in which an item appears is positively related to the
resulting price premium. This suggests that sellers discount their reserve price with time
but benefit as the number of auctions in which an item appears increases. In sum, this
research provides an understanding of how sellers use online auctions as part of an
overall business process of selling an item.
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3.2 INTRODUCTION
The Internet, along with the rapid adoption of electronic markets, has changed the
way in which buyers and sellers locate each other and transact in many industries.
Buyers and sellers now have access to many new channels—including web-based stores,
infomediaries, and electronic auctions—and may utilize several of these channels as part
of the sales process. Online auctions are one of the most widely adopted of these new
channels, with eBay in 2005 alone attracting 68 million users, 1.8 billion items, and $43.2
billion in gross merchandise volume (eBay 2005). As sellers may utilize auctions in
different ways as part of their overall sales strategy, understanding how sellers use and
obtain benefits from these online auctions is an important area for researchers to pursue.
There have been a number of studies of online auctions, examining topics such as
the validity of analytical model predictions (Lucking-Reiley 1999), the role of new
auction mechanisms (Budish and Takeyama 2001; Stephen 2001), and bidder behavior
(Bapna et al. 2004; Roth and Ockenfels 2000). While existing studies have greatly
increased our understanding of outcomes from individual online auctions, a notable gap
in the literature acknowledged by Wood (2004) is that there are "few articles on seller
strategies (other than reputation) that can be employed to gain a larger price paid." For
example, very little is known about how external factors related to the market, the
product, or the seller may influence the seller's reserve price—the key lever that the
seller uses to control the minimum price obtained from a sale. By better understanding
the underlying motivations and resulting impact related to seller strategies and behaviors,
we can better understand how sellers can optimize profits and market makers can best
serve both buyers and sellers.
- 80 -
This research examines seller behavior in the use of the electronic markets for
vehicles at eBay Motors, conceptualizing a seller's use of the auction channel as a
process of searching for high-valuation customers (Genesove 1995). A basic tenet of
search theory is that individuals optimize their search behavior based on the tradeoffs
between the expected benefits and the expected costs from search (Diamond 1985).
Consumer search behavior has frequently used duration of search as a key way to
understand shopping behavior in the retail channel (e.g., Banks and Moorthy 1999;
Dellaert and Haubl 2004; Johnson et al. 2004). Here, we conceptualize seller search
behavior through the duration of search and the number of auctions. These highly
related aspects of search behavior more fully capture the seller search process than
duration of search alone. However, duration of search is modeled as the key optimizing
variable by which sellers make decisions, as it is related to the seller's inventory holding
costs. In the context of high-valuation goods such as vehicles, inventory holding costs
can be quite large when compared to overall profit, and metrics such as the number of
days vehicles have been on the lot are frequently used to drive operational decisions.
In understanding sellers' motivations, we incorporate an understanding of how
external factors outside of the individual auction—namely, the sellers' search in other
channels (SOC) and the product offer distribution—influence the duration of search and
the resulting premium of the seller. SOC represents sellers' use of other channels for the
same product—captured through a search for the VIN number using a popular search
engine. By identifying sellers' activities in listing a particular item through other higher
valuation channels, we develop an understanding of how these activities influence the
sellers' activities in the auction channel. Similarly, characteristics of the product offer
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distribution—that is the distribution of high bids for both those auctions which ended in a
sale and those that did not—are expected to influence the willingness of the seller to
search in the online channel. In sum, by identifying these key external factors related to
the potential benefits that a seller obtains from search, we can understand both the
duration of search and the resulting price premium obtained by the seller, thereby
obtaining a more complete picture of the causes and consequences of seller search
behavior. A framework describing the proposed relationships is found in Figure 3.1.
The vehicle market on eBay motors has three distinguishing characteristics that
make it ideal as a context to examine seller behavior. First, more than half of the
auctions on the eBay motors website fail to lead to a completed transaction. This
suggests that sellers may employ complex strategies as they set and reset their reserve
price in subsequent auctions for the same product. Second, automobiles are somewhat
unique in that their vehicle identification number (VIN) enables a tracking of outcomes
for a single item across multiple auctions and even across multiple channels. With the
capability to observe how sellers use online auctions over time, we are able to obtain
insights into the strategies employed by these sellers as part of the sales process. Third,
because over 70% of sellers in the eBay Motors channel are auto dealerships which also
have access to an offline channel, it gives us a way to understand auction outcomes in a
context in which sellers have access to more than one channel. In sum, the eBay Motors
channel provides a rich context in which to develop and empirically test a model of seller
search behaviors and outcomes in the business process of selling a high-valuation
product. We tested our model, shown in Figure 3.2, using a selection of 31,445 auctions
for new and used vehicles.
- 82 -
There are two primary contributions of this paper. First, as the role of the lower
search costs of the Internet has primarily been viewed from the customers' perspective
(e.g., Bakos 1997), viewing sellers' use of online auctions as a process of searching for a
high-valuation customer provides a greater understanding of the role of low search costs
in influencing stakeholder behaviors in electronic markets. Second, as the value
implications of technology use is an important theme in the IS literature (e.g., Devaraj
and Kohli 2003; Zhu and Kraemer 2004), this research identifies how different aspects of
use—namely, the seller strategies for re-listing items and searching other online
channels—drive the value the organization obtains from the use of online auctions.
The rest of the paper is organized as follows. First, we provide a description of
the online auction mechanism and discuss relevant past research. We then provide a brief
overview of the search literature as it relates to electronic markets and explains the
behavior of sellers. This is followed by specific hypotheses related to the sellers' search
in online auctions, the data and analysis techniques used, the results, and a discussion of
the implications of this work.
3.3 LITERATURE REVIEW
In this section, we first discuss the context of this study—the online auto market
at eBay motors—and provide background on other work which has examined online
auctions. Next, we provide a basic overview of the concepts governing search theory,
with specific reference to how search theory has been applied to electronic markets and
seller behavior. This foundation then enables us to build a model predicting the causes
and consequences of seller search behavior in the next section.
The Online Auction
- 83 -
Beginning primarily as a market for collectibles and used consumer goods, the
eBay site has continued to expand in the breadth of products offered and the number of
individual items available. In 2000, eBay recognized the large number of autos being
sold on its site and set up the eBay motors site—the sub-unit of eBay specializing in
automobiles and accessories. The number of vehicles sold through eBay has continued to
grow rapidly, and eBay is used by retailers to sell both new and used vehicles, though
used vehicles make up the vast majority of sales.
The auctions on eBay Motors follow the same rules as other eBay auctions, which
have been described as second-price ascending proxy-bid auctions. The seller selects the
reserve price, the starting bid, and the period of time over which the auction will occur
(i.e., 3-10 days). Buyers search for items online and may choose to enter a "proxy bid,"
or the maximum amount that they are willing to pay for a vehicle. After a bid is placed
the auction mechanism lists the highest bidder at an increment above the second highest
bid. For example, a bid increment of $1.00 would result in a (proxy) bid of $15.00 for
bidder A and a bid of $8.00 for bidder B being displayed as a high bid of $9.00. A more
complete description of the auction mechanism used by eBay can be found in prior
research (Aldridge 2005; Bajari and Hortacsu 2002).
For some, the existence of an online market for primarily used vehicles may be
surprising in itself. Vehicles are typically considered to be high-involvement purchases,
in which buyers often search extensively before making a purchase decision (Ratchford et
al. 2003). As a result, purchasing a used vehicle without physically inspecting it initially
appears to be something that would not appeal to many consumers. Akerlof's (1970)
seminal paper examining aspects of used vehicles further casts doubt on the feasibility of
- 84 -
a market with lower levels of product quality information, as is the case with online
auctions. He suggested that as information asymmetry increases, markets are more likely
to fail as bad vehicles or "lemons" drive out the good vehicles through a process of
adverse selection. Given that when inspecting automobiles through electronic markets
individuals have less information than when inspecting automobiles in person, it would
follow that that those sellers with lemons would be more likely to pick the online auction
channel.
There are several associated findings, however, which may help to explain the
thriving online auction market for used vehicles on eBay Motors. First, the presence of a
feedback mechanism is one important design feature of online auctions that discourages
opportunistic behavior (Dellarocas 2003). Feedback mechanisms work by enabling
buyers to provide feedback on sellers and sellers to provide feedback on buyers. Since
sellers obtain a price premium from high quality reputations (Ba and Pavlou 2002;
Dellarocas 2005; Jeffrey 2005; Mikhail and James 2002), the mechanism discourages
sellers from inaccurately representing the quality of goods sold. Second, tests of
Ackerlof's hypothesis have indicated little empirical support in real world situations. An
examination of repair records for the truck market suggest that buyers of used trucks less
than 10 years old were no more likely than buyers of new trucks to experience repairs
(Bond 1982, 1984). Engers et al. (2005) similarly found no greater incidence of
problems for used vehicles when compared with new vehicles. A final factor driving the
success of the online auction market for used vehicles is that online channels typically
offer significantly lower prices and increased convenience for consumers. Scott-Morton
et al. (2001) found that new vehicles sold through online infomediaries were discounted
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an average of $450. As a result, consumers may take advantage of the ability to purchase
autos at a significant discount from offline channels if those benefits outweigh the risks
posed by increased information asymmetry. Overall, with the use of reputation
mechanisms to control information asymmetry and the advantages of cost savings,
consumers have moved in significant numbers to obtain the benefits offered by online
auctions for vehicles.
Search Theory
It is well known that extensive streams of literature exist in economics and
marketing investigating both search and auction theory. Diamond (1985) and Stiglitz
(1989) provide reviews of the search literature as it relates to consumer search for
commodity goods, and Klemperer (1999) and Milgrom (1987) provide excellent
overviews of auction theory. This research draws directly from the extensive literature
on search theory, but a full review of the search literature is outside of the scope of this
article. Instead, it is important to provide an overview of search theory as it has been
applied to the understanding of electronic markets, and seller behavior..
Search Theory, Electronic Markets, and Seller Behavior
Search theory provides a way through which to understand the behaviors of
individuals and organizations as they optimize the costs and benefits of the search
process. It has been particularly useful as a way of understanding electronic markets, as
the Internet has lowered the cost of searching for and providing information. Reviews
outlining the impact of the Internet have specifically focused on the importance of lower
consumer search costs (e.g., Bailey et al. 1999; Bakos 1998; Bakos 2001; Smith et al.
2000). Stahl (1989) formalized the impact of decreased search costs, which result in
- 86 -
lowered prices and improved consumer welfare. Bakos (1997) subsequently examined
the incentives sellers, buyers, and infomediaries have to invest in online markets, as well
as the differing role of lowered search costs for product and price information.
While the majority of research has focused on the benefits accruing to consumers
from the lower search costs associated with the use of electronic markets, recently several
papers have identified the benefits accruing to sellers. Campbell et al. (2005)
demonstrated that lower consumer search costs may lead to collusive behavior among
retailers. When search costs are low, it is much easier for retailers to monitor the prices
of competitors—reducing the benefits from "cheating" by lowering prices. As a result of
the collusion, prices increase and overall consumer welfare decreases. Lynch and Ariely
(2000) experimentally examined the impact of lowered search costs for product and price
information on both differentiated and undifferentiated products. They found that when
search costs were low for quality information, individuals were less price-sensitive. They
also found that the ability to compare prices across stores had no impact on price
sensitivity for unique products. In a related study, Kuksov (2004) found that when
product design is treated as something that can be acted upon by firms, product
differentiation can actually counteract the increased competition resulting from lower
search costs. Firms choose to differentiate their products more when search costs are
low. With increased differentiation, product fit with consumer preferences provides a
differentiating factor which limits price competition. Together, these three articles
suggest that sellers will employ strategies which offset the decline in profit resulting from
the lowered search costs of the Internet.
Search Theory and Seller Search
- 87 -
Seller search is defined as the activities engaged in by a seller in her search for a
customer to purchase a specific item. For many contexts, this process is not individually
observable, as sellers set prices and advertise as a way of searching for customers.
Auctions, however, provide a situation in which seller search is individually observable.
While the study of auctions using a framework of search theory has been quite limited, an
important related study by Genesove (1995) examined seller search in the context of
wholesale auto auctions, a context that corresponds very closely to that of the eBay
motors market. Because of this relationship, it is relevant to provide a more detailed
overview of the similarities and differences between Genesove (1995) and this work.
Genesove (1995) utilized the offer distribution characteristics—particularly the
mean and the variance of the offer distribution—to predict the likelihood of sale for a
particular auction. More specifically, in the basic search model proposed by Genesove
(1995), a seller has one vehicle, incurs a search cost for transporting the vehicle to the
auction house, and knows the offer distribution for her vehicle type. She sets her reserve
price, above which she will accept any offer and below which she will continue to search,
discounting future revenue. The procedures used in this work for the calculation of the
mean and the variance from the offer distribution are adapted directly from Genesove
(1995). Key findings from Genesove (1995) are that both the mean and the variance of
the distribution of prior offers influence the probability of sale. This work extends the
work by Genesove (1995) by examining the longitudinal processes of seller use of online
auctions for the same product as well as viewing the simultaneous use of multiple online
channels. Genesove (1995) viewed single auction outcomes without taking into account
- 88 -
whether the vehicle had been auctioned previously or whether the seller has offered the
vehicle in other channels.
The wholesale auction case examined by Genesove (1995) differs in two notable
ways from the eBay motors auction. First, in the wholesale auction, the seller receives a
bid and then makes a decision whether to continue searching or accept the offer. In the
eBay motors case, the seller selects a reserve price before the auction and then receives
the high bid at the end of the auction that may or may not meet the reserve price. While
the time at which the seller sets her reserve price is different, when sellers ex-ante have
knowledge of the distribution of prior sales they adopt the same reserve price strategy
(Kohn and Shavell 1974), thus allowing us to adopt the same search-based framework.
A second difference is that the wholesale auction analysis was completely offline
and did not allow for the simultaneous offering of an item in more than one channel. The
separation of the informational and logistical components of a transaction is one of the
key differences between offline and online auctions (Kambil and van Heck 1998), and
thus exploring seller search behavior in the presence of the lower search costs of online
auctions is an important contribution. In the next section, we incorporate the drivers and
outcomes of search into a full model of seller search behavior and price outcome.
3.4 RESEARCH MODEL AND HYPOTHESES
In building our model of the seller search, we first discuss a key dependant
variable of interest, the price premium of the seller. Next, we address the search
behavior—with particular emphasis on the duration of search and the number of auctions.
Finally, we examine the role of two external factors related to the potential benefits the
seller may possibly expect from the sale of an item (1) the sellers' search in other
- 89 -
channels and (2) the distribution of prior offers (final high bid) for products of the same
type.
Seller's Price Premium
In marketplaces such as eBay, there is no standard market clearing price for an
item of a particular type. Instead, the auction process enables a matching of buyers and
sellers through active bidding. To understand the benefits of different seller behaviors, it
is necessary to establish a reference point and then determine the performance of sellers
relative to that reference point—i.e., to determine the price premium obtained by the
seller. One possible reference point is the predicted price for a vehicle based on its
characteristics and other comparable sales. However, this would result in the price
premium for some sales being negative. In defining the price premium of the seller, we
first assume that each individual item (i) is of a particular type (j). We further assume
that for an item of type j, there exists a price P
MIN(j)
, below which no seller will sell the
item. The price premium that the seller obtains for an item i of type j is then the
difference between the price paid by the highest bidder (P
ij
) and P
MIN(j)
. This calculation
of premium is very similar to the procedure followed by Ratchford and Srinivasan (1993)
for the determination of the returns from consumer search.
Premium = P
ij
- P
MIN(j)
Duration of Search and Number of Auctions
As stated earlier, we follow prior research in marketing and labor economics to
use duration of search as a key focal variable to understand search behavior. However,
the duration of search and the number of auctions are two distinct yet highly related
components of the sellers' search behavior which may each independently influence the
- 90 -
outcomes from search. For example, a seller may list an item in 3 seven-day auctions or
7 three-day auctions and have the same total search duration. While studies have
consistently found that longer auctions yield higher prices (Bajari and Hortacsu 2002;
Lucking-Reiley et al. 2000), these studies are cross-sectional in that they compare only
auctions in which the reserve price is met while ignoring auctions in which the reserve
price is not met. As a result, it is unclear from prior empirical findings whether sellers
are able, on average, to obtain a higher premium through a single long auction or multiple
shorter auctions. Thus, a further objective of this research is to provide practical advice
to sellers on the tradeoffs between these two options.
The relationship among the sellers' search behavior—i.e., the duration of search
and the number of auctions—and the price premium depends upon both the stochastic
aspects of the search process and the reserve price strategy employed by the seller. The
longer the duration that a seller searches for a buyer, the stochastic properties of search
suggest that it is more likely that a seller will locate a higher valuation buyer. Assuming
a constant arrival of potential bidders, the longer auction clearly has an advantage in
exposing the seller's product to more potential buyers and thus increasing the likelihood
of finding a high-valuation buyer. The stochastic properties of search also provide a key
explanation of why longer auctions have been found to yield higher prices (Bajari and
Hortacsu 2002; Lucking-Reiley et al. 2000). Thus, the stochastic aspects of the search
process suggest that duration of search will be positively related to the price obtained.
Whether the overall effect of the duration of search on the price obtained is
positive or negative, however, depends on the reserve price strategy adopted by the seller.
Two simple reserve price strategies involve (1) a constant reserve price or (2) a reserve
- 91 -
price discounting strategy. Prior work by Ashfelter et al. (2002) in auctions for
impressionist and contemporary art—a market which has similarities to the online vehicle
market in that the sales rate is low and the overall price is high—developed an analytical
model which suggests that the optimal strategy is for sellers to set a reserve that is a
constant proportion of the expected high bid of the auction. This is also consistent with a
well known finding from labor economics that a worker's optimal strategy is to set a
reserve wage and accept only jobs above that wage (e.g., Lippman and McCall 1976). As
each time a seller lists an item on the online auction they receive a sample from the
overall distribution of potential buyers, on average sellers selecting a higher reserve price
would be less likely to find a buyer within a single auction (Genesove 1995), thereby
increasing the overall duration of search and the number of auctions. When a buyer is
found, the higher reserve can act to obtain a slightly higher price from the bidder with the
highest valuation (Samuelson 1981). Thus, we can expect that if sellers adopt a constant
reserve price strategy both duration of search and the number of auctions will be
positively related to the price premium.
The reserve discounting strategy is one in which sellers reduce their reserve price
with time. Sellers may be likely to adopt this strategy in order to incorporate additional
information learned as part of the sales process. In addition, as the overall value of items
is high relative to the cost of searching, sellers may be willing to start their reserve price
very high as they hope to find a "sucker." Lucking-Reiley (2000) found evidence of this
"sucker search" effect in the relationship between the fees charged by different auction
sites and the percentages of auctions that ended in a sale. He found that in 1999 when
auctions taking place on Yahoo.com did not charge a seller fee only 16% of transactions
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ended in a sale. For auctions taking place on eBay, which did charge a fee, 54% of all
auctions ended in a sale. In addition to online auctions providing the opportunity to
search for a "sucker," listing an item in subsequent auctions also provides sellers with an
opportunity to reassess their valuations downward as they obtain more information about
the current market conditions. Laboratory studies confirm this general behavior, finding
that sellers generally adjust reserve prices downward in subsequent auctions (Burdett and
Vishwanath 1988; Rosenfield and Shapiro 1981). In sum, we can expect that if sellers
engage in a reserve price discounting strategy duration of search will be negatively
related to the price obtained.
We suggest that the low cost of search provided by the online environment will
result in sellers adopting a strategy of discounting their reserve price with time. While
online services exist to provide the seller with the expected market value for a given item,
non-homogenous goods such as used vehicles can be expected to have a great deal of
variation in the price that buyers pay. Even among new vehicles, typical pricing and
negotiation is such that buyers may pay significantly different prices for the same make
and model vehicle. For example, work by Scott-Morton et al. (2001) found that buyers
negotiating prices through online infomediaries paid on average $425 less than buyers
negotiating price in person for the same make and model new vehicle. As the overall
cost of searching for a buyer using an online auction can be expected to be quite low
compared to the variation in price for high-valuation items such as vehicles, searching the
market for a high-valuation buyer ("suckers") and adjusting the reserve price downward
over time is likely to appeal to many sellers. Under this strategy, those vehicles which
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sell earlier will be the ones that yield the highest prices while those that sell in subsequent
auctions will yield a lower prices. As a result, we hypothesize:
H1a: For items sold in online auctions, seller's search duration is negatively
associated with seller's price premium.
H1b: For items sold in online auctions, the number of auctions is negatively
associated with the seller's price premium.
Determinants of Duration of Search
It is important to note that the relationship between the antecedents of search
behavior, the duration of search, and the resulting price paid is not fully mediated by the
duration of search, as is shown in Figure 3.1. In each individual auction, it is possible
that a seller will locate a high-valuation buyer. As a result, it is not necessary for a seller
to search across multiple auctions to locate a single high-valuation buyer. Instead, the
sellers' willingness to search—captured by the antecedents of search—influences both
the search duration and the resulting price premium.
Search in Other Channels
The Internet offers many different opportunities and mechanisms for sellers to
reach customers. These may include online infomediatries, online newspapers, or
individual websites. We define search in other channels (SOC) as the seller's efforts to
locate buyers in other online channels not including the eBay Motors channel. In most
cases, it would be impossible to observe a seller's activities in multiple channels for the
same product. However, in the context of vehicles, the unique VIN number along with
the established practice of listing the VIN number when advertising a vehicle online
enables us to measure SOC for each vehicle across different online channels.
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SOC is expected to increase both the duration of search and the resulting seller's
price premium. The justification for these relationships is related to the nature of the
auction channel as a discount channel and the costs incurred as a result of SOC. Similar
to electronic markets for coins (Bajari and Hortacsu 2002; Lucking-Reiley et al. 2000)
and books (Brynjolfsson et al. 2003), the prices for vehicles purchased through online
price negotiation mechanisms are lower than those purchased through the retail channel
(Scott-Morton et al. 2001). While most online channels offering vehicles—i.e., Yahoo
Autos, Autobytel, Cars Direct, or the dealers' website—may provide vehicle and price
listing services, the actual negotiation process occurs face-to-face in the retail channel.
As a result, other channels providing listing services without specific negotiation
functionally can be considered as a way for the retailer to advertise for leads to the retail
channel. An increase in the likelihood that a seller will sell an item through the higher-
valuation retail channel will increase the seller's reserve price in the discount channel
(Genesove 1995). In addition, when a seller experiences positive costs from advertising
through multiple online channels, the price at which the seller experiences zero profit
necessarily increases. In effect, a seller must set a higher reserve price in order to make
the same level of profit. As a result, we expect:
H2a: For items sold in online auctions, seller's search in other channels (SOC) is
positively associated with the seller's duration of search.
H2b: For items sold in online auctions, seller's search in other channels (SOC) is
positively associated with the seller's price premium.
Offer Distribution
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The online market for vehicles provides both buyers and sellers with a
tremendous amount of information. This information can come from both auctions that
end in a completed sale as well as those that do not. In other words, by placing a bid, an
individual is communicating to the seller a willingness to purchase an item at a given
price, and such additional information about the buyer's willingness to pay is helpful for
the seller in setting the optimal reserve price (McAfee and Vincent 1992). We define the
term offer to describe any auction that has at least one bid. The willingness of the seller
to search and the resulting price premium are expected to be influenced by the offer
distribution. We use both the expected offer mean and the expected offer variance as a
way to capture the influence of the offer distribution on these outcomes, describing each
effect individually.
The expected offer mean is predicted to be positively associated with the duration
of search and the resulting price premium obtained by the seller. Like SOC, this
relationship is also linked with the nature of online auctions as a discount channel. When
a seller places a vehicle for sale in a discount channel, the resulting price the seller can be
expected to obtain is significantly less than in the retail channel. Further, the amount of
the discount typically is dependant on the overall vehicle price, such that buyers of more
expensive vehicles obtain a greater discount (Scott-Morton et al. 2001). At the same
time, the cost of re-listing an item on the online auction is a constant and not dependant
on the price of the vehicle
1
. It follows from this that the opportunity costs for selling a
more expensive vehicle online are greater than for a less expensive vehicle relative to the
search costs, suggesting sellers will search longer and expect a greater price premium
when the expected offer mean is higher. Empirical work by Genesove (1995) also
1
At the time the data was collected, the fee for listing a vehicle was $50.
- 96 -
supports a positive relationship between mean price and search duration, finding that an
increase in the mean price decreases the probability of sale in any given auction. Thus,
we hypothesize:
H3a: For items sold in online auctions, the expected offer mean is positively
associated with the seller's duration of search.
H3b: For items sold in online auctions, the expected offer mean is positively
associated with the seller's price premium.
An increase in the expected offer variance is also expected to increase the
duration of search. The general intuition for this relationship is that an increase in the
variance increases the potential benefits that a seller can hope to obtain from search. A
seller marketing a product in a market with very low variance in offer price would have
little incentive to search in more than one auction. The influence of the available benefits
on search behavior is a central tenant of search theory (Diamond 1985). Analytical work
by Balvers (1990) noted that an increase in this variance increases the willingness of an
individual to search. This has also been supported empirically, as perceptions of price
variance were found to influence the search effort of consumers (Duncan and Olshavsky
1982). In his study of seller search behavior in wholesale auctions, Genesove (1995)
found that an increase in the variance in the price of a product decreases the probability
of a sale while subsequently increasing the price obtained if the auction is successful. In
this context, it is similarly expected that the increase in variance will increase the
duration of search and the resulting price premium obtained by the seller. Therefore, we
hypothesize:
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H4a: For items sold in online auctions, the expected offer variance is positively
associated with the seller's duration of search.
H4b: For items sold in online auctions, the expected offer variance is positively
associated with the seller's price premium.
3.5 RESEARCH METHODOLOGY
We gathered data for 3 months of completed auctions from the eBay Motors
website using an automated agent. This agent captured information about the
characteristics of the vehicles, the sellers, and the auction itself. Vehicle characteristics
included the VIN number, the number of miles, the existence of a seller warranty, the
model year, and whether the vehicle was new or used. The first 9 digits of the VIN
number were used to establish the model and trim level of the vehicle. Seller
characteristics included the positive and negative feedback score of the seller. Auction
characteristics measured included the starting bid, the number of bids, the high bid, and
whether the auction ended with a completed sale. A second automated agent utilized a
popular search engine to estimate the extent to which the seller searched in other channels
(SOC) for the same vehicle, as identified by the unique 17-digit VIN number. We used
the filtered number of unique pages returned as a proxy for SOC.
The overall dataset consisted of 450,040 auctions. Several steps were then taken
to reduce the dataset to include only those vehicles in which the price premium could be
accurately predicted. Because older vehicles typically have more variance in their overall
condition, we first filtered our data to include only vehicles that were less than 5 years
old. In addition, because we were primarily interested in understanding the behavior of
dealers with access to more than one channel, we limited our analysis to only those
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sellers that had sold at least 2 vehicles through the online auction channel. Next, as 30 is
generally used as a rule of thumb as the number in which a distribution approximates the
normal distribution, we eliminated all those models in which there were less than 30
completed sales. This filtering resulted in a total of 31,445 auctions that were analyzed,
including 5,127 completed sales.
The analysis of the data was done in three stages. The first stage involved the
calculation of the price premium obtained by the seller. For the reference point P
min
we
used the lowest price that any seller would accept for a given model of vehicle
(controlling for all vehicle characteristics) over the period of time in which the data was
examined, controlling for vehicle and seller characteristics. To do this, we regressed
vehicle and seller characteristics on the actual sale price to calculate the expected sale
price based on the vehicle characteristics. Subtracting the actual sale price from the
predicted sale price yielded a price residual which controlled for vehicle and seller
characteristics. For each model of vehicle, as determined by the first 8 digits of the VIN
number, the vehicle which had the most negative value for the residual was used as the
reference point P
min
. The price premium obtained was then calculated by subtracting the
residual value for the vehicle designated as P
min
from the residual for all vehicles of the
same model, yielding a condition adjusted value for the price premium. This is similar to
the methodology used by Ratchford and Srinivasan (1993) in their calculation of the
consumer price premium obtained from search, except in that work the dealer invoice
price was used as a reference point.
The second step in the analysis involved calculating the expected offer mean and
the expected offer variance using all auctions in which a single bid was placed—i.e., both
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those that had ended in a sale and those that had not. In calculating the expected offer
mean (m
i
) for a given vehicle, the auction characteristics x
i
, seller characteristics y
i
, and
vehicle characteristics z
i
were used as indicated in the equation below:
E[m
i
| x
i
, y
i
, z
i
] = |
0
+ x
i
|
1
+ y
i
|
2
+ z
i
|
3
(2)
The expected offer variance for a given vehicle can be calculated from the
squared difference between the price p
i
and the expected mean offer E[M
i
| x
i
, y
i
, z
i
]. By
regressing the auction characteristics x
i
, seller characteristics y
i
, and vehicle
characteristics z
i
on the log of the squared residuals we can calculate the expected offer
variance for each vehicle as indicated in the equation below.
E[(M
i
- (|
0
+ x
i
|
1
+ y
i
|
2
+ z
i
|
3
, x
i
, y
i
, z
i
))
2
| = exp (o
0
+ x
i
o
1
+ y
i
o
2
+ z
i
o
3
) (3)
We calculated the values for the expected offer mean and the expected offer
variance using the equations above. As mentioned earlier, the procedure used follows
that in related work, and addition details can be found in Genesove (1995). We first
regressed the auction, seller, and vehicle characteristics on the offer price. This allowed
us to calculate the expected offer mean for each vehicle. A second regression was then
completed on the log of the squared difference between the expected offer mean and the
actual high bid using the same auction, seller and vehicle predictors. The beta
coefficients resulting from this second analysis were used to calculate the expected offer
variance.
The final stage of the analysis was to examine the full system of equations used to
predict the duration of search and the price premium obtained by the seller. Two
instrumental variables were used in the analysis to enable the full system to meet the
necessary rank and order criteria for identification. Appropriate instruments are highly
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correlated with the dependant variables of the equation and not related to the other
independent variables. For the case of duration of search, the total number of vehicles
listed by the seller would be expected to influence the efficiency in which a seller
interacts with online auctions. This efficiency could be expected to be positively related
to the duration of search but not related to the price premium. In predicting the overall
price premium obtained by the seller, the number of bids can be expected to be positively
related to the overall premium but not to the duration of search. Other auction, vehicle,
and seller characteristics were omitted from this analysis because they were already
included in the calculation of the seller's price premium. The full system of equations
examined is shown below.
log(Premium) = |
0
+ |
1
log(A) + |
2
log(SOC) + |
3
E(M
ij
) + |
4
log(E(Var
ij
)) + |
5
log(Dur) + c
log(Dur) = |
6
+ |
7
log(A) + |
8
log(OCS) + |
9
E(M
i
j
) + |
10
log(E(Var
ij
)) + |
11
log(Sold) + c
A = The number of auctions in which a vehicle has been offered
SOC = Search in Other Channels (# of unique sites returned from search of VIN)
E(M) = The estimated value for the offer mean, from equation 2
E(Var) = The estimated offer variance, from equation 3
Dur = The total duration of search (days)
Sold = The total number of vehicles sold by the seller during the study
The analysis of these equations was done using Three Stage Least Squares (3SLS)
estimation. The 3SLS systems estimator is used to calculate the full system of equations,
taking into account the endogenous relationships between the equations while controlling
for the potential correlation among the error terms. 3SLS combines 2SLS and SUR
methods to take into account both dependent repressors and cross-equation correlation of
- 101 -
errors, and is the recommended approach for triangular structural systems (Lahiri and
Schmidt 1978), as in this study.
Results
The first stage of the analysis involved calculating the price premium obtained by
the seller. The regression analysis predicting the final sale price yielded an overall R
2
of
0.898, indicating that the characteristics of the vehicle, seller, and the auction provided a
high degree of explanatory power. Positive feedback, a high starting bid, and the
presence of a warranty each were associated with a higher price premium while negative
feedback and the number of miles were each associated with a lower price premium. The
results of this analysis are shown in Table 3.3. The predicted values calculated using the
beta coefficients from Table 3.3 were used in order to calculate the price premium using
the method described earlier.
The second stage of the analysis predicting the expected offer mean and the
expected offer variance using the vehicle characteristics also indicated a high level of
overall explanatory power, with an overall R
2
of 0.662 for the prediction of the expected
offer mean and an overall R
2
of 0.226 for the expected variance. The results of the
regression analysis are shown in Table 3.4. These analyses indicate that both the
expected offer mean and the expected offer variance are influenced by the characteristics
of the vehicles. Positive feedback had a positive influence on expected offer price and
expected offer variance. A higher starting bid, a warranty, and a new vehicle each led to
a higher offer and lower offer variance.
The results of the 3SLS analysis of the systems of equations indicate support for
the hypothesized predictors, with the exception of the relationship between the number of
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auctions and the price premium. The results of the 3SLS analysis are shown in Table 3.5.
As hypothesized, the expected offer mean and the expected offer variance were positively
associated with both the duration of search and the resulting price premium. The SOC
was positively associated with both the search duration and the resulting price premium
of the seller. The duration of search was negatively associated with the price premium of
the seller, indicating that sellers engaged in a reserve discounting strategy.
Unexpectedly, the number of auctions was positively associated with the price premium,
indicating that there were benefits to having multiple auctions.
Effect sizes for the relationships observed were well within the range where they
represent significant financial incentives for the organizations within this industry. A one
standard deviation change in the duration decreased the price premium by $150 while a
one standard deviation increase in the number of auctions was associated with a $99
increase in the price premium. SOC is associated with a 0.1 day increase in the duration
and a $117 price premium. Statistics from NADA indicate that the average dealer had
over $9 million in sales, but only $90 thousand in profits, with an average profit of $141
on 638 vehicles. Using a discount rate of 8%, the inventory costs of holding each of the
638 vehicles for an additional week results in nearly 14 thousand dollars in inventory cost
and represents 15% of the overall profit. Overall, the effect sizes related to models
examined are of sufficient size that they are likely to have an important influence on the
decision making and sales strategy related to online auctions.
3.6 DISCUSSION
This research provides a new way of viewing the outcome of subsequent auctions
for the same individual product that has not been studied empirically by researchers.
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While a great deal of research has identified factors influencing the outcomes of auctions,
we extend prior work by showing how individual auctions can be influenced by external
factors related to sellers' willingness to search. In reviewing the implications of these
findings, we first discuss the findings related to SOC and then the findings related to the
market characteristics of the mean and the variance of the offer price. We then further
discuss the relationship between duration of search, number of auctions, and the price
premium of the seller.
Search in Other Channels
The results of the analysis also suggest that the extent to which sellers search in
other channels (SOC) influences both the duration of search and the resulting price
premium of the seller. These findings have implications for both buyers and market
makers. Buyers may be able to use the SOC information as a way of identifying sellers
likely to have lower reserve prices—i.e., those exclusively utilizing the online auction
channel. The Internet and electronic markets make it easier for sellers to search for
buyers using the Internet and online markets. However, this search process is not costless
and sellers demand a price premium from buyers in the online auction channel when they
have also listed a vehicle in other online sites.
Market makers may be able to provide greater value and charge additional fees
for the bundling of services with other online channels. This service bundle may allow
sellers to even more easily sell across multiple online channels, providing a method of
price discrimination. Though selling in multiple channels online may not require a great
degree of technical sophistication, sellers still have to interact with multiple interfaces to
list a vehicle on multiple websites. An integration of these sites or automated tools which
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enabled a seller to list a vehicle on multiple different sites using a single interface may
lower the search costs of the seller and the buyer, increasing the overall efficiency of the
market.
Market Characteristics
We found that the market characteristics, as captured by the expected offer mean
and the expected offer variance, influence both the search duration and the price premium
of the seller. The influence of the expected offer mean and the expected offer variance
was identified by Genesove (1995) in a cross sectional study of wholesale auto auctions
which examined whether the reserve price would be met as an auction outcome. This
research extends that work by examining the use of the auction channel across
subsequent auctions for the same unique product, relating the expected offer mean and
the expected offer variance to the price premium of the seller and the duration of search.
Search Behavior
Our results indicated a negative relationship between the duration of search and
the price premium obtained by the seller, suggesting a reserve discounting strategy. This
results in a situation in which the seller's price premium decreases as the overall duration
of search increases, as shown in Figure 3.3. One question that logically follows from this
finding is Do sellers search too much? While a full normative analysis of the seller
search process is outside of the scope of this work, a further examination of the data
shows that in a sufficient number of cases they do. In examining all of the auctions in
which the vehicle appeared in more than one auction, 6% of the time sellers accepted a
price that was lower in the final auction (in which a sale occurred) than was offered in a
prior auction in which the reserve price was not met. For these cases, the average
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difference between the price offered in a prior auction and the final price accepted by the
dealer was $879 (standard deviation = $1500), an amount over four times the average
profit for used vehicles (NADA 2005). This loss is in addition to the inventory holding
costs resulting from the extended search effort.
One way to understand the relationship between the use of electronic auctions
observed here and duration of search is through the metaphor of the Icarus Paradox.
Icarus was given the power to fly when his father Daedalus glued wings to his back.
Once given the power fly, Icarus was too excited by his new ability and flew too close to
the sun, melting his wings and falling to his doom. Within the IS literature, the Icarus
Paradox has been used to understand the negative consequences resulting from managers
having access to extensive information on business processes and operations as a result of
advance enterprise systems. Pinsonneault and Rivard (1998) found that managers with
access to these systems tended to overinvest in the informational component of their role
as managers while neglecting other managerial roles (Pinsonneault and Rivard 1998).
Our data suggests that sellers with access to the online auctions at eBay motors may tend
to overinvest in the search for a high-valuation buyer while neglecting their key goal of
optimizing the overall sales process. As a result, while the channel remains useful as a
way for sellers to identify buyers, the overall efficiency of the channel is much lower than
it could be if sellers engaged in a reservation price strategy as suggested by Ashenfelter et
al. (2002).
The relationship between the number of auctions and the resulting price paid was
unexpectedly positive. This can also be understood by graphing the same search duration
for auctions of different lengths—a shorter auction length will yield a larger number of
- 106 -
auctions for the same auction length, as shown in Figure 3.4. One possible explanation
for this effect is the widely observed bidding pattern known as sniping—in which greater
than 80% of the bids occur in the final minute of the auction (Bapna 2003; Ockenfels and
Roth 2001; Roth and Ockenfels 2000). The positive relationship between the number of
auctions and the resulting price premium suggests that sellers may obtain more benefits
from these additional periods of sniping activity than from additional auction days,
making shorter auctions preferable. A second possible explanation is that because the
seller can be shown to reduce her reserve price with time, those sellers utilizing shorter
auctions make smaller adjustments in their reserve price. These small, targeted
reductions thereby enable sellers to extract additional surplus from buyers. In sum,
though we cannot provide a definitive reason for the relationship between price premium
and the number of auctions without an experiment, this research suggests that sellers
wishing to optimize inventory management should consider multiple shorter auctions.
An alternative explanation of the relationships observed can be considered from
the perspective of the buyer. The buyer also has access to information related to the
completion of prior auctions. This could result in learning whereby buyers access the
price they are willing to pay based on the outcomes of prior auctions. Observing prior
auctions which did not end in a sale may lead the buyer to fear winners curse (Bajari and
Hortacsu 2002; Kagel and Levin 2002; Mehta and Lee 1999) or to expect that a seller's
motivations to sell an item may increase. The interpretation of the results in this fashion,
however, does not influence the overall understanding of the relationships as interpreted
from the perspective of the seller.
Implications for Theory
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Numerous papers have examined the design characteristics and outcomes of
auctions, but relatively few have expanded the theoretical lens to include marketplace
characteristics outside of the focal unit of the individual auction. This research suggests
that there are important extensions to prior work which can be made by examining how
sellers actually use online auction channels, and how auctions are influenced by both the
overall sales process in which they occur and by external factors. In addition, this
research has identified basic strategies that sellers employ in the use of the online auction
channel. In doing so, it opens the door for researchers to examine the complex strategies
sellers.
Implications for Practice
Our findings indicate that the low search costs of the Internet may entice sellers to
search as a way of gathering information that actually may be freely—though not
conveniently—available. Sellers have numerous resources available for pricing vehicles
in the offline and wholesale market. Blue book and black book pricing guides have been
computerized to enable the seller to have nearly instant access to the expected market
value of vehicles. The same is not true for eBay Motors. A quick analysis of the eBay
Motors market indicates that the clearing price for vehicles is frequently somewhere
between the wholesale price and the trade-in value. Although dealers using eBay motors
may have access to prior closed auctions, there is no widely utilized way of determining
the expected clearing price for a given auction or for individual electronic channels.
This suggests that there may be a need for tools which help sellers to understand
the expected market clearing price depending on the channel used to attract the buyer.
Rather than general valuations, these channel-specific valuations systems would help the
- 108 -
seller to optimize investments in different online and offline channels. In addition, the
tool could help the seller to be realistic about pricing decisions by informing the seller of
the probability of sale at different reserve/listing prices for different channels. With a
more complete understanding of the likelihood of sale at different prices, the seller may
be less likely to overinvest in search, thus making the overall market more efficient.
An additional implication for market makers is related to the listing fees. This
research indicates that many sellers tend to use multiple auctions as a way to gather
information about buyers' willingness to pay. Currently, leading market makers such as
eBay offer fixed listing fees for sellers independent of the length of the auction or
whether the item has been offered previously. It appears from the findings here that
retailers may desire additional flexibility to match their specific search preferences. For
instance, some sellers may prefer an "auction bundle" of three three-day auctions to a
single ten-day auction. Thus, the market maker could extract additional surplus by giving
sellers the option to relist their product if it does not sell. An auction bundle may
increase the ability of sellers to adjust their reserve price rapidly to the level that matches
buyers' willingness to pay while increasing the amount of bidding activity.
Limitations
Before concluding it is important to acknowledge the limitations of this work.
There were several limitations specifically associated with the data collected. First, as a
measure of SOC we collected the number of records return by a search from a leading
search engine. It is possible that this method may systematically omit results from
specific types of sites—i.e., those that do not list the VIN number or have implemented
methods which prevent indexing. Further, it is also possible that the values captured for
- 109 -
SOC could represent other sellers listing the vehicle. For example, it is possible that a
dealer could list a vehicle online, sell the vehicle at a wholesale auction, and then a
second seller could offer the vehicle over eBay. This situation, however, would likely
only reduce overall effect of SOC on performance. Thus, the presence of a significant
effect suggests that this may happen infrequently.
A second limitation in measurement is related to the fact that we were unable to
capture transactions initiated via the online auction but are completed offline. For
example, when a reservation price is not met for a sale, a seller could contact a buyer
offline in an effort to negotiate a price verbally. As this analysis primarily focused on
those vehicles which had been sold through the online channel, these vehicles would have
been omitted from the statistical analysis. It is expected that the strategies of sellers
would hold in this situation.
Finally, because our analysis captured auctions over a period of three months, it is
possible that vehicles which where eventually sold were omitted from the analysis. This
selection effect is necessary—the data analysis must end at some point. While the
selection of vehicles sold within a specific time period could slightly reduce the mean
level of search, as sellers searching for an extended period of time would be more likely
to be eliminated from the actions, it is unlikely that this would have an influence on the
relationships observed. Overall, the number of vehicles omitted because of timing would
be quite small compared to the number of vehicles sold.
3.7 SUMMARY
In sum, this research provides a framework for understanding how sellers actually
use the online auction channel as part of the overall sales process. In doing so, we both
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identify relevant antecedents to search duration and the resulting relationship between
search duration and the price premium obtained by the seller. By the incorporation of a
longitudinal component to the study of auction outcomes for the same product, we obtain
surprising results which can directly inform both theory and practice.
- 111 -
3.8 FIGURES
FIGURE 3.1 - CONCEPTUAL FRAMEWORK
FIGURE 3.2 - RESEARCH MODEL
- 112 -
FIGURE 3.3 - PLOT OF PRICE PREMIUM VS. DURATION
750
500
4 5 6 7 8 9 10 11 12 13 14 15 16 17
Duration
Premium
FIGURE 3.4 - PLOT OF PRICE PREMIUM VS. DURATION FOR DIFFERENT
AUCTION LENGTHS
900
700
500
4 5 6 7 8 9 10
Duration
10 Day Auctions 3 Day Auctions
- 113 -
P
r
e
m
i
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3.9 TABLES
TABLE 3.1 - DESCRIPTIVE STATISTICS (ALL AUCTIONS ENDING IN SALE)
Variable
Starting Bid (thousand dollars)
log (Positive Feedback) (count)
log (Negative Feedback) (count)
New (yes/no)
log (Miles)
Existing Warranty (yes/no)
Seller Warranty (yes/no)
log (Total Days)
log (Auctions) (count)
Price (thousand dollars)
Expected Offer Variance (thousand dollars)
Expected Offer Mean (thousand dollars)
log (Search Other Channels) (count)
log (Vehicles Sold) (count)
log (Bids) (count)
log (Price Premium) (thousand dollars)
Mean
3.965
4.469
0.906
0.011
10.731
0.263
0.600
2.189
0.232
16.462
0.985
14.618
0.195
0.201
2.846
1.595
Std
8.067
1.765
0.969
0.106
1.287
0.441
0.490
0.562
0.420
10.970
1.080
10.337
0.413
0.406
0.892
0.603
- 114 -
TABLE 3.2 - CORRELATION MATRIX (ALL AUCTIONS ENDING IN SALE)
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Starting Bid 1.000
log (Positive
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Feedback)
log (Negative
Feedback)
New
log (Miles)
Existing Warranty
Seller Warranty
log (Total Days)
log (Auctions)
Price
EO Mean
EO Variance
log (SOC)
log (Vehicles Sold)
log (Bids)
log (Price Premium)
-0.204 1.000
-0.119 0.647 1.000
0.142 -0.130-0.081 1.000
-0.306 0.195 0.153 -0.614 1.000
0.259 -0.160-0.162 0.175 -0.484 1.000
-0.186 0.162 0.113 -0.131 0.306 -0.732 1.000
-0.016 0.145 0.025 -0.031 0.036 0.022 -0.012 1.000
0.010 0.117 0.039 -0.020 0.042 -0.004-0.003 0.712 1.000
0.479 -0.097-0.107 0.158 -0.460 0.475 -0.336 0.048 0.017 1.000
0.356 -0.099-0.115 0.239 -0.559 0.456 -0.385 0.038 0.015 0.793 1.000
0.641 -0.109-0.105 0.257 -0.522 0.451 -0.323 0.044 0.027 0.934 0.814 1.000
0.061 0.000 -0.020-0.023-0.083 0.147 -0.065 0.069 0.038 0.202 0.153 0.169 1.000
-0.135 0.210 0.102 -0.038 0.136 -0.127 0.111 0.582 0.476 -0.118-0.104-0.109-0.019 1.000
-0.579 0.213 0.110 -0.046 0.100 -0.069 0.066 0.000 -0.026-0.055-0.046-0.186-0.003 0.125 1.000
0.081 -0.038-0.002 0.157 -0.220 0.051 -0.051 0.002 -0.032 0.433 0.316 0.249 0.079 -0.098 0.068 1.000
- 115 -
TABLE 3.3 - REGRESSION ANALYSIS OF FINAL SALE PRICE (USED IN
CALCULATION OF PRICE PREMIUM)
Variable
1
Sale Price
Dependant
log (Positive Feedback) 0.159***(0.039)
log (Negative Feedback) -0.282***(0.070)
Starting bid
log(Miles)
Existing Warranty
Seller Warranty
New
R-Squared
N
0.086**(0.007) -
0.652**(0.059)
2.990***(0.202)
1.097***(0.160)
-1.806*(0.821)
0.898
5,127
1
Note: *1p<0.05 **P<0.001 ***p<0.0001
Dummy variables for vehicle model not shown.
2
Analysis includes all auctions ending in sale (i.e., the reserve price met)
TABLE 3.4 - REGRESSION ANALYSIS OF EO MEAN AND EO VARIANCE
Variable
1
EO Mean EO Variance
Dependant Dependant
log (Positive Feedback) 0.261***(0.036) 0.023**(0.008)
log (Negative Feedback) -0.159*(0.081) -0.033 (0.019)
Starting bid
log(Miles)
Existing Warranty
Seller Warranty
New
0.332***(0.005) -0.002*(0.001) -
1.507***(0.065) -0.307***(0.015)
0.723**(0.213) -0.186**(0.049)
0.652**(0.202) -0.199***(0.047)
8.703***(0.615) -0.570***(0.142)
R-Squared 0.662 0.226
N 31,445
2
31,445
2
Note: *1p<0.05 **P<0.001 ***p<0.0001
Dummy variables for vehicle model not shown
2
Analysis includes all auctions (i.e., those in which the reserve price was met and
those in which it was not met)
- 116 -
TABLE 3.5 - 3SLS ANALYSIS OF DURATION OF SEARCH AND PRICE
PREMIUM
ln (Price
ln Duration
Variable
Vehicles Sold
Dependant
0.494***(0.015)
Premium)
Dependant
Search in Other Channels 0.065***(0.013) 0.073** (0.020)
Expected Offer Mean
0.002 (0.001)
+
0.001 (0.001)
Expected Offer Variance
Number of Auctions
Duration of Search
R-Squared
N
Note: * p<0.05 **P<0.001
0.017*(0.008) 0.170***(0.013)
0.651***(0.013) 0.137**(0.043)
-0.233***(0.045)
0.423
5127
***p<0.0001
- 117 -
APPENDICES
APPENDIX A - ESSAY 1 MEASURES
Strategic Change Orientation
Technological Opportunism (Srinivasan et al. 2002)
(1 = strongly disagree; 7 = strongly agree)
1. We are often one of the first in our industry to find new technology that may
potentially affect our business.
2. We are always on the lookout for information on new technology for our
business.
3. We periodically measure how changes in technology affect our business.
4. The top management of the dealership has a strong emphasis on technological
innovation.
Market Orientation (Navar and Slater 1990)
(1 = strongly disagree; 7 = strongly agree)
1. Our competitive advantage is based on understanding and meeting our customers'
needs.
2. Our managers understand how employees can provide value to customers.
3. We frequently measure customer satisfaction.
4. We pay close attention to after-sales service and maintenance.
Entrepreneurial Orientation (Covin and Slevin 1989)
(1 = strongly disagree; 7 = strongly agree)
1. We are quick to respond to significant changes in our competitors' pricing
structures.
- 118 -
2. We are very often the first business to introduce new services to customers.
3. In dealing with competitors, we typically adopt a very competitive "undo the
competitors" position.
4. Top management regularly discusses competitors' strength and weaknesses.
IT Capabilities
IT Infrastructure (Bharadwaj et al. 1998)
(1 = strongly disagree; 7 = strongly agree)
Our dealership's information technology (i.e., computers, networks, etc.)...
1. ...meets the business needs.
2. ...has an adequate number of computers with sufficient performance to meet user
needs.
3. ...is reliable and efficient.
4. ...is flexible enough to meet the business needs.
IT Management (Bharadwaj et al. 1998)
(1 = strongly disagree; 7 = strongly agree)
Our dealership's technology manager(s)...
1. ...has specifically explained our technology management practices.
2. ...effectively plans for security control, standards compliance, and disaster
recovery (loss of information, etc.).
3. ...employs the same technology policies throughout the dealership.
4. ...has established effective partnerships with technology providers (such as lead
management systems)
Climate for IT Use (Adopted from Schneider et al. (1998)
- 119 -
(1 = low; 7 = high)
How would you rate.
1. The efforts to ensure employees use customer management system(s)?
2. The recognition and rewards employees receive for using the customer
management system(s)?
3. The leadership shown by management in supporting the use of customer
management system(s)?
4. The effectiveness of technology hardware, training, and other resources provided
to promote the use of customer management system(s)?
Mindfulness of IT Adoption (Adopted from Knight (2004))
(1 = strongly disagree; 7 = strongly agree)
Our senior managers...
1. ...believe technology will help the dealership to serve customers better.
2. ...believe technology will create a significant competitive advantage to our
dealership.
3. ...take choices of whether we adopt a new technology very seriously.
4. ...show great care in identifying and selecting technologies to adopt.
5. ...are results-focused with deciding whether to adopt new technologies.
6. ...seek outside expertise whenever making decisions related to types of
technology they are not familiar with.
Assimilation of Customer Relationship Management
Automation
Scale adopted from Ray et al. (2005)
- 120 -
Please describe the extent of adoption of automated sales processes
(0=Don't Intend to Implement, 1= Not yet begun, 3 = Standard Implementation, 5 =
Advanced Implementation)
Automation - Sales Business Unit
1. Automated regular email communications with leads.
2. Automated scheduling of tasks for members of the sales force.
3. Automated assignment of incoming leads to the appropriate person.
4. Automated tracking of responses to marketing promotions (from mailings and
email).
Automation - Service Business Unit
1. Automated regular email communications with customers.
2. Automated creation of personalized service reminders.
3. Automated marketing and follow-up with customers across multiple channels
(mailing, email, etc.).
4. Automated tracking of responses to marketing promotions (from mailings and
email).
CRM System Use
(1=Not used at all; 7=Used very extensively)
CRM System Use - Sales Area
Our dealership uses customer management system(s) to...
1. ...record interactions with customers (i.e., phone calls, customer needs).
2. ...schedule follow-up with customers (through phone calls, personal email).
3. ...understand the overall state of the sales process (i.e., total leads, lead status).
- 121 -
4. ...review and report ROI (return on investment) by lead source.
CRM System Use - Service Area
Our dealership uses customer management system(s) to...
1. ...record interactions with customers (i.e., service visits, direct marketing
materials).
2. ...categorize customers to identify those which are most valuable to the
dealership.
3. ...measure response rates to marketing promotions.
4. ...assess the lifetime value of our customers.
Process Performance
(Please describe the extent customer management systems have affected.)
(1 = created no value; 7 = created significant value)
Process Performance - Sales Area
1. .the level of service provided to customers.
2. .the productivity of salespersons.
3. .the effective management of inventory.
Process Performance - Service Area
1. .the utilization of the service area (i.e., the percentage of capacity used).
2. .the effectiveness of your service and maintenance promotions.
3. .the value we obtain from existing customer relationships.
Financial Performance (Wall et al. 2004)
(1 = Much worse than competitors; 7 = Much better than competitors)
Sales Area Performance - CONTROL
- 122 -
1992-1994—Vehicle sales—Sales growth
1992-1994—Vehicle sales—Profit Level and ROI
Service Area Performance - CONTROL
1992-1994—Parts and Service Area—Sales growth
1992-1994—Parts and Service Area—Profit Level and ROI
Sales Area Performance
2002-2004—Vehicle sales—Sales growth
2002-2004—Vehicle sales—Profit Level and ROI
Service Area Performance
2002-2004—Parts and Service Area—Sales growth
2002-2004—Parts and Service Area—Profit Level and ROI
- 123 -
APPENDIX B - ESSAY 2 MEASURES
Informational Capabilities
1. Lists price on website
2. Lists options on website
3. Lists all new vehicles on website
4. Lists photo on website
Transactional Capabilities
1. Links to independent sites
2. Tool for credit application
3. Price quote request form
4. Online service/appointment scheduling
Relational Capabilities
1. Count of the number of infomediary partnerships
2. Investment in infomediary partnerships ($/month)
3. Leads from Infomediary partners
Sales Volume
1. Total vehicle sales per month
Complementary Business Process Change
1. Are leads distributed to specific sales people who don't take regular floor traffic?
(yes/no)
Performance
1. Monthly Profit (Total sales from Internet Channel x average profit for Internet
channel)
- 124 -
Controls
1. Online discount (Offline profit - online profit)
2. Sales volume (total vehicle sales per month)
3. Domestic (count of number of domestic brands carried)
4. Luxury (count of number of luxury brands carried)
- 125 -
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