Description
Supply chain management (SCM) is the management of an interconnected or interlinked between network, channel and node businesses involved in the provision of product and service packages required by the end customers in a supply chain.
ABSTRACT
Title of Document:
SUPPLY CHAIN STRATEGY AND THE BENEFITS OF INFORMATION EXCHANGE
Tobin Edward Porterfield, Ph.D., 2007 Directed By: Professors Philip T. Evers, Associate Professor, Department of Logistics, Business, and Public Policy and Joseph P. Bailey, Research Associate Professor, Department of Information and Technology
Abstract: This dissertation investigates the use of information exchange in industrial supply chain relationships. Specific information exchange characteristics are analyzed to determine their contribution to firm performance from the perspective of both the technology champion firm and the trading partner firm. Longitudinal analyses are conducted using data gathered from an electronically mediated industrial exchange network. This unique dataset, which includes information exchange data for thirty-nine technology champion firms and their electronically integrated trading partners across a two-year observation period, provides distinct insights into the application and outcomes related to information exchange in contemporary supply chains. The analysis of this large volume of information exchange transactions identifies best practices in the use of information exchange and their impact on firm performance.
SUPPLY CHAIN STRATEGY AND THE BENEFITS OF INFORMATION EXCHANGE
By
Tobin Edward Porterfield
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2007
Advisory Committee: Professor Philip T. Evers, Co-chair Professor Joseph P. Bailey, Co-chair Professor Thomas M. Corsi Professor Martin Dresner Professor Jennifer Preece Professor Ping Wang
© Copyright by Tobin Edward Porterfield 2007
Dedication
To my wife Sue and my sons, Josh and Nick, whose love and support allowed me to keep my eye on the prize and finish this race.
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Acknowledgements
This dissertation is a result of the guidance, support, and encouragement of many faculty, students, family, and friends. My advisory committee including Professors Joe Bailey, Phil Evers, Martin Dresner, Tom Corsi, Jenny Preece, and Ping Wang provided much needed direction throughout this process. I am so grateful for their interest and inspiration in bringing this together. A special thanks to Joe Bailey and Phil Evers for their critical roles as co-chairs of my committee. Their knowledge and patience pushed me beyond my perceived abilities. Professor Bailey mentored me starting in my first semester at the University of Maryland and spent countless hours investing his time in my training. I am certain that this dissertation is only the beginning of our work together. Martin Dresner is a constant guide and friend. From our meeting years before entering the Ph.D. program and straight on through, Professor Dresner has shown a sincere interest in my work and my life. His honest critique and steady guidance have been essential ingredients in my completion of this dissertation. I hope that in my career I can connect with my students the way that Professor Dresner does. I would like to thank my colleagues in the program for providing feedback on early drafts of this research and their incredible team spirit which made the process bearable. Thank you all for your friendship and support. A special thanks to my dearest friends Adriana and Christian Hofer for their patient coaching and enduring camaraderie.
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Brian Lowell from the University of Maryland University College has been a invaluable resource in helping me understand the nuances of electronic information exchange. My family and friends were a source of constant love and support – believing in my dreams when I lost sight of my every aspiration. Words cannot express my appreciation for all that my wife, Sue, has given to me throughout this process. From covering my home responsibilities, to supporting us, encouraging me, keeping our family together, and editing skills that make me the envy of my colleagues. And a very special thanks to my friend Bob Graham for his relentless interest in my research and many hours of editing. The results of he and Sue’s editing skills are the best of the pages that follow – any errors or omissions are all mine.
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Table of Contents
Dedication .......................................................................................................................... ii Acknowledgements .......................................................................................................... iii Table of Contents .............................................................................................................. v List of Tables .................................................................................................................. viii List of Figures................................................................................................................... ix Chapter 1: Introduction ................................................................................................... 1 1.1 Information Exchange in the Supply Chain.............................................................. 1 1.2 Strategic Use of Supply Chain Relationships ........................................................... 4 1.3 Contribution of the Dissertation................................................................................ 6 Chapter 2: Theory and Review of Extant Literature.................................................... 9 2.1 Introduction............................................................................................................... 9 2.2 Transaction Cost Theory in a Supply Chain Context ............................................... 9 2.2.1 The Effect of Information Technology on the TCT Framework............... 15 2.2.2 The Effect of Information Exchange on the TCT Framework .................. 17 2.3 Review of Empirical Literature .............................................................................. 20 2.3.1 Use of Information in Supply Chains ........................................................... 20 2.3.2 Information Exchange to Support Specific Supply Chain Initiatives ....... 22 2.3.3 Using Interorganizational Systems to Exchange Information ................... 23 2.4 Research Model Development................................................................................ 24 Chapter 3: Research Setting and Data.......................................................................... 33 3.1 Use of Electronic Data Interchange Data for Empirical Research ......................... 33 3.2 Formatting Standards and Electronic Intermediaries.............................................. 37 3.2.1 EDI Exchange Networks ............................................................................... 38 3.2.2 Establishing and Maintaining an Electronic Network ............................... 38 3.3 Units of Observation ............................................................................................... 41 3.3.1 Firms in the Network ..................................................................................... 42 3.3.2 Key Constructs ............................................................................................... 46 3.4 Descriptive Statistics............................................................................................... 49 3.4.1 Descriptive Statistics of the Exchange Network .......................................... 49 3.4.2 Descriptive Statistics of the Supply Chain Echelons .................................. 50 3.5 Research Question .................................................................................................. 56 Chapter 4: Trading Partner Relationships................................................................... 58 4.1 Introduction............................................................................................................. 58 v
4.2 Development of Hypotheses................................................................................... 60 4.3 Research Methodology ........................................................................................... 66 4.3.1 Data ................................................................................................................. 66 4.3.2 Measures ......................................................................................................... 67 4.3.3 Modeling Event History ................................................................................ 72 4.3.4 Logistic Regression Modeling ....................................................................... 73 4.3.5 Cox Proportional Hazards Model ................................................................ 77 4.4 Results..................................................................................................................... 79 4.4.1 Descriptive Statistics ...................................................................................... 79 4.4.2 Logistic Regression Results ........................................................................... 83 4.4.3 Logistic Regression Results – Stratified Dataset ......................................... 85 4.4.4 Cox Proportional Hazards Model Results ................................................... 88 4.4.5 Cox Proportional Hazards Model Results – Stratified Dataset ................. 89 4.5 Discussion ............................................................................................................... 91 4.5.1 Evaluation of the Non-linear Specification .................................................. 94 4.5.2 Model Predictive Power ................................................................................ 98 4.5.3 Sensitivity Analysis ...................................................................................... 100 4.5.4 Potential Alternate Hazard Measure ......................................................... 100 4.6 Conclusion ............................................................................................................ 102 Chapter 5: Supply Chain Performance ...................................................................... 103 5.1 Introduction........................................................................................................... 103 5.2 Development of Hypotheses................................................................................. 104 5.3 Research Methodology ......................................................................................... 109 5.3.1 Data ............................................................................................................... 109 5.3.2 Measures ....................................................................................................... 111 5.3.3 Panel Data Analysis ..................................................................................... 119 5.3.4 Descriptive Statistics .................................................................................... 121 5.4 Results................................................................................................................... 126 5.5 Discussion ............................................................................................................. 128 5.5.1 Alternative Explanatory Variables ............................................................ 135 5.5.2 Alternative Lagged Explanatory Variables ............................................... 137 5.5.3 Alternative Dependent Variables ............................................................... 139 5.5.4 Additional Future Research Notes ............................................................. 140 5.6 Conclusion ............................................................................................................ 142 Chapter 6: Conclusion.................................................................................................. 143 6.1 Contribution .......................................................................................................... 143 6.2 Managerial Implications ....................................................................................... 144 6.3 Limitations and Future Research .......................................................................... 146 Appendix........................................................................................................................ 148 APPENDIX A Sample ANSI X12 and EDIFACT Message Types ........................... 149 APPENDIX B Transactional Information Types ....................................................... 150 APPENDIX C Enhanced Transaction Types.............................................................. 151 APPENDIX D Logistic Regression Results ............................................................... 153
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APPENDIX E Hazard Model Results......................................................................... 158 APPENDIX F Calculating Sales Surprise .................................................................. 163 APPENDIX G Alternative Dependent Variable Results ............................................ 168 References...................................................................................................................... 171
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List of Tables
Table 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 5.1 5.2 5.3 5.4 5.5 5.6 A.1 A.2 B.1 C.1 D.1 D.2 D.3 D.4 E.1 E.2 E.3 E.4 F.1 G.1 G.2 G.3 Description EDI Literature Review Sample of Trading Partner Network Data Network Descriptive Statistics Termination Rates by Firm Definitions of Logistic Regression Model Variables Definitions of Cox Proportional Hazard Model Variables Descriptive Statistics Pairwise Correlations Logistic Regression Results Logistic Regression Results: Stratified by Echelon Cox Proportional Hazards Model Results Cox Proportional Hazards Model Results: Stratified by Echelon Logistic Regression Results for the Linear Model Hazards Model Results for the Linear Model Logistic Regression of Interaction Model Classification Table for Estimation Sample Classification Table for Holdout Sample Results of Alternate Hazard Specification Descriptive Statistics of Unlogged Variables Pairwise Correlations Variance Inflation Factor Results Coefficient Estimates: Full Network Model Coefficient Estimates: Echelon Model Alternate Lagged Model ANSI X12 Message Types and Descriptions United Nations EDIFACT Message Types and Descriptions Transactional Information EDI Types Enhanced Information EDI Types Full Results of Logistic Termination Study Stratified Logistic Results: Manufacturers Stratified Logistic Results: Wholesalers Stratified Logistic Results: Retailers Full Hazard Model Results Stratified Hazard Model Results: Manufacturers Stratified Hazard Model Results: Wholesalers Stratified Hazard Model Results: Retailers Forecast Accuracy by Forecast Method Alternative Dependent Variable: Net Income Alternative Dependent Variable: Receivables Turnover Correlation Matrix for Alternate Dependent Variables Page 34 45 50 55 75 78 80 82 84 86 88 90 95 96 97 99 99 101 121 125 126 128 130 138 149 149 150 152 154 155 156 157 159 160 161 162 167 169 170 170
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List of Figures
Figure 2.1 2.2 3.1 3.2 4.1 Description Information Technology and the TCT Framework Information Exchange Matrix Information Exchange Directionality Trading Partner Relationships by Information Exchange Quadrant Information Exchange Hypotheses Page 10 29 47 52 65
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Chapter 1: Introduction
1.1 Information Exchange in the Supply Chain The cost-effective, timely, and accurate exchange of information allows today’s supply chains to efficiently deliver innovative solutions. Using the latest technology and supply chain strategies, a consumer in the U.S. can order a laptop computer online direct from the manufacturer. The manufacturer is able to custom build the laptop in Malaysia, coordinate the manufacture of the docking station and monitor stand in China, and have the entire order delivered within six days. In order to efficiently deliver this level of service to customers, multiple manufacturers and logistics providers are coordinated into a single virtual business entity. A key component of any business’s success is its ability to balance the costs and benefits of maintaining relationships with their trading partners. Exchanging information with trading partners is a central element in maintaining relationships. The cost of exchanging information includes the technology infrastructure as well as the gathering and formatting of the data. Sharing the wrong information with the wrong trading chain partner may cost the firm its competitive advantage and allow opportunistic actions by customers, suppliers, and competitors. Sharing the right information at the right time with the right trading chain partners can reduce costs and enhance competitive advantage. Not all supply chain relationships are equal and each requires unique amounts and types of information. Some business-to-business (B2B) exchanges are defined by closely integrated seamless relationships while others are defined by arms-length market relationships. Simple arms-length market relationships may require only the most basic
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exchange of cost and quantity information. These loose relationships exist when the buyer requests a specific catalog item, quantity, and delivery date. The supplier responds with the cost, item availability, and payment terms. The exchange is coordinated based on a minimal exchange of information to support the order cycle process. Other more tightly integrated relationships may require ongoing real-time exchanges of production plans, point-of-sale demand data, inventory quantities, and shipping schedules. The exchange of additional information allows for the coordination of production and logistic functions above and beyond the order cycle process. Information exchange that is tailored to meet the needs of the relationship benefits performance by getting the right information to the right trading partner at the right time in order to support interfirm decision making. Supply chain relationships differ in their characteristics as well as their outcomes. The degree of trust, level of commitment, use of shared knowledge, access to systems and information, and use of shared goals vary among B2B supply chain relationships. Most firms do not build close supply chain relationships just for the sake of integration; they develop close relationships for the positive outcomes that are created. For example, when interviewing the regional sales manager for a large cable manufacturer, he cited specific outcomes from building close relationships with customers. Both the firm and its customers experienced benefits from the relationship. Its customers experienced positive supply chain performance outcomes, including reductions in average inventory, increased inventory availability, access to R&D resources, greater flexibility to adjust order quantities inside manufacturing lead time, and access to enhanced emergency response services. The cable manufacturer benefited from increased flexibility in managing its
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order backlog, improved accuracy of forward production planning, better cost control through level production loading, and the avoidance of switching costs. This manufacturer noted that these benefits become possible when information flows seamlessly between organizations within the context of close supply chain relationships. Firms can identify the unique types and volumes of information exchange necessary to build and maintain each relationship. At a basic relational level, procurement and sales personnel from two distinct firms coordinate quantity, price, payment terms, and delivery information to support the exchange of products. At the other end of the relational spectrum, marketing and engineering personnel in distinct firms may exchange complex information to coordinate joint R&D efforts for the development of next generation products and services. In both situations, firms make decisions about the types of information that are exchanged in order to efficiently and effectively coordinate their resources. Direct modes of information exchange such as face-to-face, teleconferencing, and telephone contacts allow for immediate feedback among the parties. Due to the interactive nature of these modes, the exchange parties need to be available at the same time in order to coordinate the information exchange. Scheduling constraints in supply chains that extend across time zones serve to further decrease flexibility and increase the cost of using these traditional modes of information exchange. When immediate feedback and interaction are not required, information can be exchanged through email, electronic data interchange (EDI), voice mail, or web-based applications. These indirect information exchange modes support the efficient transfer of large amounts of information without both exchange parties being available at the same time. Email text 3
and attachments are sent by one firm and stored by the recipient firm until the information is needed. Although lacking in immediate feedback mechanisms, these technology-based information exchange modes have the advantage of conveying large amounts of information in a form that can be accurately stored and shared with others (Subramani 2004; Vickery et al. 2004). Firms may choose the mode of information exchange that best suits their situation. 1.2 Strategic Use of Supply Chain Relationships Increased competition makes the management of supply chain relationships a strategic issue for firms. Researchers note that supply chains exist whether they are managed or not (Mentzer et al. 2001). However, the successful management of supply chains can be a source of competitive advantage for firms (Dyer and Ouchi 1993; Houlihan 1985). Supply chain management (SCM) is described as the strategic management of individual firms as a single entity in order to bring a product or service to the market (Vickery et al. 2003). The American Production and Inventory Control Society (APICS) provides a process oriented definition of SCM: The design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally. (APICS, 2006) Collectively, these two definitions of SCM create a holistic view of SCM recognizing that the individual firms in a supply chain are distinct entities that share a common interest in operating as a single vertically integrated system. The linking of internal firm processes to external customers and suppliers is recognized as a key element in the management of supply chains. Through an empirical 4
test of five integration strategies, researchers found a correlation between firms with higher performance and their increased use of integration (Frohlich and Westbrook 2001). Their survey-based study asked firms to rate the degree of integration with both their customers and suppliers. The highest performing strategy was found to be linking closely with both customers and suppliers. In their study, information exchange was a key indicator of integrated relationships. Integrated relationships are characterized by the sharing of production plans and the use of shared information systems. Information technology (IT) facilitates the exchange of information in the supply chain. Inherent in the traditional model of a supply chain are the flow of products toward the end customer and the flow of information toward the raw materials suppliers. In its most basic form, information only flows upstream from the end customer toward the raw materials supplier. This type of information flow often consists of only basic order information that informs the supplier to ship a specific item to the requesting customer. Today’s advanced IT capabilities support the fast and efficient flow of large volumes of diverse information both upstream and downstream in the supply chain. To enhance the coordination of the supply chain among customers and suppliers, firms often share inventory information, quality reports, demand forecasts, production schedules, and marketing research with both customers and suppliers. These bi-directional flows of information go against the traditional model of the supply chain information flow but are recognized in both academic literature and business practice (Spekman et al. 1998). Technological advances enhance the ability of firms to gather, store, and transfer information. The ability of a firm to use IT is recognized as a source of firm performance
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advantage (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer 2002). Beyond the mere presence of IT, researchers find that technology facilitates the exchange of information between trading partners (Spekman et al. 1998). Recognizing the role of IT in facilitating supply chain integration, IT has been modeled as an antecedent of supply chain integration (Vickery et al. 2003). IT connections and integration strategies create linkages among firms in the supply chain, but linkages alone do not integrate firms. Moving information seamlessly between a company and its trading partners facilitates the strategic integration that coordinates firms in the supply chain (Mukhopadhyay et al. 1995). Specifically, information is most useful in a supply chain context when it is timely, accurate, and relevant to decision making (Bakos and Brynjolfsson 1993; Whipple et al. 2002). The exchange of information across these interfirm linkages integrates the firms and allows them to perform as a single virtual organization. 1.3 Contribution of the Dissertation This dissertation addresses existing gaps in the literature by examining the effect of information exchange on supply chain performance. Specifically, this dissertation examines IT enabled information exchange between firms in the supply chain. This dissertation makes four unique and significant contributions related to the use of information exchange in supply chains:
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This dissertation extends the theoretical link between information exchange and supply chain participant performance. Prior research recognizes the role of information exchange in supply chains but has been 6
limited in its measurement. These analyses use a unique archival dataset from an electronically-mediated industrial exchange network to develop measures of actual information exchanges in B2B relationships.
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This dissertation expands the growing body of literature on supply chain management by maintaining a perspective that performance is associated with the effective management of interfirm relationships. The exchange of information with trading partners is specifically advanced as a key element of interfirm relationships that has not been fully developed in the literature.
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This dissertation identifies dimensions of information exchange that contribute to the relational performance of firms in the supply chain through the use of dyadic observations of B2B interactions. Using event history analysis tools and performance measures from strategic management literature, the association between information exchange characteristics and relationship termination is developed. U-shaped relationships are identified where the effects of information exchange characteristics are negative at lower volume levels and positive at higher volumes.
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This dissertation identifies dimensions of information exchange that contribute to the operational performance of firms in the supply chain. A panel dataset of thirty-nine technology champion firms is used for
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hypotheses testing. Findings suggest that close trading partner relationships may be detrimental to performance. The remainder of this dissertation is structured as follows: Chapter 2 discusses and applies relevant theory, Chapter 3 presents the methodology and data, Chapter 4 identifies and tests hypotheses related to relationship termination, Chapter 5 identifies and tests hypotheses related to firm operational performance, and Chapter 6 offers conclusions and directions for future research.
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Chapter 2: Theory and Review of Extant Literature
2.1 Introduction This chapter uses transaction cost theory (TCT) to build a theoretical approach to understanding the effects of information exchange within electronically-mediated supply chain relationships. This unique investigation into the role of information exchange in supply chain relationships has both academic and practical application. TCT is an appropriate theoretical lens for supply chain research because it can be used to evaluate the effectiveness of buyer-supplier relationships (Grover and Malhotra 2003). TCT identifies specific characteristics of interfirm exchanges and assumptions about firm behavior to guide a firm’s choice of relationship governance structure. Simply put, TCT recognizes that there are attributes of business exchanges which may require firms to manage the relationship more carefully in order to avoid unfavorable outcomes. The chapter proceeds with an overview of the key elements of TCT that apply to this dissertation and an introduction of extant literature to form a framework for research into the use of information exchange in supply chain relationships. 2.2 Transaction Cost Theory in a Supply Chain Context TCT proposes that firms decide strategically whether to make their inputs or purchase their inputs from the market (Williamson 1975; Williamson 1985). Transaction costs are incurred when firms procure inputs from outside sources. These transaction costs include searching for a source of supply, negotiating, coordinating the exchange, and monitoring the transaction.
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The decision of whether to make or buy inputs was initially described as a dichotomous choice of either market or hierarchy (Williamson 1975). This dichotomy was later expanded as a continuum of governance structures, recognizing that market characteristics and hierarchy characteristics could be blended in forming a continuum of business relationships (Webster 1992). The space between the dichotomous choices of market and hierarchy was described as “hybrid” by Williamson (1985) and is depicted as supply chain relationships in Figure 2.1. The hybrid space recognizes that firms can organize their external transactions using non-price mechanisms which create unique governance structures ranging from closely connected relationships to arms-length market transactions (Williamson 1985). Figure 2.1 Information Technology and the TCT Framework
Market
Supply Chain Relationships
Hierarchy
Electronic Markets Hypothesis
Vendors to Partners
Move to the Middle
The continuum between pure markets and internal hierarchies has been used to describe the nature of supply chain relationships. At the far left extreme of the supply
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chain relationship continuum, the relationship is typically an arms-length or “marketlike” relationship between a supplier and its customer. These exchanges differ from a pure market exchange because the exchange is not discrete but occurs repeatedly between the same two participants such that a relationship exists through an expectation of future exchanges. This type of relationship may exist when commodities are repeatedly exchanged and the relationship is primarily governed by price mechanisms. At the other extreme -- corresponding to the right-hand “hierarchy” end of the continuum -- are close relationships where suppliers tightly integrate with their customers. The earlier example of a cable manufacturer that works closely with select customers to coordinate production and demand would be an example of a relationship on the hierarchy end of the supply chain relationship continuum. At the far right of the continuum are the processes that have been moved into the firm’s hierarchy and are no longer external supply chain relationships but instead are vertically integrated internal processes. Within the continuum in the middle are supply chain relationships characterized by varying levels of integration. TCT posits that the decision of how to manage relationships is determined by factors of the exchange transaction which include three dimensions and two assumptions. The three dimensions of exchange identified by TCT are: the frequency of exchange, the amount of uncertainty, and the degree of asset specificity (Waldman and Jensen 1998). The two assumptions are bounded rationality and opportunism. The frequency of exchange recognizes that how often firms transact to acquire a given input affects the overall cost of transacting. If an input is only required occasionally, the firm may choose to use the market. If, however, the input is needed 11
often, the cost of repeatedly renegotiating procurement from the market will increase aggregate transaction costs. While discrete purchases may be transacted through the market, when multiple transactions are required to supply an input, firms will tend to move toward internalizing the transaction to reduce their overall cost (Williamson 1985). In practice, firms move away from discrete pure market transactions and create blanket purchase orders that govern the exchange for a specific period of time. Uncertainty in exchange relationships is often a result of coordination problems between exchange partners. When one party is unsure of the plans and intentions of its exchange partner, slow or inappropriate decisions can increase costs. In practice, this can occur when items that are ordered are not delivered when expected. The customer is unsure why the ordered items were not delivered and may subsequently decide to order the items from another source rather than waiting for the initial order to be delivered. Asset specificity addresses the ease with which firms can reallocate assets to other relationships. If assets are dedicated to supporting a specific relationship and cannot be reallocated to a new relationship, these transaction-specific investments can be the source of lock-in or hold-up (Shapiro and Varian 1998). When a specialized asset is allocated in a buyer-seller relationship, the party holding the specialized asset has few options to protect the loss of that asset beyond maintaining the existing relationship. Difficult and lengthy contract negotiations, cost concessions, and ongoing monitoring requirements needed to protect the specialized asset create additional transaction costs. In practice, suppliers may co-locate their facilities inside of the customer’s warehouse in order to provide unique services. In such a case, the supplier incurs costs to set-up the operation which may not be transferred to another relationship if the customer decides to change 12
suppliers. An additional aspect of asset specificity occurs when a supplier develops a unique input for a customer specified need. If the customer decides that the input is no longer needed, then the supplier may have no use for the production capability, existing inventory, or unallocated R&D expenditures. TCT posits that underlying these three dimensions are two assumptions. TCT assumes that transacting parties are limited in their ability to collect and understand all issues related to the exchange (bounded rationality) and that if given the opportunity firms will behave selfishly in ways that are detrimental to the other party (opportunism). Firms incur costs to overcome bounded rationality and avoid opportunistic behavior. The writing of extensive contracts and carefully monitoring the exchange are two ways that firms protect themselves. However, as any of the three dimensions of exchange (frequency, uncertainty, and asset specificity) increase, there is a greater opportunity for firms to act opportunistically thus requiring additional transaction costs in order to reduce the risk. Increased transaction costs require a firm to choose a governance structure closer to a hierarchy structure in order to minimize the risks of the outside firm not performing as expected. When transaction costs become higher than the cost of internally producing the good, firms will seek the most efficient governance mechanism available, which may include internalizing the transaction and not exchanging with outside firms (Williamson 1975). Opportunism is a unique phenomenon in business since it affects interfirm transactions by both its occurrence and its risk of occurrence. Opportunistic behavior occurs through misleading reports, misrepresented work time, quality shirking, and
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similar actions reported regularly in the newspaper. These specific actions create costs that affect firm financial and operational performance. But opportunistic behaviors do not need to occur in order to create costs for firms. The mere risk of opportunism can cause firms to incur prevention costs (Wathne and Heide 2000). Even when the risk of opportunism is high, firms may choose to not act opportunistically. Literature has proposed that long-term orientation (Ganesan 1994), relational contracts (Ring and Ven de Ven 1992), pledges and idiosyncratic investments (Anderson and Weitz 1992), and explicit written contracts (Ring and Ven de Ven 1992) can discourage opportunism. But whether opportunism occurs or only the potential exists, costs are incurred. The availability of information affects these transaction costs within the TCT framework. Price alone effectively and efficiently coordinates pure markets. In an environment of perfect information, the price mechanism coordinates exchange with minimal transaction cost. Unfortunately, buyers and sellers do not possess perfect information and the resulting information asymmetry increases the cost of transacting through the market. On the surface, one might predict that these increased costs would prompt firms to avoid transacting with the market. In the long run, firms could develop internal capabilities to provide many of the inputs required for production. But in reality, firms always rely on some level of outside input either because of technical expertise, availability of resources, or economies of scale (Pfeffer and Salancik 1978). So in the short run, and often in the long run, firms rely on outside sources for inputs. If firms can decrease the comparative transaction costs of using the market, it is economically justifiable to continue using market sources for inputs. 14
2.2.1 The Effect of Information Technology on the TCT Framework The application of information technology (IT) in supply chain relationships has changed the balance between markets and hierarchies by decreasing the cost of transacting outside the firm’s boundaries (Clemons and Row 1992; Malone et al. 1987). The reduction of external coordination costs has two distinct outcomes. First, in situations where transaction costs would have forced firms to internalize exchange, the use of IT decreases the cost of exchange and allows the transaction to occur with external suppliers. Research into the use of IT in the supply chain has shown that firms are using IT to reduce their internalized transactions and form more relationships with external firms (Brynjolfsson et al. 1994; Hitt 1999). Improvements in IT have greatly reduced external coordination costs, thereby decreasing transaction costs. As shown in Figure 2.1, the incorporation of IT into the markets and hierarchies discussion has added richness to the TCT framework by helping predict movement along the continuum. The electronic markets hypothesis (EMH) recognizes that the use of IT to facilitate interfirm information exchange lowers the costs of transacting outside of the boundaries of the firm. Using IT is expected to decrease the cost of search, document processing, and monitoring. Lower transaction costs are hypothesized to encourage firms to interact with the market rather than internalize transactions (Malone et al. 1987). The EMH was followed by the vendors-to-partners thesis which recognizes that IT can be used to form close interfirm relationships where hierarchy-type benefits could be achieved in external (supply chain) relationships. Firms are predicted to move away from discrete market transactions to form closer relationships
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which enhance quality, responsiveness, and innovation (Bakos and Brynjolfsson 1993; Saeed et al. 2005; Subramani 2004). These two perspectives are important because although they both support that firms will increase their use of outside suppliers, they differ in how firms will use outside suppliers. EMH predicts an increased use of the market. Conversely, the vendors-topartners thesis predicts the use of close relationships that are similar to a vertically integrated hierarchy but still outside of the firm boundaries. Balancing these perspectives, the “move to the middle” was proposed (Clemons et al. 1993; Gurbaxani and Whang 1991). The “move to the middle” posits that the use of IT will create a balance between market and hierarchy benefits in supply chain relationships. This balanced perspective is important since it recognizes the trade-offs that must be managed when integrating IT in supply chain relationships. How IT changes the way interfirm business is conducted depends on the resources and needs of the participating firms. Some will take advantage of more accessible markets (EMH) while other firms will use IT to seek improved performance through collaboration (vendors to partners). The move to the middle is an important realization that firms will use a mix of IT strategies to manage their portfolio of trading partner relationships. While the literature is unclear where relationships will fall along the continuum, the use of IT to facilitate low cost interfirm transactions is consistent. While the use of IT can be seen as a benefit to enable firms to efficiently interact with external trading partners, the literature is inconsistent on the appropriate closeness of supply chain relationships and how much information should be shared. So many studies
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have shown the benefits of close trading partner relationships that one might assume that closer is better. However, additional research has suggested that the trading partner pool be treated as a portfolio. Through a portfolio perspective, close relationship are developed with specific trading partners while other trading partners are kept at armslength (Lambert et al. 1996a; Lambert et al. 1996b; McCutcheon and Stuart 2000). 2.2.2 The Effect of Information Exchange on the TCT Framework Information exchange is central to the coordination of firms in a supply chain context and is instrumental in both markets and hierarchies under the TCT framework. From a markets perspective, the exchange of information is central to the efficient operation of the price mechanism. Neoclassical economics assumes that full information is available to all market participants at no cost. This full availability of information to all exchange participants in the market allows the price mechanism to efficiently clear the market. Full information is rarely available to all parties in the exchange, but the parties can choose to exchange information. When information is exchanged within supply chain relationships, firms can decrease the uncertainty and lower the cost of monitoring the relationship. TCT recognizes that information is not equally and fully available to all exchange participants. TCT acknowledges information asymmetry and presupposes that information is available to the exchange parties – for a price. The cost of searching, gathering, and using information increases the cost of coordinating transactions with the market (Williamson 1975; Williamson 1985).
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Within the TCT framework, information exchange also can decrease transaction costs. Information decreases transaction costs through its effect on uncertainty, coordination, and monitoring. First, the availability of information decreases uncertainty by providing additional knowledge to trading partners. The relationship becomes less uncertain when demand information, firm goals and intentions, and production plans are known and shared between exchange partners. When information is shared in advance, there is less uncertainty about the intentions of the trading partner if problems do occur (Rozenzweig et al. 2003). Second, information is used to efficiently coordinate transactions. When suppliers know demand forecasts, marketing plans, and point-of-sale information, order quantities are expected and properly planned to minimize both overstocking and stockouts (Metters 1997). Information technology has been used extensively to facilitate the low-cost exchange of information to support the order processing cycle. Finally, information is used to facilitate monitoring mechanisms to protect against opportunistic behavior when specialized assets are at risk. When production schedules, plant capacities, inventory positions, and shipment schedules are known across the supply chain, firms have greater visibility into the operations of their trading partners (Rozenzweig et al. 2003). The regular exchange of this information allows for the monitoring of key interfirm processes critical to governance mechanisms under TCT. The downside of close trading partner relationships includes the cost of development and the risk of opportunism. Since the cost of developing a relationship cannot be recovered or applied to a new relationship, these costs represent a specific asset that is lost if the relationship fails to develop. The costs of developing close trading
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partner relationships depend on many factors including the level of integration and governance structure (Lambert et al. 1996a). Complex interactions and specific capital investments may require extensive formal agreements to coordinate their resources. Such investments in time and capital are then protected under the terms of the contract. If the anticipated benefits do not materialize, then firms have incurred unnecessary costs. Similarly, the transparency that develops when information is shared between firms may allow trading partners to act opportunistically by withholding some benefits of the relationship (Shapiro and Varian 1998). In summary, this literature stream suggests that rather than pursuing close relationships with trading partners, it is more effective to develop close relationships with the right trading partners (Lambert et al. 1996a; Lambert et al. 2004). Research has linked the exchange of information between firms to the strategic issue of supply chain integration. Using survey data from a sample of global manufacturers, Frohlich and Westbrook (2001) tested the relationship between supply chain integration strategy and performance. The study included both the direction of integration (customer facing or supplier facing) as well as the degree of integration (no integration to extensive integration). The study found that the greatest performance improvements were made by firms with the highest levels of integration with customers and suppliers. Information is central to the integration of firms in the supply chain. Frohlich and Westbrook (2001) used four information exchange measurements to capture the integrative activities of firms. The integrative activities used by firms to link with their external supply chain participants are each based on information exchange. This
19
research clarifies the bi-directional role of strategic information exchange in supply chain relationships. Marketing channels literature also supports the vital role of information exchange in supply chain relationships. Using organization theory and communications theory, researchers posit that the communications strategy is a moderator between channel conditions and channel outcomes (Mohr and Nevin 1990). This vital role of communications between supply chain participants is empirically tested and validated through the operationalization of communication as frequency, direction, modality, and content (Rinehart et al. 2004; Vickery et al. 2004). 2.3 Review of Empirical Literature 2.3.1 Use of Information in Supply Chains Inventory can be a significant cost driver in supply chains. Having too much inventory causes firms to accrue unnecessary carrying costs. Having too little inventory causes firms to accrue unnecessary stockout costs. Researchers suggest that information can be a substitute for inventory (Daugherty and Pittman 1995). In practice, when forecasts, inventory positions, and actual demand are shared with upstream suppliers inventory needs can be planned such that goods are produced when needed rather than held in inventory in case they are needed. Specific research into demand distortion has shown that information can reduce the buildup of unneeded inventory. Demand distortion, also known as the bullwhip effect, is a phenomenon whereby upstream supply chain participants experience volatility
20
in demand greater than the actual consumer demand volatility (Lee et al. 1997; Metters 1997). When the bullwhip effect occurs, small fluctuations in consumer demand create larger fluctuations in replenishment orders upstream in the supply chain. These distortions of demand cause upstream participants to build-up inventory far beyond what is required to satisfy consumer demand. Research into the causes of the bullwhip effect suggests that exchanging additional information including inventory status, order coordination, and point-of-sale data could reduce the bullwhip effect (Chen et al. 2000; Lee et al. 1997). Researchers using the beer game simulation, have found that information sharing does dampens the bullwhip effect (Croson and Donohue 2006). In their study, the sharing of information allowed upstream suppliers to anticipate downstream inventory needs. In a similar beer game simulation, it was found that the sharing of point-of-sale (POS) information upstream in the supply chain dampened the bullwhip effect but the results varied depending on the nature of the demand pattern (Steckel et al. 2004). They found that when demand is volatile (S-shaped demand pattern) then the sharing of POS information becomes a distraction to the more immediate issue of responding to orders. The exchange of information between supply chain participants has benefits beyond dampening the bullwhip effect. Through the modeling of a two-tier supply chain with one supplier and multiple retailers, it has been shown that sharing demand information can reduce inventory across the supply chain by as much as 12% (Cachon and Fisher 2000). Sharing demand information is shown to improve manufacturer forecasting accuracy and decrease safety stock (Raghunathan and Yeh 2001). This
21
research examines the role of information exchange in improving supply chain performance. 2.3.2 Information Exchange to Support Specific Supply Chain Initiatives The exchange of information in the supply chain is found to support strategic initiatives. These initiatives are thought to be strategic in nature because they allow firms to create unique competitive advantages by implementing interfirm processes. Vendor managed inventory (VMI) is an interfirm process whereby the supplier is authorized to manage inventories of his downstream customers. Since the VMI supplier has the freedom to make re-supply decisions for the retailers, inventories and transportation can be synchronized for both echelons (Cetinkaya and Lee 2000). Similarly, the continuous replenishment process (CRP) allows the upstream supplier to control restocking at retail locations. Studying a single manufacturer engaged in CRP with its retailers, it was found that stockouts were reduced and inventory turnover was increased (Lee et al. 1999). Programs like CRP allow inventory to be coordinated across multiple echelons of the supply chain. Variability in demand is absorbed upstream by keeping safety stock to satisfy demand when forecasts understate the actual demand. Researchers have shown that the sharing of demand information improves the upstream manufacturer’s forecast accuracy and allows for decreases in safety stock (Raghunathan and Yeh 2001). The use of just-in-time (JIT) processes has shown benefits for supply chain participants by allowing them to minimize the buildup of inventory throughout the supply chain. Using a survey methodology, researchers found that use of JIT is directly related to savings in logistics costs for buyers (Dong et al. 2001). Similarly combining the use of
22
EDI with a JIT environment has been shown to reduce shipment errors (Srinivasan et al. 1994). Each of these interfirm processes are designed to improve supply chain performance by coordinating resources across the supply chain. Successful coordination is dependent on the exchange of accurate and timely information (Angulo et al. 2004). 2.3.3 Using Interorganizational Systems to Exchange Information Interorganizational systems (IOS) have been identified for their boundary spanning role in integrating firms in the supply chain (Zaheer and Venkatraman 1994). These systems take many forms and use various technologies including: EDI, email, electronic exchanges, Web-based applications, and extensible mark-up language (XML). EDI is often the focus of empirical research since it represents a stable technology that is well established in industry. Empirical literature has addressed EDI as both an application of a specific IOS technology and more generally as an enabler of information exchange. Early EDI research focused on the adoption of the technology (Crum et al. 1998; Crum et al. 1996; Johnson et al. 1992). Although trade publications predicted that EDI technology would be supplanted by the newer technologies, EDI continues to be a primary method of exchanging information in industrial supply chains. Firms already possess the necessary knowledge and infrastructure to exchange information using EDI and continue to leverage the resource. Trade journals report the continued and growing use of EDI (Brockmann 2003; Sliwa 2004). More recent research has focused on the use of EDI to exchange information that supports collaborative supply chain initiatives, and may include: VMI, JIT, and CRP.
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This literature is critical for establishing the key mediating role of IT and information exchange in supply chains. IT is the enabler of information exchange and forms an efficient conduit that spans the boundaries of individual firms in the supply chain. The information that is exchanged through the use of IT supports many of the innovative processes used to improve supply chain performance. 2.4 Research Model Development Theory and existing literature clearly recognize the critical role of information exchange in supply chain relationships. A gap in the literature is how information exchange is operationalized in empirical research. Information exchange has not been studied in a way that will enhance the understanding of how information exchange improves performance. Research has addressed information exchange from many perspectives. Information exchange is often modeled as a binary measure where firms either exchange information or do not. Some studies have recognized that information is not homogenous and have measured the existence of multiple types of information. Such studies include whether demand forecast information is exchanged or whether point-of-sale information is exchanged but no more specific measures to capture the frequency or extent of the exchange. Other research has begun to address the multi-dimensional features of information and proposed robust measures to capture some of the complexities of information exchange but has been limited to data collection from single firms. This dissertation
24
combines and expands these approaches by identifying and testing objective measures of information exchange across multiple firms. Early studies of information exchange technology focused on the adoption of IOS to coordinate the supply chain. Appropriate questions at the time where centered around whether firms were exchanging information through the use of IOS and if performance was effected. As such, basic binary measures and measures of the percentage of relationships that used the technology were appropriate to understand the diffusion of technology (Allen et al. 1992; Crum et al. 1998; Crum et al. 1996; Srinivasan et al. 1994; Zaheer and Venkatraman 1994). As the use of information exchange through technology became evident, research expanded to identify characteristics of information exchange. In a study of the effects of information sharing strategies on supplier reliability, the type of information and direction of exchange were captured using binary measures (Walton and Marucheck 1997). This survey-based research measured information exchange as if the buyer shares forecasts, if the buyer shares planned production, if the buyer shares capacity information, and if the supplier shares planned production. The study concludes that relationships where buyers share forecast information experience lower supplier reliability than relationships where buyers share planned production information with suppliers. Sharing forecast information is less valuable than sharing production plans. These results support that what types of information are shared effects the performance outcomes. Additionally, these measures recognize that each firm can exchange information such that information flows in both directions across the supply chain.
25
The complexity of information exchange has been captured in some EDI studies which allow for the development of an understanding of the heterogeneity of how firms use information exchange. Facets of EDI usage that help measure these differences are the volume of exchanges through IOS, the different types of information exchanged through IOS (diversity), the number of trading partners connected through the IOS (breadth), and the extent to which the interfirm processes are intertwined (depth) (Massetti and Zmud 1996). Although the focus of the study was EDI use, the measures captured how the firms were using a specific IOS to manage their portfolio of trading partner relationships. Segregating exchange into multiple components allows researchers to address how each facet contributes to firm performance and their interaction can be considered. This dissertation adopts the multi-faceted approach and applies it specifically to the measurement of information exchange in supply chain relationships. For the purposes of this study, information types are categorized as being either transactional or enhanced based on their content. This distinction between types of information that can be exchanged is modeled by Cachon and Fisher (2000). In their study, information exchange is categorized as traditional information sharing and full information sharing. Under their depiction of traditional information sharing, only order information was given to the supplier. Full information sharing is modeled as allowing the supplier visibility of the retailer’s inventory levels. Their use of traditional information sharing is consistent with the transactional information identified in this dissertation. This distinction has also been modeled where traditional information is compared with the sharing of additional downstream demand forecasts (Cachon and Lariviere 26
2001). The inclusion of additional types of non-order information gets to the issue of how diverse information is used in the supply chain. Transactional information is used to support the order cycle and was described as having an electronic data processing orientation by Porter and Millar (1985). This information includes electronically exchanged requisitions, purchase orders, purchase order confirmations, invoices, and remittance advice documents. Exchanging these documents electronically reduces the processing costs for firms by eliminating mailing costs, eliminating mailing delay, eliminating the need to enter the data manually at the receiving site, and reducing data entry errors. Information beyond order cycle information can be used to support the coordination of interfirm resources (Cachon and Fisher 2000; Cachon and Lariviere 2001). In a study of VMI processes, researchers recognized that information including forecasts, daily demand, inventory positions, and shipment information can be exchanged in order to improve supply chain performance (Angulo et al. 2004). In this dissertation, the additional information that can be used to support interfirm coordination is included as enhanced information. This depiction of enhanced information exchange is inclusive of the full information identified in both Cachon and Fisher (2000) and Cachon and Lariviere (2001). These two types of information exchange are modeled distinctly in research depending on the focus of the study. Exchanging enhanced information between a vendor and a retailer can include demand information, shipment information, inventory positions, and forecasts to support the decisions in a VMI process (Angulo et al. 2004).
27
The outcomes of these additional information flows include improved cash flow, shortened order cycle time, and increased firm competitiveness (Iacovou et al. 1995). Conversely, the exchange of transactional information is associated with improving the speed and accuracy of the order processing cycle. The electronic exchange of requisitions, purchase orders, and invoices lowers the cost of transacting but has minimal impact beyond the order cycle. Firms may choose to exchange any combination of the various types of information. Since research has identified that performance outcomes are affected differently based on the types of information exchanged, one could expect that various combinations of information types will similarly affect performance outcomes (Cachon and Fisher 2000). These two dimensions of information exchange characteristics become the foundation for improving the understanding of information exchange in supply chain relationships. To illustrate the theorized relationship between the exchange of transactional information and the exchange of enhanced information in trading partner relationships, Figure 2.2 provides a two-by-two matrix. The four quadrants identify four distinct combinations of exchange volume between transactional and enhanced information within trading partner relationships. The mean values of transactional information exchange volume and enhanced information exchange volume are identified for each technology champion firm. By comparing the exchange volumes for each trading partner relationship with the mean of the technology champion firm, relationships can be identified as operating either above or below the mean for each type. Trading partners that are above the firm mean for both transactional and enhanced information exchange 28
are recognized as having closer relationships relative to other trading partners exchanging with the technology champion firm. Figure 2.2 Information Exchange Matrix
Transactional High Information Exchange Volume Low
I. Transactional Relationships
II. Close Relationships
III. Arms-length Relationships
IV. Enhanced Relationships
Low
High
Enhanced Information Exchange Volume
The top-left quadrant, I Transactional Relationships, depicts a condition where the frequency of transactional information exchange is high and the frequency of enhanced information is low. In practice, this situation may occur when a commodity item is exchanged. Although the item may be needed often or it is requested by multiple business units within the organization, the information exchanged is oriented towards the order cycle and little if any additional information is exchanged. Such may be the case in a supply chain relationship for providing office supplies. The buying firm could create a contract that decentralizes the ordering process which allows each operating area to place orders with the office products supplier on an as-needed basis. This would potentially create a situation where order cycle information is exchanged at a high frequency. Once
29
the items for the contract are identified and pricing / service expectations are agreed upon, minimal additional information would be required to support the relationship. Enhanced information such as production schedules, demand forecasts, and logistics documents are rarely shared in these commodity relationships because the cost of exchanging them is often greater than the value they add in managing basic commodity products. The supplier simply fills the order and delivers it based on a pre-determined service-level agreement. The top-right quadrant, II Close Relationships, depicts a condition where both transactional and enhanced information are exchanged at a high volume. A high volume of transactional information is indicative of many interfirm orders being placed and filled. Trading partner relationships that exist in this quadrant may exchange commodity item or production materials that are supplied on a frequent basis. Materials supplied through a just-in-time process could represent items that are ordered on an hourly or daily basis for sequencing into the production cycle. Due to the critical nature of the items, additional enhanced information could be exchanged to synchronize production further down the supply chain. The exchange of additional information including demand forecasts, actual customer demand, production sequences, and inventory balances can enhance the planning process across multiple echelons of the supply chain. The bottom-left quadrant, III Arms-length Relationships, depicts a condition where both transactional and enhanced information are exchanged at a low volume. These trading partner relationships are expected to represent non-critical items that are ordered on an infrequent basis. The items may be low in cost or low in usage which in either case would support infrequent ordering. Low cost items have a minimal effect on 30
carrying costs relative to their ordering costs as modeled in the economic order quantity (EOQ) calculation and are often ordered in larger quantities less often. Similarly, low demand items would be ordered infrequently even in a just-in-time environment. Commodity or low-criticality items would be less likely to benefit from the exchange of enhanced information. In practice, these items might include maintenance, repair, and operations (MRO) supplies which are used to support the functions of the firm. The bottom-right quadrant, IV Enhanced Relationships, depicts a condition where transactional information is exchanged at a low volume but enhanced information is exchanged at a high volume. The high volume of exchange for enhanced information would suggest that the items supplied through these trading partner relationships are critical in nature either due to their use in the process or their cost. The exchanging of enhanced information would support enhanced planning and synchronizing across interfirm processes. The combination of high enhanced volume with low transactional volume would suggest that the items are either ordered in bulk or not needed very often. In practice, these items may be direct materials that are used in production of the firm’s product. Large quantities of critical inputs that are ordered infrequently may include raw materials that are ordered by rail car such as flour and sugar. Small quantities of critical inputs may include custom hydraulic equipment for large construction equipment. In either case, large volumes of planning and sequencing information may be exchanged to ensure that the input is available when needed, but minimal transactional information is exchanged due to the infrequent need to procure the items. This matrix provides a grid though which information exchange can be observed to better understand how firms use information exchange strategically in their supply 31
chain relationships. Based on the TCT theoretical lens and the extant literature on the use of IT-enabled information exchange, this dissertation will address the research question of how information exchange is associated with supply chain performance. Chapter three describes the research setting and data that will be used to test hypotheses through studies developed in Chapters four and five.
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Chapter 3: Research Setting and Data
This dissertation uses a unique dataset extracted from an established, electronically-mediated industrial exchange network. Where most previous studies have been limited to survey-based perceived measures of information exchange, simulation of information exchange, or analytical modeling of supply chain interaction, this study uses objective measures of actual exchanges of information between industrial supply chain participants. This proprietary database has been made available by one of the largest EDI network providers in the industry. The data include summary volumes at a network, technology champion firm, trading partner, and dyadic level on a monthly basis for the years 2004 and 2005. Additional secondary data on firm performance and company specific data has been provided from Standard and Poor’s Compustat database. Interactions with this EDI network provider helped provided qualitative and quantitative information pertaining to this research. The qualitative information is used to help describe the context of the investigation of the research questions. Conversations with employees from the EDI network provider helped give great insight into the nature of the relationships between the technology champion firms and their trading partners. Although none of the qualitative information was used explicitly, it was used implicitly to help structure the research. 3.1 Use of Electronic Data Interchange Data for Empirical Research For the purposes of this study, EDI is defined as a specific type of IOS which 1) exists between at least two organizations, 2) transfers data between independent
33
application systems at each firm, 3) uses standardized data formats, and 4) transfers the data using telecommunication links (Iacovou et al. 1995; Pfeiffer 1992). EDI under this definition is a computer-based method of formatting and exchanging information that is relatively fast, accurate, and low-cost. The benefits of technology adoption related to EDI implementation have been addressed in logistics and EDI literature. A survey of warehousing firms found that firms using EDI had a strategic impact by providing a greater (average) number of services to customers and by more easily accommodating special customer requests (Rogers et al. 1992). The use of EDI in an automotive setting resulted in a cost savings of between sixty and one hundred dollars per vehicle (Mukhopadhyay et al. 1995). While early EDI studies focus on the growth and adoption of the technology, more recent research accepts the widespread use of EDI and instead focuses on the best use of the technology. For example, Subramani (2004) looks at how firms can extend EDI relationships to develop domain-specific knowledge and business-specific processes that create negative externalities for their competitors. Researchers in IT, operations management, and logistics have viewed EDI as a specific application of IOS. This perspective contributes to the understanding of the use of IT as a tool for the exchange of interfirm information. Key EDI-based literature, methodology, and findings are summarized in Table 3.1. Table 3.1 EDI Literature Review Author (s) Methodology (Subramani 2004) Empirical – Case study •
Major Findings Suppliers benefit from EDI implementation when they use the integration to build business process specificity or domain knowledge specificity
34
(Machuca and Barajas 2004) (Hill and Scudder 2002)
Empirical – Simulation Empirical – Survey
• • •
(Mukhopadhyay and Kekre 2002)
Empirical – Archival data
• • •
(Angeles and Nath 2001) (Raghunathan and Yeh 2001)
Empirical – Survey Empirical – Modeling
• • • • • •
(Droge and Germain 2000)
Empirical – Survey
(Cachon and Fisher 2000) (Lee et al. 1999)
Empirical – Modeling Empirical – Case study Empirical – Survey
• • • • • • • • • 35
(Crum et al. 1998)
(Williams et al. 1998)
Empirical – Survey
(Walton and
Empirical –
EDI use contributes to cost savings and SCM improvements (reduces bullwhip) EDI seen as a source of efficiency rather than integration Firms are more accommodating to customer EDI than supplier EDI Use of integrated EDI results in more ontime payments and less credit orders Customer driven EDI implementation results in greater benefits for the supplier Supplier process specificity results in greater benefits for the supplier Compatibility of trading partners is important for successful EDI implementation Sharing of demand information improves the manufacturer’s forecast accuracy Information sharing decreases safety stock Continuous replenishment programs benefit retailer and manufacturer by moving inventory faster The use of EDI and firm financial performance are positively related Increased inventory is positively related to EDI, stable demand, small firm size, routine production technology and JIT usage Use of IT to accelerate the flow of products is more valuable than using IT to expand the flow of information Retailers who are forced to adopt systems can benefit if they reengineer their internal processes Use of EDI is increasing Customer service and marketing are implementation drivers Use of transaction sets is concentrated Stakeholders generally satisfied with EDI EDI can be measured as a multidimensional technology Longer use of EDI and increased investment increase the width, depth, and range of use EDI reduces uncertainty due to timeliness
Marucheck 1997)
Survey • •
(Crum et al. 1996)
Empirical – Survey
• • •
(Massetti and Zmud 1996) (Mukhopadhyay et al. 1995)
Conceptual Empirical – case study
• • •
(Wang and Seidmann 1995)
Empirical – Modeling
•
• (Iacovou et al. 1995) Empirical – Structured Interviews • • • (Srinivasan et al. 1994) (Zaheer and Venkatraman 1994) Empirical – case study Empirical – Survey • • • • • • • • 36
(Allen et al. 1992)
Empirical – Survey
(Rogers et al. 1992)
Empirical –
and information flow Demand information is negatively related to supplier performance Production schedule sharing is positively related to supplier performance Adopters and non-adopters differ on environmental and organizational factors Slow increase in use of new transactions Customer show higher EDI satisfaction than carriers (suppliers) Identified four dimensions of EDI usage: volume, diversity, breadth, depth Real dollar savings for production are attributed to the use of EDI. Savings from inventory carrying, obsolete inventory, premium freight, paperwork Supplier’s use of EDI creates positive network externalities for himself and negative network externalities for its competitors Buyer pays a premium for EDI integrated supply For small firms, pre-adoption awareness of EDI benefits is low External pressure from customers and competitors drives adoption of EDI Implementation cost is a barrier for small firm adoption of EDI Suppliers with integrated EDI have lower shipment errors Asset specificity is positively related to the degree of electronic integration Trust is positively related to the degree of electronic integration Reciprocal investment is negatively related to the degree of electronic integration Large carriers are expanding EDI usage more than small carriers EDI is used more without contracts than with contracts Carriers implement EDI to meet customer requirements and improve customer service Warehousing firms offering EDI services
Survey questionnaire • • • •
(Johnson et al. 1992)
Empirical – Survey
(Crum and Allen 1990)
Empirical – Survey
are better able to accommodate customer requests Warehousing firms offering EDI technology provide more services to their customer than non-EDI firms 56% used firm-specific (custom) formats Meeting customer requirements/customer service was greatest benefit of implementation EDI usage positively correlated with supplier base reductions
3.2 Formatting Standards and Electronic Intermediaries EDI provides standard communication formats that allow companies to universally exchange data. An EDI standard is a specific format for translating discrete business documents into electronic messages. Each business document type is defined using an EDI standard format. Purchase orders, invoices, shipping notices, demand data and hundreds of other business documents are specifically defined for transfer between companies. Through the use of standardized format, all purchase orders will have the same electronic layout. This standardization allows the purchase order to be created and interpreted by all firms using the format standard. Firms that use the EDI standards can more easily exchange information with external firms since the published standards specify how the data is interpreted for use between firms. There are currently two organizations recognized for developing EDI message standards. The American National Standards Institute (ANSI) develops domestic standards and the United Nations (UN) creates international standards. The UN standards establish criteria for standard EDI messages for Administration, Commerce, and
37
Transport (EDIFACT) (Massetti and Zmud 1996). The ANSI X12 and the United Nations EDIFACT standards are both used for the formatting of electronic documents using EDI (Zuckerman 2004). Domestically, the ANSI X12 standard for EDI business documents is most popular as it is specifically designed to support business transactions in North America. The United Nations EDIFACT standard is used for international EDI transactions. Alternative formatting methods include XML and relation specific messaging formats (Zuckerman 2004). Although low set-up and implementation costs are making alternative formatting methods more popular, their lack of standardization limits their effectiveness. 3.2.1 EDI Exchange Networks For the purposes of this study, the sponsoring firm will be called the technology champion firm which corresponds to other research that identifies the sponsor as the relationship initiator (Iacovou et al. 1995; Truman 1998). Sponsor firms participate in the exchange of information and take a leadership role in the development, maintenance, and expansion of the IOS with their trading partners. Their customers and suppliers that join the EDI network are identified as participants, adopters or simply trading partners (Iacovou et al. 1995). For the purposes of this study, trading partner will be used to describe the firms that were invited to join the network by the technology champion firm. 3.2.2 Establishing and Maintaining an Electronic Network The exchange of EDI documents can be arranged directly between firms or by using an EDI service provider as an intermediary. The service provider offers technical support and operates the exchange network. Many firms seek the services of EDI
38
intermediaries to avoid the development and maintenance costs related to creating proprietary communications networks. Researchers note that EDI implementation costs create a technology barrier as EDI champion firms attempted to expand the technology to their trading partners (Walton and Marucheck 1997). Value-added network providers (VANs) reduce the technology barrier by becoming electronic post offices for firms that do not want to build and maintain their own proprietary EDI communication networks. A firm choosing to use an integrator to implement EDI-based transactions with its trading partners would select a VAN, which in turn, works with the sponsor’s trading partners to form electronic linkages. Once the trading partner joins the electronic network, the VAN serves as a clearing-house for all EDI transactions between the sponsor firm and its trading partners. The VAN is responsible for the high-speed connectivity, security, training, and data format support to ensure compliance with various EDI standards. EDI is subject to network externalities such that the benefits to sponsors are increased as additional firms adopt the technology (Shapiro and Varian 1998). Growth of the EDI network allows sponsors to transact with more trading partners and reduce the cost of maintaining alternate parallel systems to transact with non-EDI-capable trading partners (Iacovou et al. 1995). Johnson et. al (1992) aptly note that firms are increasing their use of EDI by getting more trading partners to use EDI and by expanding the types of information exchanged using EDI. Although adoption of EDI and growth of the EDI network are in the best interest of the technology sponsors, many trading partners are reluctant to adopt the technology. Technology diffusion research recognizes that smaller firms may be at a disadvantage in adopting EDI. Studies focused on the diffusion of EDI technology recognize that the cost
39
of EDI implementation can deter the adoption of EDI by some firms (Iacovou et al. 1995). Three factors have been identified for their effect on EDI adoption for small firms: perceived EDI benefits, organizational readiness, and external pressure (Crum and Allen 1990; Iacovou et al. 1995). Perceived EDI benefits are the recognition by a firm’s management that implementing EDI provides a relative competitive advantage. Organizational readiness refers to the firm’s technical and financial resources that can be allocated to EDI implementation. External pressure refers to influences outside of the firm that encourage the adoption of EDI. These external influences can originate with competitors or trading partners. Through an empirical study of seven companies, researchers found that the strongest influence of small firm adoption of EDI was external pressure from trading partners (Iacovou et al. 1995). Firms that were highly dependent on their EDI champion trading partners showed the highest likelihood of EDI adoption. In the same study, results indicated a positive relationship between perceived benefits and adoption but mixed results for the relationship of organizational readiness and EDI adoption. The pressure from sponsor firms to implement EDI has often been identified as a driver of EDI adoption by trading partners. As one might expect, meeting a customer requirement was one of the most important reasons that trucking firms implement EDI (Allen et al. 1992; Johnson et al. 1992). Moreover, firms often couple the use of EDI with other strategic initiatives such as the reduction of trading partners and the use of longer-term contracts (Allen et al. 1992; Crum and Allen 1990). Although trading partners are increasingly migrating to the Internet to support EDI transactions, their interactions with the technology champion firms are still captured
40
in this dissertation. The full-service integrator providing the data for this study supports both proprietary communications links as well as communication through existing Internet channels using the AS2 specification. AS2 is a relatively new specification that is becoming the standard for securely transporting EDI data through the Internet. Firms have been expanding their use of AS2 as a way to reap the benefits of standardized EDI formatting while avoiding the cost of using a proprietary communications network. Existing EDI integrators often offer software and hosting services for Internet EDI in order to meet customer demands for hybrid solutions. The technology champion firms in this study are exchanging EDI documents through either the proprietary VAN network or are using Internet EDI. Whether a technology champion firms uses the proprietary communication network or the Internet to connect with their trading partners is immaterial since the data is captured as it is routed to the electronic mailbox. In other words, regardless of the network that transports the EDI information exchange (VAN or Internet), the interaction with a technology champion firm and its trading partners is captured by examining information sent to and from the technology champion’s EDI mailbox. 3.3 Units of Observation This dissertation adopts two distinct units of observation. The first unit of observation is used in Chapter 4 and focuses on the exchange dyad. Each dyad consists of a technology champion firm and one trading partner. Using this unit of analysis allows for the exploration of information exchange within a specific supply chain relationship. The second unit of observation is adopted in Chapter 5. This unit of observation is the
41
technology champion firm. This single firm level unit of observation supports the exploration of how a firm’s use of the technology affects its performance. Both analyses focus on the inter-firm information exchanged through an EDIbased trading network. The information exchange transactions on this trading network during a twenty-four month period provide the data for both analyses. This proprietary longitudinal dataset includes observations of the electronic transactions for thirty-nine publicly traded technology champion firms and their EDI connected trading partners. These thirty-nine focal firms are the EDI technology champions that formed initial relationships with the EDI integrator and then enlisted the participation of their trading partners. The EDI integrator coordinates with each of the technology champion firm’s trading partners to create a telecommunications link either through a proprietary communications network or an alternate public network such as the Internet. 3.3.1 Firms in the Network The technology champion firms in this trading partner network represent a broad range of industries. Based on the two-digit SIC code, the thirty-nine technology champion firms are distributed across three echelons of the supply chain (manufacturing, wholesale trade, and retail trade). Of the thirty-nine firms, twenty-three of them are manufacturers, eleven are retailers, and five are wholesalers. The dataset provided by the EDI integrator includes all EDI transactions for the years 2004 and 2005 for the technology champion firms. Researchers have noted that firms can employ multiple methods and technologies to exchange information with their trading partners (Vickery et al. 2004). This being the case, the EDI network which
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provided the data may not be the primary exchange technology for a particular technology champion firm. In situations where the focal EDI network is not the primary exchange technology, changes in the data exchanged through the network may be confounded by the use of alternate channels. Since the services provided by the EDI integrator are scalable, technology champion firms use the network in various ways to support their business needs. Some technology champion firms use the network for only specific transaction types or relationships. Other technology champion firms use the network as their primary mode of interfirm communications. The EDI integrator was not able to fully assess the champion firm’s use of alternate methods or strategy in using this network. To minimize the effects of including firms where alternate technologies may be in place, technology champion firms were eliminated from the study if they did not transact order cycle data through the observed network. An assumption was made that if order cycle transactions are not being exchanged on this network then the firm must be using at least one other primary channel to transact in the supply chain. A test was conducted to verify that order related data was transacted by the technology champion firm during the study period. If the champion firm did not transact any order cycle documents with its trading partners at any time during the study period, they were eliminated from study. The resulting 39 technology champion firms included in this study represent 23 manufacturers, 5 wholesalers, and 11 retailers. The EDI integrator noted that technology champion firm relationships may vary by trading partner. As such, the potential exists that for an individual trading partner relationship, alternate technologies and communications methods may be in place. If a particular technology champion firm-trading partner dyad does not use this network as
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their primary exchange channel, any measures made from observations of this network may be biased. To minimize any potential confounding effects from unobserved information exchange, each dyadic relationship was analyzed to ensure that order cycle data was transacted by the dyad and that the information exchange was reciprocal. The presence of order cycle information exchange within the dyad provides an indication that the observed network at least supports the foundational electronic data processing functions of the dyad. By confirming the reciprocal exchange of information within the dyad, there is an increased likelihood that the observations represent a fully functioning supply chain relationship where information is shared by both parties. For the purposes of this analysis, all observed trading partner relationships include order cycle transactions and a reciprocal sharing of information across the dyad. The study period covers twenty-four months, starting in January 2004 and ending in December 2005. Transactional data are collected and transferred to a data warehouse by the EDI integrator at the close of each calendar month. Monthly extracts were made from the EDI integrator’s data warehouse using Business Objects. Each observation in the extracted files contains unique technology champion firm-trading partner volumes for each transaction type during the period. A sample of the data is shown in Table 3.2; the EDI integrator transaction database provides observations at a summary level for each dyadic relationship on the network.
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Table 3.2 Sample of Trading Partner Network Data Observation Technology Send / Trading Period Champion Receive Partner Firm 1 X S A Jan. 2005 2 X S A Feb. 2005 3 Y R B Feb. 2005 …n
EDI Transaction ID 850 304 610
Transaction Count 23 30 341
As illustrated in Table 3.2, each observation identifies the technology champion firm and its exchange with a specific trading partner during the period. The identities of the technology champion firms are masked throughout the analysis under the terms of the non-disclosure agreement with the EDI integrator. The identities of the trading partner firms were not supplied by the network provider. Each observation then identifies whether the technology champion firm received ( R ) the EDI document or sent ( S ) the EDI document. The EDI integrator assigns unique mailbox numbers to each of the trading partners to facilitate the routing of EDI messages. The period is the abbreviated month and year of the transaction. The EDI Transaction ID is an alpha numeric value that identifies either an ANSI X12 standardized message, an EDIFACT standardized message, or a generic value identifying a firm specific custom message. Examples of standard EDI transactions under the ANSI X12 and United Nations EDIFACT message types are included in Appendix A. The termination study developed in Chapter 4 uses the monthly observations to support the analysis of information exchange characteristics and relationship termination. For the technology champion firm performance analysis developed in Chapter 5, the monthly information exchange data is aggregated to the calendar quarter. This
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aggregation allows for the matching of firm quarter information exchange measures to the publicly available firm financial data. Technology champion firm’s financial and operational measures were obtained from Standard and Poor’s Compustat financial database. Public companies provide selected operational and financial information on a quarterly and annual basis. The Compustat data have been extracted and matched for all thirty-nine technology champion firms for each of the eight quarterly EDI observation periods. 3.3.2 Key Constructs The three dimensions of transactions outlined by Williamson (1975 and 1985) are found within the measures commonly used in the research of information exchange and EDI adoption. Researchers have identified some characteristics of information exchange including information frequency, information diversity, and degree of customization (Crum et al. 1998; Massetti and Zmud 1996; Srinivasan et al. 1994; Williams et al. 1998). These richer measures of information exchange have extended research beyond the foundational level of binary measures which only measured if trading partners shared information. Greater interest is now focused on what information is exchanged and how the exchange affects supply chain performance (Saeed et al. 2005). This dissertation builds on these empirical measures and extends them to recognize the directional properties of information exchange and the diversity of information exchange.
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Directionality of Information Exchange Since this dataset provides a unique view of supply chain information exchange, the standard frequency measures of TCT and information exchange research are being combined and augmented to recognize not only the number of exchanges each month (frequency) but also the direction of the exchange. Since direction of exchange is sensitive to the position of the focal party within the exchange relationship, this study will maintain all statements about direction of exchange based on the perspective of the technology champion firm. Information exchange noted as “Send” represents information transferred from the technology champion firm to the trading partner. Information exchange noted as “Receive” represents information transferred from the trading partner firm to the technology champion firm as illustrated in Figure 3.1. Figure 3.1 Information Exchange Directionality
Trading Partner A
RE CE
IVE
SE ND
I VE CE E R ND SE
Trading Partner D
Trading Partner B
RECEIVE SEND
Technology Champion Firm
RECEIVE SEND
Trading Partner E
Trading Partner C
VE EI C ND RE SE
REC EI
SEN D
VE
Trading Partner F
Suppliers
Focal Firm
Customers
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Figure 3.1 has been labeled to associate the electronic exchange network with a traditional supply chain configuration. Specifically, the roles of supplier, focal firm and customer have been noted to illustrate the flow of information across the network. The roles of participants in the electronic exchange network used in this dissertation are not specified since the roles are based on each unique transaction rather than on the specific location of a firm within the supply chain. Potentially, a trading partner may supply raw materials to a focal firm and then in a later transaction purchase finished goods from the same focal firm, thus the trading partner’s role could change from supplier to customer depending on the transaction. What is unique in the structure of this network is that technology champion firms that are retailers predominately interact only with their suppliers through the exchange network. Consumers do not use EDI value added networks to transact with retailers. Information Type The level of detail available in an EDI enabled exchange network allows for the categorization of information by type as well as volume. Each transaction is identified with a transaction code to identify the functional process to which the information belongs. The sending firm identifies the transaction type so that the receiving firm knows where to route the information and which EDI standard to use when accepting the data into their system. The standardized formatting and identification of EDI transactions allows the transactions to be identified by the type of information they include. A preliminary categorization of the EDI standard transaction types into the two previously defined
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categories was provided to the EDI network integrator. Account executives then responded individually based on their knowledge of how the transaction types are used on the network. Modifications were made to the categorization and the revised categories were returned to the network integrator for review. The final form of the grouping used for all subsequent analysis is included in Appendices B and C. The use of standardized transaction types is a key element of EDI networks. In an EDI enabled network, purchase orders are identified with transaction code 850. The EDI ANSI X12 standard for transaction code 850 provides details of field locations and formats such that the 850 transactions from any trading partner can easily be incorporated into the host firm’s computerized order processing system. As described in Chapter 2, these transaction codes are used in this study to group information exchange documents as being either transactional or enhanced. 3.4 Descriptive Statistics 3.4.1 Descriptive Statistics of the Exchange Network The EDI exchange network provides a unique view of the electronic information exchanges for thirty-nine technology champion firms and their 18,644 trading partner relationships. Over a two-year period, there are 320,788,026 business documents exchanged in this network. Based on the categorization of information exchange provided in Chapter 2, these exchanges are representative of both the transactional and enhanced types. As described in Table 3.3, a small percentage of the documents exchanged (2%) could not be identified with either category. This occurs when nonstandard EDI transactions are defined by a technology champion firm or within a specific dyad. EDI participants may use non-standard transaction codes to identify documents 49
that are unique to their trading partner relationships. Seventy-five percent of the transactions are categorized as transactional exchanges. Twenty-three percent of the documents exchanged are categorized as enhanced information exchange. The remaining two percent were not mapped to either primary group and are identified as miscellaneous. Table 3.3 Network Descriptive Statistics Technology Champion Firms Trading Partner Relationships Transactional Information Exchanges Enhanced Information Exchanges Miscellaneous Information Exchanges Unique Document Types
39 18,644 241,014,613 72,927,949 6,845,464 352
Since the data aggregated monthly by the EDI integrator is oriented to the technology champion firms, the number of trading partners on the network will be slightly overstated. This occurs when a technology champion firm is a trading partner of another technology champion firm. When a technology champion firm is a supplier or customer of another technology champion firm, they will be included as both a technology champion firm and a trading partner. This “double counting” is not be an issue for two reasons. First, the firm should be treated differently in each observation because of the role it plays in the information exchange—either as the technology champion or as the trading partner. Second, the double counting has minimal impact on the study outcomes due to the large number of trading partners and the relatively small number of technology champion firms. 3.4.2 Descriptive Statistics of the Supply Chain Echelons Within the network, technology champion firms represent three distinct echelons of the supply chain (manufacturing, wholesale trade, and retail trade). Using the
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information exchange matrix introduced in Figure 2.2, the portfolio of trading partner relationships can be characterized for each echelon of the supply chain. By considering the types of information exchanged with each trading partner, a descriptive analysis provides some understanding of how each echelon uses information exchange to interact with their pool of trading partners. Trading partner relationships can be categorized using the typology identified in Figure 2.2. By comparing the volume of transactional and enhanced information exchange for each trading partner relationship to the mean for the technology champion firm, each trading partner relationship can be associated with a quadrant in the information exchange matrix. Figure 3.2 shows the distribution of the trading partner relationships in the network based on the information exchange quadrants.
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Figure 3.2 Trading Partner Relationship by Information Exchange Quadrant
Transactional High Information mean Exchange Volume Low
I. Transactional Relationships N=1,170
II. Close Relationships N=1,345
III. Arms-length Relationships N=15,111 Low
mean
IV. Enhanced Relationships N=1,018 High
Enhanced Information Exchange Volume
Each trading partner relationship is associated with a quadrant of the matrix based on the mean exchange volume of the technology champion firm in the relationship. As shown in Figure 3.2, eighty percent of the trading partner relationships in the network are characterized as arms-length based on their relatively low volumes of transactional and enhanced information exchange. Conversely, seven percent of the trading partner relationships in the network are characterized by a high volume of both transactional and enhanced information exchange as identified by the III Close Relationships quadrant. This distribution of relationships supports research which suggests that firms are showing a tendency toward managing their trading partner relationships as a portfolio consisting of a wide array of types (Krapfel et al. 1991). These relationship types are often characterized by their closeness and their ability to support strategic initiatives.
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Technology champion firms vary in their use of close and arms-length relationships. From a supply chain echelon perspective, manufacturing firms have the lowest percentage of arms-length relationships of any of the three echelons but it still represents seventy-five percent of their relationships. Retail trade firms have the highest percentage of arms-length relationship at eighty-two percent. Expanding this analysis to consider the performance of trading partner relationships operating in each quadrant may provide insight into a relationship between information exchange characteristics and relationship performance. Strategy research has recognized survival as a measure of firm performance (Shaver and Flyer 2000). The importance of relationship survival is echoed in marketing literature which recognizes that firms are reducing the number of trading partners they exchange with in order to develop competitive benefits with selected trading partners (Spekman 1988). Additionally, researchers have noted that over time, dissatisfied exchange partners will leave the relationship to seek new partners (Hirschman 1970). Subsequently, trading partners can benefit by avoiding termination. Performance in an electronic exchange network can be observed as exchange continued over time. As illustrated in Table 3.4, trading partner relationships characterized by low levels of transactional and enhanced information exchange experience the highest termination rates. Termination here is measured by exchange occurring in 2004 but not in 2005. The unit of observation is limiting since it creates the possibility that a trading partner relationship may actually be terminated in 2004 which would generate low volumes of information exchange which could potentially assign the relationship incorrectly to the III Arms-length quadrant. Table 3.4 does provide some 53
initial insight into the variation of relationship termination rates across the network. It should be noted that the absence of information exchange through the network is assumed to indicate the termination of the business relationship. The potential to assign relationships to incorrect quadrants is addressed in subsequent chapters by using multiple methods which evaluate information exchange characteristics and performance on a month-to-month and quarter-by-quarter basis. The use of smaller discrete time period observations allows for a closer association of information exchange characteristics and the performance outcomes. Retailers on average show the highest termination rate with trading partners characterized as arms-length (41%) and lowest termination rate with trading partners characterized as close relationships (8%). Manufacturers and wholesalers show similar differences between arms-length and close relationships however, wholesalers on average have the lowest termination rate with trading partner relationships characterized as transactional (3%). Additional analysis will be conducted in each study to explore how the effects of information exchange may vary based on the location of a firm within the supply chain.
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Table 3.4 Average Termination Rates by Firm (From 2004 to 2005)
Firm I. Transactional Relationships Termination Rate Manufacturer A Manufacturer B Manufacturer C Manufacturer D Manufacturer E Manufacturer F Manufacturer G Manufacturer H Manufacturer I Manufacturer J Manufacturer K Manufacturer L Manufacturer M Manufacturer N Manufacturer O Manufacturer P Manufacturer Q Manufacturer R Manufacturer S Manufacturer T Manufacturer U Manufacturer V Manufacturer W Mfg Average Wholesaler A Wholesaler B Wholesaler C Wholesaler D Wholesaler E Whslr Average Retailer A Retailer B Retailer C Retailer D Retailer E Retailer F Retailer G Retailer H Retailer J Retailer K Retailer L Rtlr Average Overall 4% 0% 0% 0% 0% 0% 7% 4% 23% 0% 100% 22% 11% 20% 0% 0% 2% 0% 0% 11% 20% 67% 0% 12% 0% 2% 10% 0% 3% 3% 13% 0% 0% 12% 17% 20% 43% 12% 11% 4% 4% 10% 10% N 47 6 4 3 11 1 15 23 22 4 14 28 18 5 1 7 62 2 4 9 10 3 6 13 23 41 21 24 64 35 23 63 17 265 18 5 7 222 9 51 12 63 1,170 II. Close Relationships Termination Rate 16% 0% 0% 0% 0% 33% 0% 0% 0% 0% 100% 18% 18% 17% 0% 0% 2% 0% 0% 0% 13% 0% 0% 8% 3% 5% 18% 2% 3% 4% 5% 2% 0% 14% 11% 0% 0% 7% 19% 3% 0% 8% 7% III. Arms-length Relationships Termination Rate 41% 10% 14% 58% 19% 29% 57% 17% 35% 14% 100% 49% 32% 41% 11% 10% 9% 25% 19% 29% 22% 8% 10% 30% 32% 35% 34% 17% 16% 23% 23% 14% 0% 48% 28% 46% 90% 49% 33% 27% 28% 41% 36% N 354 41 69 45 223 52 83 88 83 22 110 205 146 63 19 96 481 81 31 21 91 13 41 107 324 251 219 418 827 408 397 303 189 2,203 198 99 68 4,484 255 1,050 1,368 965 15,111 IV. Enhanced Relationships Termination Rate 0% 0% 33% 0% 4% 0% 33% 7% 33% 0% 100% 20% 57% 0% 25% 0% 0% 0% 0% 33% 25% 20% 0% 10% 0% 29% 18% 2% 4% 7% 7% 5% 0% 29% 0% 0% 40% 9% 8% 1% 0% 10% 10% N 12 4 3 4 49 2 3 14 18 3 5 5 7 6 4 6 57 5 26 3 4 10 4 11 9 14 11 55 53 28 15 41 1 90 1 1 5 316 12 113 27 57 1,018 Overall Termination Rate 35% 8% 12% 44% 14% 28% 45% 13% 32% 10% 100% 44% 30% 34% 12% 9% 7% 22% 8% 24% 21% 12% 8% 25% 27% 29% 30% 13% 14% 19% 20% 10% 0% 42% 26% 43% 80% 41% 30% 23% 27% 35% 31% N 19 1 10 7 23 6 8 8 3 1 5 11 6 11 1 3 96 6 9 1 15 0 1 11 32 19 17 57 58 37 42 63 14 185 18 3 3 492 27 31 33 83 1,345 N 432 52 86 59 306 61 109 133 126 30 134 249 177 85 25 112 696 94 70 34 120 26 52 142 388 325 268 554 1,002 507 477 470 221 2,743 235 108 83 5,514 303 1,245 1,440 1,167 18,644
Termination Rate is based on relationship termination prior to January 1, 2005
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One might assume that the trading partner relationships in the III Arms-length quadrant provide sporadic materials on an as-needed basis since they are characterized by low levels of transactional exchange, however, there are other trading partner relationships with similar low levels of transactional information exchange that experience significantly lower levels of termination. Trading partner relationships in the IV Enhanced quadrant are characterized by similar low levels of transactional information exchange but experience termination rates below ten percent across any of the three echelons. From an information exchange perspective, the difference for trading partner relationships in the IV Enhanced quadrant also includes the exchange of relatively high levels of information beyond what is exchanged to support the routine order cycle. This dissertation will develop this relationship between termination rates and the closeness of trading partner relationships. 3.5 Research Question Theory and extant research recognize the critical role of information exchange in supply chains. The descriptive analysis in this chapter indicates that technology champion firms vary in how they use information to interact with their trading partners. Firms differ in their use of distinct types of information, volumes of information exchange, and their balance between sending and receiving information. Theory supports that information exchange affects the performance of both relationships and individual firms. This descriptive analysis supports that based on relationship termination, performance varies with information exchange characteristics
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when relationship performance is defined as survival. The literature is not clear on what characteristics of information exchange affect supply chain performance. This study proceeds with Chapter 4 by developing hypotheses and empirically testing relationship performance based on relationship termination. This approach focuses specifically on how information exchange affects trading partner relationship survival.
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Chapter 4: Trading Partner Relationships
4.1 Introduction This part of the dissertation contributes to the literature by providing an empirical examination of the effects of information exchange on trading partner relationship termination. Specifically, this study provides a longitudinal examination of trading partner relationship termination in an electronically-mediated B2B network. The network includes observations of trading partner relationships across the manufactures, wholesalers and retailers. The level of analysis of this study is the trading dyad. Dyadic studies have been of great interest to researchers since it is the smallest relational unit in the supply chain where interfirm actions can enhance or cripple the supply chain (Anderson et al. 1994; Dwyer et al. 1987; Dyer and Singh 1998; Svensson 2004; Whipple et al. 2002). Research into supply chain relationships often takes either a buyer’s or seller’s perspective and focuses on the performance of one party. This dyadic study uses termination as the performance measure recognizing that both participants invest in the business relationship and neither achieves future relational benefits if the relationship is terminated. By adopting a dyadic level of analysis, this study captures the unique characteristics that are often overlooked in a firm-level or industry-level analysis. The performance measure of interest in this study is relationship termination. Relationship termination is particularly pertinent as a performance measure when the unit of analysis is a relational dyad. Research has recognized that there is value in supply chain relationships that endure over time. Relationships between supply chain
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participants are noted for creating sustainable competitive advantage because of their ability to develop causal ambiguity and time compression diseconomies (Dierickx and Cool 1989; Dyer and Singh 1998). Causal ambiguity develops in relationships due to the complex nature of the relationship whereby competitors can observe the relationship but still not fully duplicate it such that they can obtain the same competitive results. From an interfirm process orientation, time compression diseconomies recognize that the benefits of the relationship are built over time and that if a competitor desires to duplicate the relationship they will incur greater costs to develop the relationship quickly. Additionally, firms often allocate resources to develop relationships and there is an expectation that the initial investment will provide benefits for an extended period of time (Jackson 1985). When firms invest in relationships and those relationships are terminated, they return to the market to invest in developing new relationships. Firm survival is often the focus of strategic management research due to the inherent interest academically and practically in avoiding termination or exit from a market (Cottrell and Nault 2004; Disney et al. 2003). This study focuses on relationship termination as a performance measure and applies it to the analysis of dyadic supply chain relationships. There are several contributions of this examination into the use of information exchange in supply chains and its impact on relationship termination. First, this study differentiates between the types of information exchanged within B2B industrial supply chain relationships. The effects of transactional information exchange are separated from the effects of enhanced information exchange. Second, objective measures of longitudinal information exchange are captured and used for empirical research. Third, the use of two statistical methodologies, provides an improved understanding of how
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individual firms and their exchange of information is associated with the termination of their trading partner relationships. These results are expanded by associating the relational outcomes of firms in different echelons of the supply chain (i.e. manufacturers, wholesaler, and retailers) with their use information exchange in their trading partner relationships. The remainder of this chapter is organized as follows. Section 4.2 provides a development of hypotheses; section 4.3 develops the research methodology; section 4.4 provides the model results and hypotheses tests; section 4.5 discusses the contribution and limitations; and section 4.6 concludes this portion of the dissertation. 4.2 Development of Hypotheses From TCT, transactional volume is an important part of understanding the closeness of a supply chain relationship. Webster (1992) recognized that interfirm transactions were migrating from discrete market transactions toward relational exchange where firms experience recurring transactions over time. As firms move away from discrete market transactions, they have the potential to identify new opportunities through coordination with their trading partners as they understand each other’s needs more through repeated exchanges (Webster 1992). In an arms-length market transaction, there is minimal expectation of future exchange so the motivation to perform is limited. Under such conditions, trading partners may choose to not fulfill the contract with limited ramifications. Arms-length transactions are characterized by: limited investment of specific assets, minimal information exchange, low levels of interdependence, low transaction costs, and minimal
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investment in governance structures (Dyer and Singh 1998; Williamson 1985). The result is that the buyer experiences minimal costs related to selecting a new supplier for subsequent purchases – there is nothing unique about the relationship. In this situation there is no commitment or loyalty to the relationship which allows either party to exit the relationship without expression of cause or justification (Hirschman 1970). As transactions move from discrete to recurring, the foundations of a relationship emerge (Webster 1992). A distinction between discrete and relational exchange then is the repetition of procurement exchanges over time. As transactions move away from discrete market events, there is the potential to develop relational aspects including trust, dependence, and loyalty (Hirschman 1970; Morgan and Hunt 1994). The replacement of market transactions with multi-year contracts further creates an environment of lock-in whereby switching costs are legally imposed to minimize exit (Shapiro and Varian 1998). Moving away from market transactions introduces a longer-term orientation and closer relationships (Kalwani and Narayandas 1995). As dependence increases, the opportunity for trading partners to act in their own best interests increases. A sole supplier of an input has the opportunity to extract additional margin with less chance of repercussions in the short-run. To ensure against such actions, firms may increase monitoring mechanisms or realign their relationships in order to minimize risk (Williamson 1975). At higher levels of transactional volume, termination may actually become a greater risk such that a non-linear relationship exists between transactional volume and relationship termination.
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To test the combination of these opposing forces, a non-linear hypothesis is put forth as, Hypothesis 1: There is a u-shaped relationship between transactional information exchange volume and relationship termination. As relationships move beyond a basic transactional focus, firms can create unique competitive advantages (Dyer and Singh 1998). By exchanging information, firms identify opportunities to combine complementary resources. A manufacturer, The Campbell Soup Company, formed ties with their retailers beyond the sales-to-purchasing interface (Lee et al. 1999). These additional linkages provided information that supported the development of processes whereby Campbell’s could monitor end customer demand and provide inventory management services for their retailers. Campbell’s enhanced their relationship by embedding interfirm routines that provided unique value to the retailer. The retailer experienced improved performance in the form of lower stockouts and higher inventory turnover. Research has also recognized that too much information can be detrimental to performance. Through a simulation, researchers allowed for large volumes of point-ofsale information to flow upstream in the supply chain (Steckel et al. 2004). Results of their study indicate that depending on the demand variability, additional information may not aid the planning process but actually diminish performance by distracting upstream decision makers. One might recognize the potential to overload a relationship with information when IT is used to mediate the exchange. The marginal cost of sending additional information is low once the fixed costs of formatting the data and establishing
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the network are incurred. One might argue that the additional information could be ignored but the filtering of information required to identify the pertinent information may still diminish performance as previously found. Receiving an overload of information can be detrimental to performance either because the receiver must filter the information to identify the pertinent parts or may become frustrated and ignore all the information from the sender. This diminishing affect of too much information suggests that there is a non-linear relationship between the volume of enhanced information exchange and relationship performance. Thus, Hypothesis 2: There is a u-shaped relationship between enhanced information exchange volume and relationship termination. Enhanced information may be more indicative of a close relationship and, therefore, will decrease the likelihood of relationship termination. Strategy research recognizes the value of information exchange beyond the sales-to-purchasing interface (Dyer and Singh 1998). This enhanced information exchange occurs when multiple interorganizational functions integrate. In practice, this occurs when the serial interface between the marketing function of the selling firm and the sales function of the buying firm is augmented by the creation of parallel exchanges between multiple operational areas in each firm. These multiple functional interfirm interfaces are noted as a source innovation such that new products, services, and technologies are developed (Dyer and Singh 1998). The sales-to-purchasing interface is supported by routine order cycle document exchanges (requisitions, purchase orders, invoices, etc.). Exchanges between
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the functional areas of two firms would be supported through the exchange of additional information described in this study as enhanced (demand data, forecasts, inventory levels, forecasts, production schedules, etc.). Given the additional functionality supported by the exchange of enhanced information, one could expect that a single procurement transaction might be the result of the multiple exchanges of enhanced information. A purchase order could be preceded by the exchange of demand forecasts, production schedules, and inventory positions which improve the quality of the purchase decision. After ordering, additional information on shipment schedules, production sequences, and quality control tests may be provided. This post-sale information provides additional value by smoothing the integration of the purchased input. The order cycle information is then preceded and followed by enhanced information which improves the quality of the decision and the use of the delivered input. Research has recognized that early implementations of IT for information exchange in the supply chain were oriented toward transactional efficiency (Narasimhan and Kim 2001; Williams et al. 1997). These studies acknowledge that the use of information exchange goes beyond supporting the foundational processes of the support activities in the supply chain. Specifically, IT can be used to affect the primary activities of the value creation which include: operations, inbound logistics, outbound logistics, marketing, sales, and service functions (Porter 1980). As firms look to implement justin-time (JIT) and other strategic programs with their suppliers, key information is exchanged in order to synchronize interfirm processes (Bardi et al. 1994). Through the exchange of enhanced information, firms are able to integrate similar functions,
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consolidate redundant functions, and eliminate unnecessary activities thus improving their performance beyond low cost transactional processing. Thus, Hypothesis 3: The ratio of enhanced to transactional information exchange volume is negatively associated with relationship termination. The three proposed hypotheses are summarized in Figure 4.1. Hypothesis 1 and Hypothesis 2 identify u-shaped relationships between the information exchange characteristics and relationship termination. Hypothesis 3 proposes a negative relationship between the ratio of enhanced to transactional information exchange and relationship termination. Figure 4.1 Information Exchange Hypotheses
Information Exchange Characteristics Transactional Information Exchange Volume
H1 (u-shaped)
Enhanced Information Exchange Volume Ratio of Enhanced To Transactional Information Exchange Volume
H2 (u-shaped)
Relationship Termination
H3 (-)
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4.3 Research Methodology The goal of this study is to explore the relationship between information exchange and relationship termination in a business-to-business dyad. Using the electronicallymediated trading partner network detailed in Chapter 3, specific measures of information exchange are defined to support hypothesis testing to associate dyadic information exchange characteristics with relationship termination. Since previous research has not addressed information exchange characteristics and supply chain performance at this level of granularity, a multi-level analysis is provided. First, a logistic regression model is developed to support an event history analysis using the information exchange characteristics as independent variables and relationship termination as a dichotomous dependent variable. Second, an alternative event history analysis is developed using the Cox Proportional Hazards Model. 4.3.1 Data The data provided for this study support multiple levels of analysis. Analysis of trading partner relationship termination is best understood through longitudinal data at the dyadic relationship level. The dataset includes monthly observations of information exchange between each of the thirty-nine technology champion firms and their electronically-mediated trading partners. Each dyadic relationship is characterized by its information exchange and its potential termination. Using multiple analysis tools, this portion of the dissertation will evaluate trading partner relationship termination at a dyadic level.
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A key aspect of event history analysis is identifying the group of study participants that are at risk of the hazard occurring during a given period. Trading partner relationships that are at risk during a given time period are referred to as the “risk set” (Allison 1985). In a supply chain context, trading partner relationships that are terminated in January are no longer at risk in February. At the end of each period in the study, the set of study participants is reduced by the number of relationships that experience termination. Event history studies require knowledge of the number of participants that are at risk each period and the number of participants that experience the hazard (termination) each period. Event history analysis then attempts to associate explanatory variables with the variation in the period-to-period hazard occurrences. 4.3.2 Measures Trading Partner Relationship Termination The termination of a trading partner relationship is operationalized through the data of an electronically mediated information exchange network. Within a given period of time, the technology champion firm either transacts with the trading partner or they do not. When information ceases to be exchanged, the relationship is considered to be terminated. A binary variable is used to identify whether the relationship has been terminated. For each month, the hazard variable is coded as ‘0’ if information is exchanged within the dyad. When the dyadic relationship ceases to exchange information, the hazard variable is coded ‘1’ to recognize the termination of the relationship. Due to the potential seasonality of some trading partner relationships, gaps may occur in the month-to-month data. These gaps would make it appear that a dyadic
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relationship was terminated and then re-started one or more months later. In a supply chain context, seasonality of some relationships may create instances where a dyad does not exchange information for a month or more but the relationship itself is continuing with transactions appearing later in the study period. A supplier of winter coats may not receive orders from a specific retailer during the spring months, but as the planning for winter sales begins, information will start being exchanged and the relationship will continue. Trading partner relationships are only considered terminated if they stop transacting and do not resume transacting in a later month during the study. The termination variable is set to ‘1’ to identify termination of a relationship only if the information exchange does not resume during the study period. Relationship termination is identified by the absence of transactions in future periods. Since the dataset includes data for twenty-four months, there are no relationships identified as terminated in the last month of the study (month 24). Without data for the 25th month, there is no way to ascertain whether relationships continued or were terminated. This situation is common in longitudinal studies where variables are based on differences between observation periods. As a result, there is no variation in the dependent variable for the twenty-fourth month of the study and those observations are all dropped prior to estimating the model. Transactional Information Exchange Volume The volume of transactional information exchange is measured as a count variable (in millions). Information is identified as transactional based on the categorization of EDI transaction types described in Chapter 2 and specifically listed in Appendix B. Each
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EDI transaction type is identified as containing either transactional information or enhanced information. EDI transactions identified as containing transactional information are aggregated by the month for each dyad. In a longitudinal analysis, the use of a cumulative measure of transactional volume is critical due the nature of termination in a supply chain context. Practitioners recognize that when a relationship is targeted for termination, the exchange of information does not stop immediately but instead decreases over time. This situation occurs either through an intentional “weaning” of the trading partner or simply because the invoices and payments from recent shipments may require additional time before all outstanding transactions are reconciled. Use of a single month measure of information exchange volume would relate the final termination event to the prior month’s potentially miniscule transactional exchanges made to reconcile a relationship whose primary exchange potentially ended months earlier. To address this issue, this study uses aggregate exchange volumes from the start of the study to the final month prior to relationship termination. This treatment of information exchange as a cumulative measure allows for termination to be explained based on a full measure of information exchange characteristics rather than potentially minimal end-of-life information exchanges that would not be representative of the true characteristics of the relationship. To capture the non-linear relationship between transactional information exchange and trading partner relationship termination, a squared variable is introduced. The study of information and knowledge often recognize an increasing or decreasing effect at higher levels of exchange which creates a non-linear relationship. In such studies, the predictor variable causing the non-linear relationship is modeled using both
69
its calculated value and its squared value (Berman et al. 2002; Steckel et al. 2004) For each dyadic monthly observation, the mathematical square of the cumulative transactional information volume is calculated. Enhanced Information Exchange Volume The volume of enhanced information exchange is measured as a count variable (in millions) and is analogous to the transactional information exchange measure described above. Based on the categorization of enhanced information described in Chapter 2 and listed in Appendix C, EDI transaction types are identified as containing either enhanced information or transactional information. EDI transactions identified as containing enhanced information are aggregated by month and trading dyad. To capture the non-linear relationship between the exchange of enhanced information and trading partner relationship termination, a squared variable is introduced as described previously for the transactional exchange volume measure. For each dyadic monthly observation, the mathematical square of the enhanced information volume is calculated. Transactional to Enhanced Information Exchange Ratio To measure the relationship between the exchange of enhanced information and transactional information, a simple ratio is calculated. The ratio addresses the comparative effect of enhanced information to transactional information on the termination of a trading partner relationship. As shown in 4.1, the ratio is calculated by
70
dividing the enhanced information exchange volume by the transactional information exchange volume for each unique trading dyad and month combination.
RATIO ijt =
Enhanced_Volume ijt Transactional_Volume ijt
[ 4.1]
where ij represents a technology champion firm-trading partner dyad and t is a specific time period
Control Variable – Firm Dummy The data for this analysis includes observations of thirty-nine technology champion firms. To control for the firm specific variation in the dataset, a series of dummy variables are included to identify the technology champion firms associated with each observation. The thirty-nine technology champion firms require the inclusion of an additional thirty-eight binary variables. This control variable is required in both the Logistic regression and the Cox Proportional Hazards Model to control for firm effects. Control Variable – Month The data includes monthly observations for each technology champion-trading partner dyad with the potential for each dyad to include up to twenty-four monthly observations. A series of dummy variables are included to identify the month of each observation and control for variation due to time. The twenty-four monthly observation periods require the use of an additional twenty-three binary month dummy variables. This variable is used in the logistic regression to control for the time effects. Control variables for time are not required in the Cox Proportional Hazards Model since the time factor is controlled using the DURATION variable.
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4.3.3 Modeling Event History The study of events and their causes have been the source of great interest in many academic fields. The event of interest is often failure in the form of death or termination with the studies addressing the potential causes of the event. In the context of supply chain relationships, the hazard encountered is that the trading partner relationship is terminated. This section examines how the hazard (termination of the supply chain relationship) is associated with the characteristics of information exchange. In studying events, the data include the history of the event occurrence and measures of potential explanatory variables. Typically, these explanatory variables change over time creating the need to model the history as a series of longitudinal observations. Using standard regression techniques to model the data often causes unnecessary loss of information (Allison 1985). Similarly, loss of information can occur if the event of interest does not occur during the study period. If the relationship does not terminate during the study period, the length of the relationship is unknown and could be assumed to be the full length of the study, but that too would underestimate its true value in that the relationship could continue for years beyond the study. In event studies, this is referred to as censoring. Censoring is the situation where variables are measured within the range of the study period but their values are unknown prior to the study and after the study. Left-censoring refers to the unknown measures of variables prior to the study. Right-censoring refers to the unknown values after the study. Censoring also occurs when a participant in the study leaves the study prior to its completion and without experiencing the hazard event. In an electronically mediated trading partner network, firms may migrate to new technologies or change network providers which could 72
potentially eliminate them from the study. Models that allow for censoring of data keep the observations of hazard and non-hazard relationships up to the point when the participant leaves the study thus minimizing the effects of lost data points. In either case, the analysis of event history requires unique treatment to avoid the effects of both censored data and time varying explanatory variables. Each of these situations can be addressed using either a logistic regression or a proportional hazard model to estimate the likelihood of the event (Allison 1985). This study presents results for hypotheses tests using both methods. Both the logistic regression and proportional hazards model estimate the likelihood of an event occurring. This allows for an additional verification of the predictive power of the models by holding back a portion of the data for post-hoc analysis of the estimated model coefficients. To facilitate this test, ten percent of the observations are not included in the data used to estimate the coefficients. This randomly generated “hold-out” sample will be used for a post-hoc analysis which is presented in the discussion of the study results. 4.3.4 Logistic Regression Modeling A logistic regression may be used to specify how the probability of an event depends on selected explanatory variables. First, a logistic regression supports the use of a binary dependent variable to identify the event occurrence. The event of interest in this study is the termination of the trading partner relationship. Second, the model estimates the probability of the hazard (termination) occurring during a specific time period given the levels of the explanatory variables.
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Using a discrete-time model, variation in the hazard in each time period is allowed by letting each intercept term (?) identify a unique time period. When using a logistic regression, these time period constants are modeled as a set of dummy variables, one for each of the observed time periods (Allison 1985). Additionally, this dataset contain observations of multiple trading partner relationships for each firm so a vector of firm dummy variables are also included in the model as shown in 4.2.
log ( Pt / (1 ? Pt ) ) = ? t + ?1TRANSACTIONAL_VOLUME t-1 +
?2TRANSACTIONAL_VOLUME2 t-1 + ?3 ENHANCED_VOLUME t-1 + ?4 ENHANCED_VOLUME 2 t-1 +?5 RATIO t-1 + ? ? jMONTH t +? ? i FIRMi + ?
j=0 i=0 n n
[4.2]
where Pt is the probability of termination of a trading partner relationship
A maximum likelihood estimation is used to calculate the coefficients. The overall effect is that the data are modeled such that a unique observation is created to represent each period that a trading partner relationship is at risk. For example, trading partner relationships that terminated after four months contribute four trading partnermonths of observations. Trading partner relationships that have not terminated by the end of the twenty-four months of the study are considered to be censored. Censoring in this context refers to the fact that the relationships may have terminated after the study period but that information is unknown and not within the range of study. Censored trading partner relationships then contribute the maximum twenty-four trading partnermonths. For each trading partner-month, the dependent variable is coded ‘1’ if the trading partner relationship terminated that month, otherwise it is coded zero. The information 74
exchange characteristics then serve as explanatory variables and take on their cumulative values up through the month prior to termination. This lagging of the explanatory variables is critical in the analysis of electronically mediated information exchange. Since the dependent variable (termination) is defined as the absence of any information exchange on the network, the month in which the trading partner relationship is absent will show zero values for both the transactional and enhanced information exchange volumes. A simple lagging of one month then relates the termination in one month to the information exchange characteristics cumulative to the month prior to termination. For example, a trading partner relationship that shows no transactions in October is coded as terminated for the month of October and the information exchange characteristics are measured as cumulative up to the termination occurrence. Since the relationship had no transactions in October, the transactional and enhanced information exchange volumes are cumulative through September. The variables for the logistic regression analysis are summarized in Table 4.1.
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Table 4.1 Definition of Logistic Regression Model Variables Variable Definition Dependent Variable 1 if terminated in that month, 0 otherwise TERMINATIONit Independent Variables TRANSACTIONAL_VOLUMEijt Cumulative volume of transactional information exchange between a trading partner and the technology champion firm 2 TRANSACTIONAL_VOLUME ijt The mathematical square of the cumulative volume of transactional information exchange between a trading partner and the technology champion firm Cumulative volume of enhanced information ENHANCED_VOLUMEijt exchange between a trading partner and the technology champion firm 2 The mathematical square of the volume of enhanced ENHANCED_VOLUME ijt information exchange between a trading partner and the technology champion firm The ratio of cumulative enhanced information RATIOij exchange volume to the cumulative transactional information exchange volume between a trading partner and the technology champion firm A series of binary dummy variables to identify the MONTHt month of observation A series of binary dummy variables to identify the FIRMi technology champion firm As noted by Allison (1985), the logistic regression procedure adequately handles both the censoring and time-varying explanatory variable issues that can be problematic in estimating the probability of a hazard occurring. Censoring is addressed in this study by including trading partner relationship observations for all periods in which their termination was at risk. Variances in the explanatory variables in each period are captured by including each trading partner-month combination as a separate observation and including a series of dummy variables to identify the month. This application of a logistic regression is specifically adapted to account for changes in the hazard rate over time. By including a set of dummy variables for the time periods, the intercept is adapted for each discrete time (Allison 1985). This specification
76
supports the inclusion of time-varying explanatory variables which recognize that the probability of a hazard occurring is explained through the cumulative exchange of information within a relationship while controlling for the effects of time. 4.3.5 Cox Proportional Hazards Model An alternative method to model event history is the Cox Proportional Hazards regression analysis. This technique addresses the occurrence or nonoccurrence of an event and its timing. The Cox Proportional Hazards Model (hereafter simply called the hazards model) has been used in empirical research to study the likelihood of market leader dethronement (Ferrier et al. 1999), manufacturing firm survival in the UK (Disney et al. 2003), and the likelihood of a customer purchasing additional financial products given demographic factors (Thomas et al. 2005). In this study, the event of interest is the termination of the trading partner relationship. The hazards model is one of a series of methods to estimate hazard rates but is often preferred by researchers because it efficiently estimates the model even in studies with large numbers of observations and censored observations (Ferrier et al. 1999; Tuma and Hannan 1984). As discussed previously, observations are considered censored when the event of interest occurred either before the study began or after the study period ended. Censoring of observations can be a concern in the study of supply chain relationships where many of the trading partner relationships do not experience termination during the study period. The hazards model uses each trading partner-month observation since the trading partner relationship was at risk during each month of the study regardless of whether they experienced the hazard event or not.
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Two critical inputs to the hazards model are whether the relationship terminated and how long the relationship lasted. The dependent variable of the model is the dichotomous measure of relationship termination described earlier for the logistic regression model. TERMINATION is a binary measure of relationship termination and is set to ‘1’ if the relationship terminated during the month. DURATION is a count of the number of months that the relationship existed since the beginning of the study period. The variable DURATION is entered as the time function in the model (?). The remaining explanatory variables are as described previously and are summarized in Table 4.2. Table 4.2 Definition of Cox Proportional Hazards Model Variables Variable Definition Dependent Variable 1 if terminated, 0 otherwise TERMINATIONijt Time Function Variable Number of months the trading partner relationship has DURATIONijt existed since the beginning of the study period Independent Variables TRANSACTIONAL_VOLUMEijt Cumulative volume of transactional information exchange between a trading partner and the technology champion firm 2 TRANSACTIONAL_VOLUME ijt The mathematical square of the cumulative volume of transactional information exchange between a trading partner and the technology champion firm Cumulative volume of enhanced information ENHANCED_VOLUMEijt exchange between a trading partner and the technology champion firm 2 The mathematical square of the volume of enhanced ENHANCED_VOLUME ijt information exchange between a trading partner and the technology champion firm The ratio of cumulative enhanced information RATIOijt exchange volume to the cumulative transactional information exchange volume between a trading partner and the technology champion firm A series of binary dummy variables to identify the FIRMi technology champion firm
78
The only assumption of the Cox model is that the hazard is proportional in that for any two individual observations, at any point in time the ratio of their hazard is a constant. Specifically, for any point in time (t), hi(t)/ hj(t) = c where i and j refer to distinct trading partners and c is the constant which depends on the explanatory variables but not on time. Proportionality is tested using the scaled Schoenfeld residuals of the
covariates on functions of time and a visual observation of the plotted residuals. A nonzero slope is an indication of a violation of the proportionality assumption (Allison 1985).
(log)TERMINATIONijt = ?(t-1) + ?1TRANSACTION_VOLUME(t-1) +
2 ?2TRANSACTION_VOLUME(t-1) + ?3ENHANCED_VOLUME(t-1) + 2 ?4ENHANCED_VOLUME(t-1) + ?5RATIO(t-1) +?? i FIRMi + ? i=0 n
[ 4.3]
where i is the technology champion firm, j is the trading partner, and t is the month
4.4 Results This study seeks to expand the understanding of how relationship termination varies according to information exchange practices. Extending the strategic management literature’s use of relationship survival as a key measure of relational performance (Dyer and Singh 1998), the information exchange matrix (Figure 2.2) provides a framework to associate information exchange practices with trading partner relationship performance in the form of relationship termination. 4.4.1 Descriptive Statistics The dataset used for this analysis includes observations of the information exchange transactions for thirty-nine technology champion firms and their electronicallymediated trading partner relationships. These data capture EDI-based electronic
79
document exchanges over a twenty-four month period beginning January 2004 and ending December 2005. Descriptive statistics on the overall dataset are included in Table 4.3. Table 4.3 Descriptive Statistics
Obs TERMINATION 255,076 TRANSACTIONAL_VOLUME 255,076 TRANSACTIONAL_VOLUME^2 255,076 ENHANCED_VOLUME 255,076 ENHANCED_VOLUME^2 255,076 ENHANCED-TRANSACTIONAL_RATIO 255,076 Variable Mean Std. Dev. 0.0252 0.1567 0.0107 0.0802 0.0065 0.4903 0.0033 0.0315 0.0010 0.0536 12.9318 942.7700 Min 0 0 0 0 0 0 Max 1.00 10.56 111.54 3.16 9.98 283,562.50
Monthly information exchange within each technology champion–trading partner dyad results in a large number of observations on which to test the hypotheses. These 237,021 observations are used in both the logistic and Cox Proportional Hazard models. The measures of information exchange described in Chapter 2 and developed into specific measures for the termination models. Transactional volume is measured cumulatively up to the observation month and varies between zero and eleven million document exchanges. The mean of cumulative transactional volume is approximately eleven thousand with a relatively large standard deviation of eighty-three thousand. This indicates that the dyads included in this dataset vary greatly in the amount of transactional information exchanged. Similarly, the measure of cumulative enhanced information exchange varies across the observed dyads. The cumulative volume of enhanced information exchange ranges from zero to approximately three million documents exchanged. The mean is approximately three thousand which is relatively low due to the fact that many dyads
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have an observed value of zero enhanced documents exchanged indicating that they do not exchange information beyond the transactional information. The measure of the ratio of enhanced to transactional information exchange is affected by the variation in both the cumulative transactional and enhanced measures. The mean ratio is approximately thirteen with many dyads reporting a ratio of zero resulting from not exchanging any enhanced information documents. The mean of thirteen indicates an average ratio of thirteen enhanced documents to every transactional document. The ratio measure ranges from zero to 283,563 indicating great variation in the ratio across the dyads. The pairwise correlations provided in Table 4.4 indicate a negative and statistically significant relationship between both the transactional volume and enhanced volume measures with relationship termination. In interpreting the relationship, it is important to remember that termination is coded as ‘1’ if the relationship is terminated and ‘0’ if the relationship is not terminated. These negative correlations indicate that at higher levels of exchange volume, the trading partner relationships are not terminated.
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Table 4.4 Pairwise Correlations
TRANSACTIONAL TRANSACTIONAL _VOLUME _VOLUME^2 TRANSACTIONAL_VOLUME ENHANCED _VOLUME ENHANCEDENHANCED TRANSACTIONAL_ _VOLUME^2 RATIO
1 255,076 0.7651 0.0000 255,076 0.1854 0.0000 255,076 0.0522 0.0000 255,076 -0.0018 0.7087 255,076
TRANSACTIONAL_VOLUME^2
1 255,076 0.0159 0.0000 255,076 0.0053 0.0080 255,076 -0.0002 0.9268 255,076
ENHANCED VOLUME
1 255,076 0.8162 0.0000 255,076 0.0867 0.0000 255,076
ENHANCED_VOLUME^2
1 255,076 0.0346 0.0000 255,076
ENHANCED-TRANSACTIONAL_RATIO
1 255,076
Statistically significant correlations are highlighted in bold and italics
As expected, the linear and non-linear (squared) measures show a positive and statistically significant correlation for both the transactional and enhanced measures. This is not unusual in models that use squared measures for the testing of non-linear relationship. A positive and statistically significant relationship also is identified between the ratio measure (ENHANCED-TRANSACTIONAL_RATIO) and both the linear enhanced information exchange measure (ENHANCED_INFORMATION_VOLUME) and the non-linear enhanced information exchange measure (ENHANCED_INFORMATION_VOLUME^2). The pairwise correlations provide further support for the distinct nature of the transactional and enhanced dimensions of information exchange. Transactional information exchange volume (TRANSACTIONAL_VOLUME) and enhanced information exchange volume (ENHANCED_VOLUME) are positively and statisticallysignificantly correlated at 18%. This positive relationship suggests that the levels of each
82
dimension move in the same direction. However, their relatively low level of correlation suggests that they can distinctively measure two unique types of information exchange. 4.4.2 Logistic Regression Results Results for the explanatory variables of the logistic regression model are provided in Table 4.5. The model fit is statistically significant based on the Likelihood Ratio (LR) Chi-Square test statistic. Although logistic regression does not have an equivalent to the R-squared that is found in ordinary least squares (OLS) regression, the McFadden pseudo R-squared is a provided as an indicator of the explanatory power of the model. The pseudo R-square reported for this model is 10.01%. Full results including the coefficient estimates of the control variables are included in Appendix D. The estimated coefficients of the transactional information exchange and transactional information exchange squared variables are used to test Hypothesis 1. A negative estimated coefficient for the transactional information exchange volume variable (TRANSACTIONAL_VOLUME) indicates a decrease in the likelihood of termination with each increase in transactional information exchange volume. A negative coefficient for the squared transactional information exchange variable (TRANSACTIONAL_VOLUME^2) indicates the non-linear u-shaped relationship that is hypothesized. The negative coefficient for the non-linear term specifies that at higher levels of the transactional exchange volume, the likelihood of termination actually increases. The results support Hypothesis 1.
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Table 4.5 Logistic Regression Results
TERMINATION TRANSACTIONAL_VOLUME TRANSACTIONAL_VOLUME^2 ENHANCED_VOLUME ENHANCED_VOLUME^2 ENHANCED-TRANSACTIONAL_RATIO Coef. (Std Err) -6.2809 (0.7646) 0.6102 (0.0764) -10.2760 (2.1512) 3.2679 (0.8111) 4.41E-06 (0.00002) P>| t | 0.000 0.000 0.000 0.000 0.824 sig ** ** ** ** ns H3: Not Supported H2: Supported Hypotheses Testing H1: Supported
? ?? (technology champion dummy variables)
??
t
i
t
(month dummy variables)
. .
-3.9163 (0.1402) 255,076 5,684.44 0.0000 0.0948 -27,150.29
. .
0.000
. .
**
constant observations LR chi2 Prob > chi2 Pseudo R2 Log Likelihood
**| t | 0.000 0.000 0.000 0.000 0.885 sig ** ** ** ** ns
??
i
?
(technology champion dummy variables)
??t (month dummy variables)
t
. .
-2.0442 (0.1416) 51,420 3,169.00 0.0000 0.2468 -4,851.56
. .
0.000
. .
**
. .
-2.9665 (0.2050) 43,321 902.26 0.0000 0.1265 -3,114.46
. .
0.000
. .
**
. .
-2.2066 (0.1547) 160,335 3,523.42 0.0000 0.0886 -18,130.01
. .
0.000
. .
**
constant observations LR chi2 Prob > chi2 Pseudo R2 Log Likelihood
** chi2 Log Likelihood
**chi2 Log Likelihood Z -3.25 3.45 -2.63 1.64 -0.71 18.88 --24.95 -3.00 13.54 13.42 -3.18 0.70 --2.07 ---0.32 31.09 7.69 12.33 9.02 ---0.55 15.42 --4.90 --1.34 -1.11 16.51 22.20 --6.53 20.06 P>|z| 0.0010 0.0010 0.0090 0.1010 0.4770 0.0000 --0.0000 -0.0030 0.0000 0.0000 -0.0010 0.4850 --0.0390 --0.7460 0.0000 0.0000 0.0000 0.0000 ---0.5820 0.0000 -0.0000 --0.1820 0.2690 0.0000 0.0000 --0.0000 0.0000 sig * * * ns ns ** --** -* ** ** -* ns --* --ns ** ** ** ** ---ns ** -** --ns ns ** ** --** **
51,420 2,636 0.0000 -11,757.96
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Table E.3 Stratified Hazard Model Results: Wholesalers
TERMINATION Coef. Std. Err TRANSACTIONAL_VOLUME -34.7517 5.4582 TRANSACTIONAL_VOLUME^2 3.2472 0.5900 ENHANCED_VOLUME -8.7122 5.2078 ENHANCED_VOLUME^2 2.6181 1.8341 ENHANCED_TRANSACTIONAL RATIO 2.33E-05 1.49E-05 Control Variables FIRM_DUMMY1 --FIRM_DUMMY2 -0.9459 0.1547 FIRM_DUMMY3 0.0500 0.1269 FIRM_DUMMY4 --FIRM_DUMMY5 --FIRM_DUMMY6 --FIRM_DUMMY7 --FIRM_DUMMY8 --FIRM_DUMMY9 --FIRM_DUMMY10 --FIRM_DUMMY11 --FIRM_DUMMY12 --FIRM_DUMMY13 --FIRM_DUMMY14 --FIRM_DUMMY15 --FIRM_DUMMY16 --FIRM_DUMMY17 --FIRM_DUMMY18 --FIRM_DUMMY19 --FIRM_DUMMY20 --FIRM_DUMMY21 --FIRM_DUMMY22 --FIRM_DUMMY23 --FIRM_DUMMY24 --FIRM_DUMMY25 --FIRM_DUMMY26 --FIRM_DUMMY27 --FIRM_DUMMY28 -1.6967 0.1517 FIRM_DUMMY29 --FIRM_DUMMY30 --FIRM_DUMMY31 --FIRM_DUMMY32 --FIRM_DUMMY33 --FIRM_DUMMY34 --FIRM_DUMMY35 -0.5020 0.1070 FIRM_DUMMY36 --FIRM_DUMMY37 --FIRM_DUMMY38 --Model Statistics Observations LR chi2 Prob>chi2 Log Likelihood Z -6.37 5.50 -1.67 1.43 1.56 --6.11 0.39 -------------------------11.18 -------4.69 ---P>|z| 0.0000 0.0000 0.0940 0.1530 0.1190 -0.0000 0.6930 ------------------------0.0000 ------0.0000 ---sig ** ** + ns ns -** ns ------------------------** ------** ----
43,321 329 0.0000 -6,419.96
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Table E.4 Stratified Hazard Model Results: Retailers
TERMINATION Coef. Std. Err TRANSACTIONAL_VOLUME -15.0735 1.4312 TRANSACTIONAL_VOLUME^2 4.5713 0.4082 ENHANCED_VOLUME -51.6863 7.4789 ENHANCED_VOLUME^2 30.8093 4.1358 ENHANCED_TRANSACTIONAL RATIO 1.67E-04 0.000321 Control Variables FIRM_DUMMY1 --FIRM_DUMMY2 --FIRM_DUMMY3 --FIRM_DUMMY4 --FIRM_DUMMY5 -0.7456 0.1563 FIRM_DUMMY6 --FIRM_DUMMY7 --FIRM_DUMMY8 --FIRM_DUMMY9 -1.1698 0.1604 FIRM_DUMMY10 --FIRM_DUMMY11 --FIRM_DUMMY12 2.5306 0.1520 FIRM_DUMMY13 -0.4184 0.1386 FIRM_DUMMY14 --FIRM_DUMMY15 -0.5060 0.1697 FIRM_DUMMY16 --FIRM_DUMMY17 --FIRM_DUMMY18 --FIRM_DUMMY19 --FIRM_DUMMY20 --FIRM_DUMMY21 --FIRM_DUMMY22 --FIRM_DUMMY23 -0.1775 0.2989 FIRM_DUMMY24 -0.3869 0.1358 FIRM_DUMMY25 --FIRM_DUMMY26 --FIRM_DUMMY27 1.2830 0.1475 FIRM_DUMMY28 --FIRM_DUMMY29 --FIRM_DUMMY30 -1.4972 0.1484 FIRM_DUMMY31 --FIRM_DUMMY32 --FIRM_DUMMY33 --FIRM_DUMMY34 --FIRM_DUMMY35 -1.1059 0.1413 FIRM_DUMMY36 --FIRM_DUMMY37 --FIRM_DUMMY38 --Model Statistics Observations LR chi2 Prob>chi2 Log Likelihood Z -10.53 11.20 -6.91 7.45 0.52 -----4.7700 ----7.2900 --16.6500 -3.0200 --2.9800 --------0.5900 -2.8500 --8.7000 ---10.0900 -----7.82 ---P>|z| 0.0000 0.0000 0.0000 0.0000 0.6030 ----0.0000 ---0.0000 --0.0000 0.0030 -0.0030 -------0.5530 0.0040 --0.0000 --0.0000 ----0.0000 ---sig ** ** ** ** ns ----** -----** * -* -------ns * --** --** ----** ----
160,335 2,549 0.0000 45,616.05
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APPENDIX F Calculating Sales Surprise
This study evaluates two standard forecasting methods based on historical data to calculate the sales surprise. Although alternative forecasting methods are available, the two methods selected provide robust forecasts for evaluation and are commonly used in practice. A moving average forecast uses actual data values from prior periods to generate a forecast. A moving average forecast is generated using the following equation:
n
F t = MAn =
?
?A
i =1
t ?i
n
=
At ? n + ..... + At ? 2 + At ?1 n
[ F.1]
?
Where F t is the forecast for time period t , MAn identifies the number of periods included in the moving average window, At-1 is the actual value in period t-1, and n is the number of data points in the moving average (Stevenson 2007). The moving average forecast can include as many data points as desired; however, since it is averaging or smoothing the historic values increasing the number of data points decreases the responsiveness of the forecast to recent changes. Similarly, decreasing the number of data points increases the sensitivity of the forecast to recent changes. Moving average forecasts are popular in practice but require the decision maker to determine the appropriate number of periods to include in the forecast. For the purposes of this study, moving average forecasts were calculated for all firms based on the inclusion of two, four, and eight periods.
163
Exponential smoothing (also called Single Smoothing or noted “SS”) is a more sophisticated averaging forecast method which can incorporate large amounts of historic data points while placing emphasis on the more recent events. The exponential smoothing forecast adds a percentage of the difference between the prior period forecast and the actual value observed during the prior period. An exponential smoothing forecast is generated using the following equation:
? ? ? ? ? F t = F t ?1 +? ? A t-1 ? F t ?1 ? ? ?
[ F.2]
Where Ft is the forecast for period t, F t ?1 is the forecast for the prior period, ? is a smoothing constant, and At-1 is the actual value observed during the prior period (Stevenson 2007; Tersine 1993). This forecast method is generated over time, such that the forecasts and forecasting error in each of the previous periods are incorporated into the current forecast, the model is more generally stated as:
? t ?
?
F t = ? ? (1 ? ? )
k =1
k ?1
At ? k + (1 ? ? ) F 0
t
[ F.3]
?
?
Where F t is the forecasted demand level for period t , F 0 is the forecasted demand level for the initial period, and At ?1 is the actual demand for period t ? 1 . The smoothing constant represented by ? incorporates a percentage of the previous forecast error into the new forecast. The responsiveness of the forecast is then driven by the magnitude of the smoothing constant. The closer the smoothing constant is to zero, the
164
slower the forecast will adjust for forecasting error in the prior period. A smoothing constant closer to 1.00 will be very responsive to recent forecast error. In practice, the smoothing constant is selected based on judgment of the decision maker or trial and error. Smoothing constants commonly range from 0.05 to 0.50 depending on the underlying demand behavior and business responsiveness needs. For the purposes of this study, smoothing constants of 0.05, 0.25, and 0.50 were used to estimate forecasts for all firms. Results of these forecasts will be discussed at the end of this section. Since the exponential smoothing forecast uses the historic period error to calculate the current forecast, a starting forecast must be identified. An average of the first several periods is often used to generate the starting forecast (Stevenson 2007). For the purposes of this study, a moving average forecast based on the four quarters of 2003 was used to generate a starting forecast for first quarter of 2004. The exponential smoothing forecast was then run starting in first quarter (Q1 2004) through the next seven periods to the end of the study (Q4 2005). The resulting six forecasts (MA2, MA4, MA8, SS?=0.05, SS?=0.25, SS?=0.50) are then compared to identify the best fit for estimating sales. A key measure used to select forecast methods is forecast accuracy (Stevenson 2007). Forecast accuracy is operationalized as the difference between the actual observed value during a period and the forecasted value for the period (error).
?
et = At - F t
[ F.4]
165
Where et is the forecasting error for period t based on the difference between the actual
?
observed value for the period (At) and the forecast for the period ( F t ). A larger value of error indicates greater inaccuracy in the forecast. Positive error then is the result of a forecast that was too low, negative error is the result of a forecast that was too high relative to the actual observed value for the period. Forecast error is aggregated using one of two standard measures. The two measures are; the mean squared error (MSE) and mean absolute deviation (MAD).
MAD =
?e
n
[ F.5]
[ F.6]
MSE =
?e
n
2
The MAD is the average absolute difference between the forecast and observed values for each period-firm combination. By using the absolute value of the difference, the canceling effects of negative and positive error are eliminated. Similarly, the MSE method eliminates the canceling effects of positive and negative error by squaring the period-firm error. The difference in the two methods is that the MAD treats all error equally where the MSE weights the errors based on their squared values. In either case, lower aggregate results identify less error in the forecasting method (Stevenson 2007). As shown in Table F.1, the moving average methods produce lower error than the exponential smoothing methods. The lowest overall error is produced by the moving average 4-period and moving average 4-period specifications. This holds for both the mean squared error and mean absolute deviation methods of calculating forecast error.
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TABLE F.1 Forecast Accuracy by Forecast Method
Forecast Method Moving Average - 2 Periods Moving Average - 4 Periods Moving Average - 8 Periods Exponential Smoothing ?=0.05 Exponential Smoothing ?=0.25 Exponential Smoothing ?=0.50 Mean Squared Error 262,994,910 211,310,507 351,056,626 648,074,453 1,436,286,821 4,312,283,372 Mean Absolute Deviation 170,127 156,971 210,372 274,312 371,650 547,632
For the purposes of this study, moving average 4-period is used to calculate the firmperiod forecasted sales, which is used in deriving the per-period sales surprise.
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APPENDIX G Alternative Dependent Variable Results
The following tables provide model fit statistics and coefficient estimates for two alternative model specifications. Table G.1 provides output for the alternative Net Income dependent variable. Table G.2 provides output for the alternative Receivables Turnover dependent variable. Table G.3 includes the pairwise correlations for the primary dependent variable (INVENTORY_TURNOVER) and both alternative dependent variables.
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TABLE G.1 Alternative Dependent Variable: Net Income
n groups R-square within between overall 170 39 0.2036 0.5991 0.4371 3.12 0.0014
F Prob>F
(log)NET_INCOME
Coef. (Std Err) 0.5085 (0.2819) -0.2039 (0.1342) 0.1429 (0.3047) -0.0917 (0.0949) 25.8130 (10.5323) -2.3305 (1.1113) 2.7638 (0.6295) 0.0004 (0.0976) 0.5500 (0.2019) 0.2888 (0.1767) 0.3513 (0.1760)
P>| t |
sig
Explanatory Variables (log)CLOSE (log)ASYMMETRY (log)CONCENTRATION (log)CHURN_RATE constant Control Variables (log)FIRMSIZE (log)SALES_SURPRISE (log)NET_INCOME_LAG SEASON1DUMMY SEASON2DUMMY SEASON3DUMMY
**
Supply chain management (SCM) is the management of an interconnected or interlinked between network, channel and node businesses involved in the provision of product and service packages required by the end customers in a supply chain.
ABSTRACT
Title of Document:
SUPPLY CHAIN STRATEGY AND THE BENEFITS OF INFORMATION EXCHANGE
Tobin Edward Porterfield, Ph.D., 2007 Directed By: Professors Philip T. Evers, Associate Professor, Department of Logistics, Business, and Public Policy and Joseph P. Bailey, Research Associate Professor, Department of Information and Technology
Abstract: This dissertation investigates the use of information exchange in industrial supply chain relationships. Specific information exchange characteristics are analyzed to determine their contribution to firm performance from the perspective of both the technology champion firm and the trading partner firm. Longitudinal analyses are conducted using data gathered from an electronically mediated industrial exchange network. This unique dataset, which includes information exchange data for thirty-nine technology champion firms and their electronically integrated trading partners across a two-year observation period, provides distinct insights into the application and outcomes related to information exchange in contemporary supply chains. The analysis of this large volume of information exchange transactions identifies best practices in the use of information exchange and their impact on firm performance.
SUPPLY CHAIN STRATEGY AND THE BENEFITS OF INFORMATION EXCHANGE
By
Tobin Edward Porterfield
Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2007
Advisory Committee: Professor Philip T. Evers, Co-chair Professor Joseph P. Bailey, Co-chair Professor Thomas M. Corsi Professor Martin Dresner Professor Jennifer Preece Professor Ping Wang
© Copyright by Tobin Edward Porterfield 2007
Dedication
To my wife Sue and my sons, Josh and Nick, whose love and support allowed me to keep my eye on the prize and finish this race.
ii
Acknowledgements
This dissertation is a result of the guidance, support, and encouragement of many faculty, students, family, and friends. My advisory committee including Professors Joe Bailey, Phil Evers, Martin Dresner, Tom Corsi, Jenny Preece, and Ping Wang provided much needed direction throughout this process. I am so grateful for their interest and inspiration in bringing this together. A special thanks to Joe Bailey and Phil Evers for their critical roles as co-chairs of my committee. Their knowledge and patience pushed me beyond my perceived abilities. Professor Bailey mentored me starting in my first semester at the University of Maryland and spent countless hours investing his time in my training. I am certain that this dissertation is only the beginning of our work together. Martin Dresner is a constant guide and friend. From our meeting years before entering the Ph.D. program and straight on through, Professor Dresner has shown a sincere interest in my work and my life. His honest critique and steady guidance have been essential ingredients in my completion of this dissertation. I hope that in my career I can connect with my students the way that Professor Dresner does. I would like to thank my colleagues in the program for providing feedback on early drafts of this research and their incredible team spirit which made the process bearable. Thank you all for your friendship and support. A special thanks to my dearest friends Adriana and Christian Hofer for their patient coaching and enduring camaraderie.
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Brian Lowell from the University of Maryland University College has been a invaluable resource in helping me understand the nuances of electronic information exchange. My family and friends were a source of constant love and support – believing in my dreams when I lost sight of my every aspiration. Words cannot express my appreciation for all that my wife, Sue, has given to me throughout this process. From covering my home responsibilities, to supporting us, encouraging me, keeping our family together, and editing skills that make me the envy of my colleagues. And a very special thanks to my friend Bob Graham for his relentless interest in my research and many hours of editing. The results of he and Sue’s editing skills are the best of the pages that follow – any errors or omissions are all mine.
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Table of Contents
Dedication .......................................................................................................................... ii Acknowledgements .......................................................................................................... iii Table of Contents .............................................................................................................. v List of Tables .................................................................................................................. viii List of Figures................................................................................................................... ix Chapter 1: Introduction ................................................................................................... 1 1.1 Information Exchange in the Supply Chain.............................................................. 1 1.2 Strategic Use of Supply Chain Relationships ........................................................... 4 1.3 Contribution of the Dissertation................................................................................ 6 Chapter 2: Theory and Review of Extant Literature.................................................... 9 2.1 Introduction............................................................................................................... 9 2.2 Transaction Cost Theory in a Supply Chain Context ............................................... 9 2.2.1 The Effect of Information Technology on the TCT Framework............... 15 2.2.2 The Effect of Information Exchange on the TCT Framework .................. 17 2.3 Review of Empirical Literature .............................................................................. 20 2.3.1 Use of Information in Supply Chains ........................................................... 20 2.3.2 Information Exchange to Support Specific Supply Chain Initiatives ....... 22 2.3.3 Using Interorganizational Systems to Exchange Information ................... 23 2.4 Research Model Development................................................................................ 24 Chapter 3: Research Setting and Data.......................................................................... 33 3.1 Use of Electronic Data Interchange Data for Empirical Research ......................... 33 3.2 Formatting Standards and Electronic Intermediaries.............................................. 37 3.2.1 EDI Exchange Networks ............................................................................... 38 3.2.2 Establishing and Maintaining an Electronic Network ............................... 38 3.3 Units of Observation ............................................................................................... 41 3.3.1 Firms in the Network ..................................................................................... 42 3.3.2 Key Constructs ............................................................................................... 46 3.4 Descriptive Statistics............................................................................................... 49 3.4.1 Descriptive Statistics of the Exchange Network .......................................... 49 3.4.2 Descriptive Statistics of the Supply Chain Echelons .................................. 50 3.5 Research Question .................................................................................................. 56 Chapter 4: Trading Partner Relationships................................................................... 58 4.1 Introduction............................................................................................................. 58 v
4.2 Development of Hypotheses................................................................................... 60 4.3 Research Methodology ........................................................................................... 66 4.3.1 Data ................................................................................................................. 66 4.3.2 Measures ......................................................................................................... 67 4.3.3 Modeling Event History ................................................................................ 72 4.3.4 Logistic Regression Modeling ....................................................................... 73 4.3.5 Cox Proportional Hazards Model ................................................................ 77 4.4 Results..................................................................................................................... 79 4.4.1 Descriptive Statistics ...................................................................................... 79 4.4.2 Logistic Regression Results ........................................................................... 83 4.4.3 Logistic Regression Results – Stratified Dataset ......................................... 85 4.4.4 Cox Proportional Hazards Model Results ................................................... 88 4.4.5 Cox Proportional Hazards Model Results – Stratified Dataset ................. 89 4.5 Discussion ............................................................................................................... 91 4.5.1 Evaluation of the Non-linear Specification .................................................. 94 4.5.2 Model Predictive Power ................................................................................ 98 4.5.3 Sensitivity Analysis ...................................................................................... 100 4.5.4 Potential Alternate Hazard Measure ......................................................... 100 4.6 Conclusion ............................................................................................................ 102 Chapter 5: Supply Chain Performance ...................................................................... 103 5.1 Introduction........................................................................................................... 103 5.2 Development of Hypotheses................................................................................. 104 5.3 Research Methodology ......................................................................................... 109 5.3.1 Data ............................................................................................................... 109 5.3.2 Measures ....................................................................................................... 111 5.3.3 Panel Data Analysis ..................................................................................... 119 5.3.4 Descriptive Statistics .................................................................................... 121 5.4 Results................................................................................................................... 126 5.5 Discussion ............................................................................................................. 128 5.5.1 Alternative Explanatory Variables ............................................................ 135 5.5.2 Alternative Lagged Explanatory Variables ............................................... 137 5.5.3 Alternative Dependent Variables ............................................................... 139 5.5.4 Additional Future Research Notes ............................................................. 140 5.6 Conclusion ............................................................................................................ 142 Chapter 6: Conclusion.................................................................................................. 143 6.1 Contribution .......................................................................................................... 143 6.2 Managerial Implications ....................................................................................... 144 6.3 Limitations and Future Research .......................................................................... 146 Appendix........................................................................................................................ 148 APPENDIX A Sample ANSI X12 and EDIFACT Message Types ........................... 149 APPENDIX B Transactional Information Types ....................................................... 150 APPENDIX C Enhanced Transaction Types.............................................................. 151 APPENDIX D Logistic Regression Results ............................................................... 153
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APPENDIX E Hazard Model Results......................................................................... 158 APPENDIX F Calculating Sales Surprise .................................................................. 163 APPENDIX G Alternative Dependent Variable Results ............................................ 168 References...................................................................................................................... 171
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List of Tables
Table 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 5.1 5.2 5.3 5.4 5.5 5.6 A.1 A.2 B.1 C.1 D.1 D.2 D.3 D.4 E.1 E.2 E.3 E.4 F.1 G.1 G.2 G.3 Description EDI Literature Review Sample of Trading Partner Network Data Network Descriptive Statistics Termination Rates by Firm Definitions of Logistic Regression Model Variables Definitions of Cox Proportional Hazard Model Variables Descriptive Statistics Pairwise Correlations Logistic Regression Results Logistic Regression Results: Stratified by Echelon Cox Proportional Hazards Model Results Cox Proportional Hazards Model Results: Stratified by Echelon Logistic Regression Results for the Linear Model Hazards Model Results for the Linear Model Logistic Regression of Interaction Model Classification Table for Estimation Sample Classification Table for Holdout Sample Results of Alternate Hazard Specification Descriptive Statistics of Unlogged Variables Pairwise Correlations Variance Inflation Factor Results Coefficient Estimates: Full Network Model Coefficient Estimates: Echelon Model Alternate Lagged Model ANSI X12 Message Types and Descriptions United Nations EDIFACT Message Types and Descriptions Transactional Information EDI Types Enhanced Information EDI Types Full Results of Logistic Termination Study Stratified Logistic Results: Manufacturers Stratified Logistic Results: Wholesalers Stratified Logistic Results: Retailers Full Hazard Model Results Stratified Hazard Model Results: Manufacturers Stratified Hazard Model Results: Wholesalers Stratified Hazard Model Results: Retailers Forecast Accuracy by Forecast Method Alternative Dependent Variable: Net Income Alternative Dependent Variable: Receivables Turnover Correlation Matrix for Alternate Dependent Variables Page 34 45 50 55 75 78 80 82 84 86 88 90 95 96 97 99 99 101 121 125 126 128 130 138 149 149 150 152 154 155 156 157 159 160 161 162 167 169 170 170
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List of Figures
Figure 2.1 2.2 3.1 3.2 4.1 Description Information Technology and the TCT Framework Information Exchange Matrix Information Exchange Directionality Trading Partner Relationships by Information Exchange Quadrant Information Exchange Hypotheses Page 10 29 47 52 65
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Chapter 1: Introduction
1.1 Information Exchange in the Supply Chain The cost-effective, timely, and accurate exchange of information allows today’s supply chains to efficiently deliver innovative solutions. Using the latest technology and supply chain strategies, a consumer in the U.S. can order a laptop computer online direct from the manufacturer. The manufacturer is able to custom build the laptop in Malaysia, coordinate the manufacture of the docking station and monitor stand in China, and have the entire order delivered within six days. In order to efficiently deliver this level of service to customers, multiple manufacturers and logistics providers are coordinated into a single virtual business entity. A key component of any business’s success is its ability to balance the costs and benefits of maintaining relationships with their trading partners. Exchanging information with trading partners is a central element in maintaining relationships. The cost of exchanging information includes the technology infrastructure as well as the gathering and formatting of the data. Sharing the wrong information with the wrong trading chain partner may cost the firm its competitive advantage and allow opportunistic actions by customers, suppliers, and competitors. Sharing the right information at the right time with the right trading chain partners can reduce costs and enhance competitive advantage. Not all supply chain relationships are equal and each requires unique amounts and types of information. Some business-to-business (B2B) exchanges are defined by closely integrated seamless relationships while others are defined by arms-length market relationships. Simple arms-length market relationships may require only the most basic
1
exchange of cost and quantity information. These loose relationships exist when the buyer requests a specific catalog item, quantity, and delivery date. The supplier responds with the cost, item availability, and payment terms. The exchange is coordinated based on a minimal exchange of information to support the order cycle process. Other more tightly integrated relationships may require ongoing real-time exchanges of production plans, point-of-sale demand data, inventory quantities, and shipping schedules. The exchange of additional information allows for the coordination of production and logistic functions above and beyond the order cycle process. Information exchange that is tailored to meet the needs of the relationship benefits performance by getting the right information to the right trading partner at the right time in order to support interfirm decision making. Supply chain relationships differ in their characteristics as well as their outcomes. The degree of trust, level of commitment, use of shared knowledge, access to systems and information, and use of shared goals vary among B2B supply chain relationships. Most firms do not build close supply chain relationships just for the sake of integration; they develop close relationships for the positive outcomes that are created. For example, when interviewing the regional sales manager for a large cable manufacturer, he cited specific outcomes from building close relationships with customers. Both the firm and its customers experienced benefits from the relationship. Its customers experienced positive supply chain performance outcomes, including reductions in average inventory, increased inventory availability, access to R&D resources, greater flexibility to adjust order quantities inside manufacturing lead time, and access to enhanced emergency response services. The cable manufacturer benefited from increased flexibility in managing its
2
order backlog, improved accuracy of forward production planning, better cost control through level production loading, and the avoidance of switching costs. This manufacturer noted that these benefits become possible when information flows seamlessly between organizations within the context of close supply chain relationships. Firms can identify the unique types and volumes of information exchange necessary to build and maintain each relationship. At a basic relational level, procurement and sales personnel from two distinct firms coordinate quantity, price, payment terms, and delivery information to support the exchange of products. At the other end of the relational spectrum, marketing and engineering personnel in distinct firms may exchange complex information to coordinate joint R&D efforts for the development of next generation products and services. In both situations, firms make decisions about the types of information that are exchanged in order to efficiently and effectively coordinate their resources. Direct modes of information exchange such as face-to-face, teleconferencing, and telephone contacts allow for immediate feedback among the parties. Due to the interactive nature of these modes, the exchange parties need to be available at the same time in order to coordinate the information exchange. Scheduling constraints in supply chains that extend across time zones serve to further decrease flexibility and increase the cost of using these traditional modes of information exchange. When immediate feedback and interaction are not required, information can be exchanged through email, electronic data interchange (EDI), voice mail, or web-based applications. These indirect information exchange modes support the efficient transfer of large amounts of information without both exchange parties being available at the same time. Email text 3
and attachments are sent by one firm and stored by the recipient firm until the information is needed. Although lacking in immediate feedback mechanisms, these technology-based information exchange modes have the advantage of conveying large amounts of information in a form that can be accurately stored and shared with others (Subramani 2004; Vickery et al. 2004). Firms may choose the mode of information exchange that best suits their situation. 1.2 Strategic Use of Supply Chain Relationships Increased competition makes the management of supply chain relationships a strategic issue for firms. Researchers note that supply chains exist whether they are managed or not (Mentzer et al. 2001). However, the successful management of supply chains can be a source of competitive advantage for firms (Dyer and Ouchi 1993; Houlihan 1985). Supply chain management (SCM) is described as the strategic management of individual firms as a single entity in order to bring a product or service to the market (Vickery et al. 2003). The American Production and Inventory Control Society (APICS) provides a process oriented definition of SCM: The design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally. (APICS, 2006) Collectively, these two definitions of SCM create a holistic view of SCM recognizing that the individual firms in a supply chain are distinct entities that share a common interest in operating as a single vertically integrated system. The linking of internal firm processes to external customers and suppliers is recognized as a key element in the management of supply chains. Through an empirical 4
test of five integration strategies, researchers found a correlation between firms with higher performance and their increased use of integration (Frohlich and Westbrook 2001). Their survey-based study asked firms to rate the degree of integration with both their customers and suppliers. The highest performing strategy was found to be linking closely with both customers and suppliers. In their study, information exchange was a key indicator of integrated relationships. Integrated relationships are characterized by the sharing of production plans and the use of shared information systems. Information technology (IT) facilitates the exchange of information in the supply chain. Inherent in the traditional model of a supply chain are the flow of products toward the end customer and the flow of information toward the raw materials suppliers. In its most basic form, information only flows upstream from the end customer toward the raw materials supplier. This type of information flow often consists of only basic order information that informs the supplier to ship a specific item to the requesting customer. Today’s advanced IT capabilities support the fast and efficient flow of large volumes of diverse information both upstream and downstream in the supply chain. To enhance the coordination of the supply chain among customers and suppliers, firms often share inventory information, quality reports, demand forecasts, production schedules, and marketing research with both customers and suppliers. These bi-directional flows of information go against the traditional model of the supply chain information flow but are recognized in both academic literature and business practice (Spekman et al. 1998). Technological advances enhance the ability of firms to gather, store, and transfer information. The ability of a firm to use IT is recognized as a source of firm performance
5
advantage (Bharadwaj 2000; Santhanam and Hartono 2003; Zhu and Kraemer 2002). Beyond the mere presence of IT, researchers find that technology facilitates the exchange of information between trading partners (Spekman et al. 1998). Recognizing the role of IT in facilitating supply chain integration, IT has been modeled as an antecedent of supply chain integration (Vickery et al. 2003). IT connections and integration strategies create linkages among firms in the supply chain, but linkages alone do not integrate firms. Moving information seamlessly between a company and its trading partners facilitates the strategic integration that coordinates firms in the supply chain (Mukhopadhyay et al. 1995). Specifically, information is most useful in a supply chain context when it is timely, accurate, and relevant to decision making (Bakos and Brynjolfsson 1993; Whipple et al. 2002). The exchange of information across these interfirm linkages integrates the firms and allows them to perform as a single virtual organization. 1.3 Contribution of the Dissertation This dissertation addresses existing gaps in the literature by examining the effect of information exchange on supply chain performance. Specifically, this dissertation examines IT enabled information exchange between firms in the supply chain. This dissertation makes four unique and significant contributions related to the use of information exchange in supply chains:
?
This dissertation extends the theoretical link between information exchange and supply chain participant performance. Prior research recognizes the role of information exchange in supply chains but has been 6
limited in its measurement. These analyses use a unique archival dataset from an electronically-mediated industrial exchange network to develop measures of actual information exchanges in B2B relationships.
?
This dissertation expands the growing body of literature on supply chain management by maintaining a perspective that performance is associated with the effective management of interfirm relationships. The exchange of information with trading partners is specifically advanced as a key element of interfirm relationships that has not been fully developed in the literature.
?
This dissertation identifies dimensions of information exchange that contribute to the relational performance of firms in the supply chain through the use of dyadic observations of B2B interactions. Using event history analysis tools and performance measures from strategic management literature, the association between information exchange characteristics and relationship termination is developed. U-shaped relationships are identified where the effects of information exchange characteristics are negative at lower volume levels and positive at higher volumes.
?
This dissertation identifies dimensions of information exchange that contribute to the operational performance of firms in the supply chain. A panel dataset of thirty-nine technology champion firms is used for
7
hypotheses testing. Findings suggest that close trading partner relationships may be detrimental to performance. The remainder of this dissertation is structured as follows: Chapter 2 discusses and applies relevant theory, Chapter 3 presents the methodology and data, Chapter 4 identifies and tests hypotheses related to relationship termination, Chapter 5 identifies and tests hypotheses related to firm operational performance, and Chapter 6 offers conclusions and directions for future research.
8
Chapter 2: Theory and Review of Extant Literature
2.1 Introduction This chapter uses transaction cost theory (TCT) to build a theoretical approach to understanding the effects of information exchange within electronically-mediated supply chain relationships. This unique investigation into the role of information exchange in supply chain relationships has both academic and practical application. TCT is an appropriate theoretical lens for supply chain research because it can be used to evaluate the effectiveness of buyer-supplier relationships (Grover and Malhotra 2003). TCT identifies specific characteristics of interfirm exchanges and assumptions about firm behavior to guide a firm’s choice of relationship governance structure. Simply put, TCT recognizes that there are attributes of business exchanges which may require firms to manage the relationship more carefully in order to avoid unfavorable outcomes. The chapter proceeds with an overview of the key elements of TCT that apply to this dissertation and an introduction of extant literature to form a framework for research into the use of information exchange in supply chain relationships. 2.2 Transaction Cost Theory in a Supply Chain Context TCT proposes that firms decide strategically whether to make their inputs or purchase their inputs from the market (Williamson 1975; Williamson 1985). Transaction costs are incurred when firms procure inputs from outside sources. These transaction costs include searching for a source of supply, negotiating, coordinating the exchange, and monitoring the transaction.
9
The decision of whether to make or buy inputs was initially described as a dichotomous choice of either market or hierarchy (Williamson 1975). This dichotomy was later expanded as a continuum of governance structures, recognizing that market characteristics and hierarchy characteristics could be blended in forming a continuum of business relationships (Webster 1992). The space between the dichotomous choices of market and hierarchy was described as “hybrid” by Williamson (1985) and is depicted as supply chain relationships in Figure 2.1. The hybrid space recognizes that firms can organize their external transactions using non-price mechanisms which create unique governance structures ranging from closely connected relationships to arms-length market transactions (Williamson 1985). Figure 2.1 Information Technology and the TCT Framework
Market
Supply Chain Relationships
Hierarchy
Electronic Markets Hypothesis
Vendors to Partners
Move to the Middle
The continuum between pure markets and internal hierarchies has been used to describe the nature of supply chain relationships. At the far left extreme of the supply
10
chain relationship continuum, the relationship is typically an arms-length or “marketlike” relationship between a supplier and its customer. These exchanges differ from a pure market exchange because the exchange is not discrete but occurs repeatedly between the same two participants such that a relationship exists through an expectation of future exchanges. This type of relationship may exist when commodities are repeatedly exchanged and the relationship is primarily governed by price mechanisms. At the other extreme -- corresponding to the right-hand “hierarchy” end of the continuum -- are close relationships where suppliers tightly integrate with their customers. The earlier example of a cable manufacturer that works closely with select customers to coordinate production and demand would be an example of a relationship on the hierarchy end of the supply chain relationship continuum. At the far right of the continuum are the processes that have been moved into the firm’s hierarchy and are no longer external supply chain relationships but instead are vertically integrated internal processes. Within the continuum in the middle are supply chain relationships characterized by varying levels of integration. TCT posits that the decision of how to manage relationships is determined by factors of the exchange transaction which include three dimensions and two assumptions. The three dimensions of exchange identified by TCT are: the frequency of exchange, the amount of uncertainty, and the degree of asset specificity (Waldman and Jensen 1998). The two assumptions are bounded rationality and opportunism. The frequency of exchange recognizes that how often firms transact to acquire a given input affects the overall cost of transacting. If an input is only required occasionally, the firm may choose to use the market. If, however, the input is needed 11
often, the cost of repeatedly renegotiating procurement from the market will increase aggregate transaction costs. While discrete purchases may be transacted through the market, when multiple transactions are required to supply an input, firms will tend to move toward internalizing the transaction to reduce their overall cost (Williamson 1985). In practice, firms move away from discrete pure market transactions and create blanket purchase orders that govern the exchange for a specific period of time. Uncertainty in exchange relationships is often a result of coordination problems between exchange partners. When one party is unsure of the plans and intentions of its exchange partner, slow or inappropriate decisions can increase costs. In practice, this can occur when items that are ordered are not delivered when expected. The customer is unsure why the ordered items were not delivered and may subsequently decide to order the items from another source rather than waiting for the initial order to be delivered. Asset specificity addresses the ease with which firms can reallocate assets to other relationships. If assets are dedicated to supporting a specific relationship and cannot be reallocated to a new relationship, these transaction-specific investments can be the source of lock-in or hold-up (Shapiro and Varian 1998). When a specialized asset is allocated in a buyer-seller relationship, the party holding the specialized asset has few options to protect the loss of that asset beyond maintaining the existing relationship. Difficult and lengthy contract negotiations, cost concessions, and ongoing monitoring requirements needed to protect the specialized asset create additional transaction costs. In practice, suppliers may co-locate their facilities inside of the customer’s warehouse in order to provide unique services. In such a case, the supplier incurs costs to set-up the operation which may not be transferred to another relationship if the customer decides to change 12
suppliers. An additional aspect of asset specificity occurs when a supplier develops a unique input for a customer specified need. If the customer decides that the input is no longer needed, then the supplier may have no use for the production capability, existing inventory, or unallocated R&D expenditures. TCT posits that underlying these three dimensions are two assumptions. TCT assumes that transacting parties are limited in their ability to collect and understand all issues related to the exchange (bounded rationality) and that if given the opportunity firms will behave selfishly in ways that are detrimental to the other party (opportunism). Firms incur costs to overcome bounded rationality and avoid opportunistic behavior. The writing of extensive contracts and carefully monitoring the exchange are two ways that firms protect themselves. However, as any of the three dimensions of exchange (frequency, uncertainty, and asset specificity) increase, there is a greater opportunity for firms to act opportunistically thus requiring additional transaction costs in order to reduce the risk. Increased transaction costs require a firm to choose a governance structure closer to a hierarchy structure in order to minimize the risks of the outside firm not performing as expected. When transaction costs become higher than the cost of internally producing the good, firms will seek the most efficient governance mechanism available, which may include internalizing the transaction and not exchanging with outside firms (Williamson 1975). Opportunism is a unique phenomenon in business since it affects interfirm transactions by both its occurrence and its risk of occurrence. Opportunistic behavior occurs through misleading reports, misrepresented work time, quality shirking, and
13
similar actions reported regularly in the newspaper. These specific actions create costs that affect firm financial and operational performance. But opportunistic behaviors do not need to occur in order to create costs for firms. The mere risk of opportunism can cause firms to incur prevention costs (Wathne and Heide 2000). Even when the risk of opportunism is high, firms may choose to not act opportunistically. Literature has proposed that long-term orientation (Ganesan 1994), relational contracts (Ring and Ven de Ven 1992), pledges and idiosyncratic investments (Anderson and Weitz 1992), and explicit written contracts (Ring and Ven de Ven 1992) can discourage opportunism. But whether opportunism occurs or only the potential exists, costs are incurred. The availability of information affects these transaction costs within the TCT framework. Price alone effectively and efficiently coordinates pure markets. In an environment of perfect information, the price mechanism coordinates exchange with minimal transaction cost. Unfortunately, buyers and sellers do not possess perfect information and the resulting information asymmetry increases the cost of transacting through the market. On the surface, one might predict that these increased costs would prompt firms to avoid transacting with the market. In the long run, firms could develop internal capabilities to provide many of the inputs required for production. But in reality, firms always rely on some level of outside input either because of technical expertise, availability of resources, or economies of scale (Pfeffer and Salancik 1978). So in the short run, and often in the long run, firms rely on outside sources for inputs. If firms can decrease the comparative transaction costs of using the market, it is economically justifiable to continue using market sources for inputs. 14
2.2.1 The Effect of Information Technology on the TCT Framework The application of information technology (IT) in supply chain relationships has changed the balance between markets and hierarchies by decreasing the cost of transacting outside the firm’s boundaries (Clemons and Row 1992; Malone et al. 1987). The reduction of external coordination costs has two distinct outcomes. First, in situations where transaction costs would have forced firms to internalize exchange, the use of IT decreases the cost of exchange and allows the transaction to occur with external suppliers. Research into the use of IT in the supply chain has shown that firms are using IT to reduce their internalized transactions and form more relationships with external firms (Brynjolfsson et al. 1994; Hitt 1999). Improvements in IT have greatly reduced external coordination costs, thereby decreasing transaction costs. As shown in Figure 2.1, the incorporation of IT into the markets and hierarchies discussion has added richness to the TCT framework by helping predict movement along the continuum. The electronic markets hypothesis (EMH) recognizes that the use of IT to facilitate interfirm information exchange lowers the costs of transacting outside of the boundaries of the firm. Using IT is expected to decrease the cost of search, document processing, and monitoring. Lower transaction costs are hypothesized to encourage firms to interact with the market rather than internalize transactions (Malone et al. 1987). The EMH was followed by the vendors-to-partners thesis which recognizes that IT can be used to form close interfirm relationships where hierarchy-type benefits could be achieved in external (supply chain) relationships. Firms are predicted to move away from discrete market transactions to form closer relationships
15
which enhance quality, responsiveness, and innovation (Bakos and Brynjolfsson 1993; Saeed et al. 2005; Subramani 2004). These two perspectives are important because although they both support that firms will increase their use of outside suppliers, they differ in how firms will use outside suppliers. EMH predicts an increased use of the market. Conversely, the vendors-topartners thesis predicts the use of close relationships that are similar to a vertically integrated hierarchy but still outside of the firm boundaries. Balancing these perspectives, the “move to the middle” was proposed (Clemons et al. 1993; Gurbaxani and Whang 1991). The “move to the middle” posits that the use of IT will create a balance between market and hierarchy benefits in supply chain relationships. This balanced perspective is important since it recognizes the trade-offs that must be managed when integrating IT in supply chain relationships. How IT changes the way interfirm business is conducted depends on the resources and needs of the participating firms. Some will take advantage of more accessible markets (EMH) while other firms will use IT to seek improved performance through collaboration (vendors to partners). The move to the middle is an important realization that firms will use a mix of IT strategies to manage their portfolio of trading partner relationships. While the literature is unclear where relationships will fall along the continuum, the use of IT to facilitate low cost interfirm transactions is consistent. While the use of IT can be seen as a benefit to enable firms to efficiently interact with external trading partners, the literature is inconsistent on the appropriate closeness of supply chain relationships and how much information should be shared. So many studies
16
have shown the benefits of close trading partner relationships that one might assume that closer is better. However, additional research has suggested that the trading partner pool be treated as a portfolio. Through a portfolio perspective, close relationship are developed with specific trading partners while other trading partners are kept at armslength (Lambert et al. 1996a; Lambert et al. 1996b; McCutcheon and Stuart 2000). 2.2.2 The Effect of Information Exchange on the TCT Framework Information exchange is central to the coordination of firms in a supply chain context and is instrumental in both markets and hierarchies under the TCT framework. From a markets perspective, the exchange of information is central to the efficient operation of the price mechanism. Neoclassical economics assumes that full information is available to all market participants at no cost. This full availability of information to all exchange participants in the market allows the price mechanism to efficiently clear the market. Full information is rarely available to all parties in the exchange, but the parties can choose to exchange information. When information is exchanged within supply chain relationships, firms can decrease the uncertainty and lower the cost of monitoring the relationship. TCT recognizes that information is not equally and fully available to all exchange participants. TCT acknowledges information asymmetry and presupposes that information is available to the exchange parties – for a price. The cost of searching, gathering, and using information increases the cost of coordinating transactions with the market (Williamson 1975; Williamson 1985).
17
Within the TCT framework, information exchange also can decrease transaction costs. Information decreases transaction costs through its effect on uncertainty, coordination, and monitoring. First, the availability of information decreases uncertainty by providing additional knowledge to trading partners. The relationship becomes less uncertain when demand information, firm goals and intentions, and production plans are known and shared between exchange partners. When information is shared in advance, there is less uncertainty about the intentions of the trading partner if problems do occur (Rozenzweig et al. 2003). Second, information is used to efficiently coordinate transactions. When suppliers know demand forecasts, marketing plans, and point-of-sale information, order quantities are expected and properly planned to minimize both overstocking and stockouts (Metters 1997). Information technology has been used extensively to facilitate the low-cost exchange of information to support the order processing cycle. Finally, information is used to facilitate monitoring mechanisms to protect against opportunistic behavior when specialized assets are at risk. When production schedules, plant capacities, inventory positions, and shipment schedules are known across the supply chain, firms have greater visibility into the operations of their trading partners (Rozenzweig et al. 2003). The regular exchange of this information allows for the monitoring of key interfirm processes critical to governance mechanisms under TCT. The downside of close trading partner relationships includes the cost of development and the risk of opportunism. Since the cost of developing a relationship cannot be recovered or applied to a new relationship, these costs represent a specific asset that is lost if the relationship fails to develop. The costs of developing close trading
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partner relationships depend on many factors including the level of integration and governance structure (Lambert et al. 1996a). Complex interactions and specific capital investments may require extensive formal agreements to coordinate their resources. Such investments in time and capital are then protected under the terms of the contract. If the anticipated benefits do not materialize, then firms have incurred unnecessary costs. Similarly, the transparency that develops when information is shared between firms may allow trading partners to act opportunistically by withholding some benefits of the relationship (Shapiro and Varian 1998). In summary, this literature stream suggests that rather than pursuing close relationships with trading partners, it is more effective to develop close relationships with the right trading partners (Lambert et al. 1996a; Lambert et al. 2004). Research has linked the exchange of information between firms to the strategic issue of supply chain integration. Using survey data from a sample of global manufacturers, Frohlich and Westbrook (2001) tested the relationship between supply chain integration strategy and performance. The study included both the direction of integration (customer facing or supplier facing) as well as the degree of integration (no integration to extensive integration). The study found that the greatest performance improvements were made by firms with the highest levels of integration with customers and suppliers. Information is central to the integration of firms in the supply chain. Frohlich and Westbrook (2001) used four information exchange measurements to capture the integrative activities of firms. The integrative activities used by firms to link with their external supply chain participants are each based on information exchange. This
19
research clarifies the bi-directional role of strategic information exchange in supply chain relationships. Marketing channels literature also supports the vital role of information exchange in supply chain relationships. Using organization theory and communications theory, researchers posit that the communications strategy is a moderator between channel conditions and channel outcomes (Mohr and Nevin 1990). This vital role of communications between supply chain participants is empirically tested and validated through the operationalization of communication as frequency, direction, modality, and content (Rinehart et al. 2004; Vickery et al. 2004). 2.3 Review of Empirical Literature 2.3.1 Use of Information in Supply Chains Inventory can be a significant cost driver in supply chains. Having too much inventory causes firms to accrue unnecessary carrying costs. Having too little inventory causes firms to accrue unnecessary stockout costs. Researchers suggest that information can be a substitute for inventory (Daugherty and Pittman 1995). In practice, when forecasts, inventory positions, and actual demand are shared with upstream suppliers inventory needs can be planned such that goods are produced when needed rather than held in inventory in case they are needed. Specific research into demand distortion has shown that information can reduce the buildup of unneeded inventory. Demand distortion, also known as the bullwhip effect, is a phenomenon whereby upstream supply chain participants experience volatility
20
in demand greater than the actual consumer demand volatility (Lee et al. 1997; Metters 1997). When the bullwhip effect occurs, small fluctuations in consumer demand create larger fluctuations in replenishment orders upstream in the supply chain. These distortions of demand cause upstream participants to build-up inventory far beyond what is required to satisfy consumer demand. Research into the causes of the bullwhip effect suggests that exchanging additional information including inventory status, order coordination, and point-of-sale data could reduce the bullwhip effect (Chen et al. 2000; Lee et al. 1997). Researchers using the beer game simulation, have found that information sharing does dampens the bullwhip effect (Croson and Donohue 2006). In their study, the sharing of information allowed upstream suppliers to anticipate downstream inventory needs. In a similar beer game simulation, it was found that the sharing of point-of-sale (POS) information upstream in the supply chain dampened the bullwhip effect but the results varied depending on the nature of the demand pattern (Steckel et al. 2004). They found that when demand is volatile (S-shaped demand pattern) then the sharing of POS information becomes a distraction to the more immediate issue of responding to orders. The exchange of information between supply chain participants has benefits beyond dampening the bullwhip effect. Through the modeling of a two-tier supply chain with one supplier and multiple retailers, it has been shown that sharing demand information can reduce inventory across the supply chain by as much as 12% (Cachon and Fisher 2000). Sharing demand information is shown to improve manufacturer forecasting accuracy and decrease safety stock (Raghunathan and Yeh 2001). This
21
research examines the role of information exchange in improving supply chain performance. 2.3.2 Information Exchange to Support Specific Supply Chain Initiatives The exchange of information in the supply chain is found to support strategic initiatives. These initiatives are thought to be strategic in nature because they allow firms to create unique competitive advantages by implementing interfirm processes. Vendor managed inventory (VMI) is an interfirm process whereby the supplier is authorized to manage inventories of his downstream customers. Since the VMI supplier has the freedom to make re-supply decisions for the retailers, inventories and transportation can be synchronized for both echelons (Cetinkaya and Lee 2000). Similarly, the continuous replenishment process (CRP) allows the upstream supplier to control restocking at retail locations. Studying a single manufacturer engaged in CRP with its retailers, it was found that stockouts were reduced and inventory turnover was increased (Lee et al. 1999). Programs like CRP allow inventory to be coordinated across multiple echelons of the supply chain. Variability in demand is absorbed upstream by keeping safety stock to satisfy demand when forecasts understate the actual demand. Researchers have shown that the sharing of demand information improves the upstream manufacturer’s forecast accuracy and allows for decreases in safety stock (Raghunathan and Yeh 2001). The use of just-in-time (JIT) processes has shown benefits for supply chain participants by allowing them to minimize the buildup of inventory throughout the supply chain. Using a survey methodology, researchers found that use of JIT is directly related to savings in logistics costs for buyers (Dong et al. 2001). Similarly combining the use of
22
EDI with a JIT environment has been shown to reduce shipment errors (Srinivasan et al. 1994). Each of these interfirm processes are designed to improve supply chain performance by coordinating resources across the supply chain. Successful coordination is dependent on the exchange of accurate and timely information (Angulo et al. 2004). 2.3.3 Using Interorganizational Systems to Exchange Information Interorganizational systems (IOS) have been identified for their boundary spanning role in integrating firms in the supply chain (Zaheer and Venkatraman 1994). These systems take many forms and use various technologies including: EDI, email, electronic exchanges, Web-based applications, and extensible mark-up language (XML). EDI is often the focus of empirical research since it represents a stable technology that is well established in industry. Empirical literature has addressed EDI as both an application of a specific IOS technology and more generally as an enabler of information exchange. Early EDI research focused on the adoption of the technology (Crum et al. 1998; Crum et al. 1996; Johnson et al. 1992). Although trade publications predicted that EDI technology would be supplanted by the newer technologies, EDI continues to be a primary method of exchanging information in industrial supply chains. Firms already possess the necessary knowledge and infrastructure to exchange information using EDI and continue to leverage the resource. Trade journals report the continued and growing use of EDI (Brockmann 2003; Sliwa 2004). More recent research has focused on the use of EDI to exchange information that supports collaborative supply chain initiatives, and may include: VMI, JIT, and CRP.
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This literature is critical for establishing the key mediating role of IT and information exchange in supply chains. IT is the enabler of information exchange and forms an efficient conduit that spans the boundaries of individual firms in the supply chain. The information that is exchanged through the use of IT supports many of the innovative processes used to improve supply chain performance. 2.4 Research Model Development Theory and existing literature clearly recognize the critical role of information exchange in supply chain relationships. A gap in the literature is how information exchange is operationalized in empirical research. Information exchange has not been studied in a way that will enhance the understanding of how information exchange improves performance. Research has addressed information exchange from many perspectives. Information exchange is often modeled as a binary measure where firms either exchange information or do not. Some studies have recognized that information is not homogenous and have measured the existence of multiple types of information. Such studies include whether demand forecast information is exchanged or whether point-of-sale information is exchanged but no more specific measures to capture the frequency or extent of the exchange. Other research has begun to address the multi-dimensional features of information and proposed robust measures to capture some of the complexities of information exchange but has been limited to data collection from single firms. This dissertation
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combines and expands these approaches by identifying and testing objective measures of information exchange across multiple firms. Early studies of information exchange technology focused on the adoption of IOS to coordinate the supply chain. Appropriate questions at the time where centered around whether firms were exchanging information through the use of IOS and if performance was effected. As such, basic binary measures and measures of the percentage of relationships that used the technology were appropriate to understand the diffusion of technology (Allen et al. 1992; Crum et al. 1998; Crum et al. 1996; Srinivasan et al. 1994; Zaheer and Venkatraman 1994). As the use of information exchange through technology became evident, research expanded to identify characteristics of information exchange. In a study of the effects of information sharing strategies on supplier reliability, the type of information and direction of exchange were captured using binary measures (Walton and Marucheck 1997). This survey-based research measured information exchange as if the buyer shares forecasts, if the buyer shares planned production, if the buyer shares capacity information, and if the supplier shares planned production. The study concludes that relationships where buyers share forecast information experience lower supplier reliability than relationships where buyers share planned production information with suppliers. Sharing forecast information is less valuable than sharing production plans. These results support that what types of information are shared effects the performance outcomes. Additionally, these measures recognize that each firm can exchange information such that information flows in both directions across the supply chain.
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The complexity of information exchange has been captured in some EDI studies which allow for the development of an understanding of the heterogeneity of how firms use information exchange. Facets of EDI usage that help measure these differences are the volume of exchanges through IOS, the different types of information exchanged through IOS (diversity), the number of trading partners connected through the IOS (breadth), and the extent to which the interfirm processes are intertwined (depth) (Massetti and Zmud 1996). Although the focus of the study was EDI use, the measures captured how the firms were using a specific IOS to manage their portfolio of trading partner relationships. Segregating exchange into multiple components allows researchers to address how each facet contributes to firm performance and their interaction can be considered. This dissertation adopts the multi-faceted approach and applies it specifically to the measurement of information exchange in supply chain relationships. For the purposes of this study, information types are categorized as being either transactional or enhanced based on their content. This distinction between types of information that can be exchanged is modeled by Cachon and Fisher (2000). In their study, information exchange is categorized as traditional information sharing and full information sharing. Under their depiction of traditional information sharing, only order information was given to the supplier. Full information sharing is modeled as allowing the supplier visibility of the retailer’s inventory levels. Their use of traditional information sharing is consistent with the transactional information identified in this dissertation. This distinction has also been modeled where traditional information is compared with the sharing of additional downstream demand forecasts (Cachon and Lariviere 26
2001). The inclusion of additional types of non-order information gets to the issue of how diverse information is used in the supply chain. Transactional information is used to support the order cycle and was described as having an electronic data processing orientation by Porter and Millar (1985). This information includes electronically exchanged requisitions, purchase orders, purchase order confirmations, invoices, and remittance advice documents. Exchanging these documents electronically reduces the processing costs for firms by eliminating mailing costs, eliminating mailing delay, eliminating the need to enter the data manually at the receiving site, and reducing data entry errors. Information beyond order cycle information can be used to support the coordination of interfirm resources (Cachon and Fisher 2000; Cachon and Lariviere 2001). In a study of VMI processes, researchers recognized that information including forecasts, daily demand, inventory positions, and shipment information can be exchanged in order to improve supply chain performance (Angulo et al. 2004). In this dissertation, the additional information that can be used to support interfirm coordination is included as enhanced information. This depiction of enhanced information exchange is inclusive of the full information identified in both Cachon and Fisher (2000) and Cachon and Lariviere (2001). These two types of information exchange are modeled distinctly in research depending on the focus of the study. Exchanging enhanced information between a vendor and a retailer can include demand information, shipment information, inventory positions, and forecasts to support the decisions in a VMI process (Angulo et al. 2004).
27
The outcomes of these additional information flows include improved cash flow, shortened order cycle time, and increased firm competitiveness (Iacovou et al. 1995). Conversely, the exchange of transactional information is associated with improving the speed and accuracy of the order processing cycle. The electronic exchange of requisitions, purchase orders, and invoices lowers the cost of transacting but has minimal impact beyond the order cycle. Firms may choose to exchange any combination of the various types of information. Since research has identified that performance outcomes are affected differently based on the types of information exchanged, one could expect that various combinations of information types will similarly affect performance outcomes (Cachon and Fisher 2000). These two dimensions of information exchange characteristics become the foundation for improving the understanding of information exchange in supply chain relationships. To illustrate the theorized relationship between the exchange of transactional information and the exchange of enhanced information in trading partner relationships, Figure 2.2 provides a two-by-two matrix. The four quadrants identify four distinct combinations of exchange volume between transactional and enhanced information within trading partner relationships. The mean values of transactional information exchange volume and enhanced information exchange volume are identified for each technology champion firm. By comparing the exchange volumes for each trading partner relationship with the mean of the technology champion firm, relationships can be identified as operating either above or below the mean for each type. Trading partners that are above the firm mean for both transactional and enhanced information exchange 28
are recognized as having closer relationships relative to other trading partners exchanging with the technology champion firm. Figure 2.2 Information Exchange Matrix
Transactional High Information Exchange Volume Low
I. Transactional Relationships
II. Close Relationships
III. Arms-length Relationships
IV. Enhanced Relationships
Low
High
Enhanced Information Exchange Volume
The top-left quadrant, I Transactional Relationships, depicts a condition where the frequency of transactional information exchange is high and the frequency of enhanced information is low. In practice, this situation may occur when a commodity item is exchanged. Although the item may be needed often or it is requested by multiple business units within the organization, the information exchanged is oriented towards the order cycle and little if any additional information is exchanged. Such may be the case in a supply chain relationship for providing office supplies. The buying firm could create a contract that decentralizes the ordering process which allows each operating area to place orders with the office products supplier on an as-needed basis. This would potentially create a situation where order cycle information is exchanged at a high frequency. Once
29
the items for the contract are identified and pricing / service expectations are agreed upon, minimal additional information would be required to support the relationship. Enhanced information such as production schedules, demand forecasts, and logistics documents are rarely shared in these commodity relationships because the cost of exchanging them is often greater than the value they add in managing basic commodity products. The supplier simply fills the order and delivers it based on a pre-determined service-level agreement. The top-right quadrant, II Close Relationships, depicts a condition where both transactional and enhanced information are exchanged at a high volume. A high volume of transactional information is indicative of many interfirm orders being placed and filled. Trading partner relationships that exist in this quadrant may exchange commodity item or production materials that are supplied on a frequent basis. Materials supplied through a just-in-time process could represent items that are ordered on an hourly or daily basis for sequencing into the production cycle. Due to the critical nature of the items, additional enhanced information could be exchanged to synchronize production further down the supply chain. The exchange of additional information including demand forecasts, actual customer demand, production sequences, and inventory balances can enhance the planning process across multiple echelons of the supply chain. The bottom-left quadrant, III Arms-length Relationships, depicts a condition where both transactional and enhanced information are exchanged at a low volume. These trading partner relationships are expected to represent non-critical items that are ordered on an infrequent basis. The items may be low in cost or low in usage which in either case would support infrequent ordering. Low cost items have a minimal effect on 30
carrying costs relative to their ordering costs as modeled in the economic order quantity (EOQ) calculation and are often ordered in larger quantities less often. Similarly, low demand items would be ordered infrequently even in a just-in-time environment. Commodity or low-criticality items would be less likely to benefit from the exchange of enhanced information. In practice, these items might include maintenance, repair, and operations (MRO) supplies which are used to support the functions of the firm. The bottom-right quadrant, IV Enhanced Relationships, depicts a condition where transactional information is exchanged at a low volume but enhanced information is exchanged at a high volume. The high volume of exchange for enhanced information would suggest that the items supplied through these trading partner relationships are critical in nature either due to their use in the process or their cost. The exchanging of enhanced information would support enhanced planning and synchronizing across interfirm processes. The combination of high enhanced volume with low transactional volume would suggest that the items are either ordered in bulk or not needed very often. In practice, these items may be direct materials that are used in production of the firm’s product. Large quantities of critical inputs that are ordered infrequently may include raw materials that are ordered by rail car such as flour and sugar. Small quantities of critical inputs may include custom hydraulic equipment for large construction equipment. In either case, large volumes of planning and sequencing information may be exchanged to ensure that the input is available when needed, but minimal transactional information is exchanged due to the infrequent need to procure the items. This matrix provides a grid though which information exchange can be observed to better understand how firms use information exchange strategically in their supply 31
chain relationships. Based on the TCT theoretical lens and the extant literature on the use of IT-enabled information exchange, this dissertation will address the research question of how information exchange is associated with supply chain performance. Chapter three describes the research setting and data that will be used to test hypotheses through studies developed in Chapters four and five.
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Chapter 3: Research Setting and Data
This dissertation uses a unique dataset extracted from an established, electronically-mediated industrial exchange network. Where most previous studies have been limited to survey-based perceived measures of information exchange, simulation of information exchange, or analytical modeling of supply chain interaction, this study uses objective measures of actual exchanges of information between industrial supply chain participants. This proprietary database has been made available by one of the largest EDI network providers in the industry. The data include summary volumes at a network, technology champion firm, trading partner, and dyadic level on a monthly basis for the years 2004 and 2005. Additional secondary data on firm performance and company specific data has been provided from Standard and Poor’s Compustat database. Interactions with this EDI network provider helped provided qualitative and quantitative information pertaining to this research. The qualitative information is used to help describe the context of the investigation of the research questions. Conversations with employees from the EDI network provider helped give great insight into the nature of the relationships between the technology champion firms and their trading partners. Although none of the qualitative information was used explicitly, it was used implicitly to help structure the research. 3.1 Use of Electronic Data Interchange Data for Empirical Research For the purposes of this study, EDI is defined as a specific type of IOS which 1) exists between at least two organizations, 2) transfers data between independent
33
application systems at each firm, 3) uses standardized data formats, and 4) transfers the data using telecommunication links (Iacovou et al. 1995; Pfeiffer 1992). EDI under this definition is a computer-based method of formatting and exchanging information that is relatively fast, accurate, and low-cost. The benefits of technology adoption related to EDI implementation have been addressed in logistics and EDI literature. A survey of warehousing firms found that firms using EDI had a strategic impact by providing a greater (average) number of services to customers and by more easily accommodating special customer requests (Rogers et al. 1992). The use of EDI in an automotive setting resulted in a cost savings of between sixty and one hundred dollars per vehicle (Mukhopadhyay et al. 1995). While early EDI studies focus on the growth and adoption of the technology, more recent research accepts the widespread use of EDI and instead focuses on the best use of the technology. For example, Subramani (2004) looks at how firms can extend EDI relationships to develop domain-specific knowledge and business-specific processes that create negative externalities for their competitors. Researchers in IT, operations management, and logistics have viewed EDI as a specific application of IOS. This perspective contributes to the understanding of the use of IT as a tool for the exchange of interfirm information. Key EDI-based literature, methodology, and findings are summarized in Table 3.1. Table 3.1 EDI Literature Review Author (s) Methodology (Subramani 2004) Empirical – Case study •
Major Findings Suppliers benefit from EDI implementation when they use the integration to build business process specificity or domain knowledge specificity
34
(Machuca and Barajas 2004) (Hill and Scudder 2002)
Empirical – Simulation Empirical – Survey
• • •
(Mukhopadhyay and Kekre 2002)
Empirical – Archival data
• • •
(Angeles and Nath 2001) (Raghunathan and Yeh 2001)
Empirical – Survey Empirical – Modeling
• • • • • •
(Droge and Germain 2000)
Empirical – Survey
(Cachon and Fisher 2000) (Lee et al. 1999)
Empirical – Modeling Empirical – Case study Empirical – Survey
• • • • • • • • • 35
(Crum et al. 1998)
(Williams et al. 1998)
Empirical – Survey
(Walton and
Empirical –
EDI use contributes to cost savings and SCM improvements (reduces bullwhip) EDI seen as a source of efficiency rather than integration Firms are more accommodating to customer EDI than supplier EDI Use of integrated EDI results in more ontime payments and less credit orders Customer driven EDI implementation results in greater benefits for the supplier Supplier process specificity results in greater benefits for the supplier Compatibility of trading partners is important for successful EDI implementation Sharing of demand information improves the manufacturer’s forecast accuracy Information sharing decreases safety stock Continuous replenishment programs benefit retailer and manufacturer by moving inventory faster The use of EDI and firm financial performance are positively related Increased inventory is positively related to EDI, stable demand, small firm size, routine production technology and JIT usage Use of IT to accelerate the flow of products is more valuable than using IT to expand the flow of information Retailers who are forced to adopt systems can benefit if they reengineer their internal processes Use of EDI is increasing Customer service and marketing are implementation drivers Use of transaction sets is concentrated Stakeholders generally satisfied with EDI EDI can be measured as a multidimensional technology Longer use of EDI and increased investment increase the width, depth, and range of use EDI reduces uncertainty due to timeliness
Marucheck 1997)
Survey • •
(Crum et al. 1996)
Empirical – Survey
• • •
(Massetti and Zmud 1996) (Mukhopadhyay et al. 1995)
Conceptual Empirical – case study
• • •
(Wang and Seidmann 1995)
Empirical – Modeling
•
• (Iacovou et al. 1995) Empirical – Structured Interviews • • • (Srinivasan et al. 1994) (Zaheer and Venkatraman 1994) Empirical – case study Empirical – Survey • • • • • • • • 36
(Allen et al. 1992)
Empirical – Survey
(Rogers et al. 1992)
Empirical –
and information flow Demand information is negatively related to supplier performance Production schedule sharing is positively related to supplier performance Adopters and non-adopters differ on environmental and organizational factors Slow increase in use of new transactions Customer show higher EDI satisfaction than carriers (suppliers) Identified four dimensions of EDI usage: volume, diversity, breadth, depth Real dollar savings for production are attributed to the use of EDI. Savings from inventory carrying, obsolete inventory, premium freight, paperwork Supplier’s use of EDI creates positive network externalities for himself and negative network externalities for its competitors Buyer pays a premium for EDI integrated supply For small firms, pre-adoption awareness of EDI benefits is low External pressure from customers and competitors drives adoption of EDI Implementation cost is a barrier for small firm adoption of EDI Suppliers with integrated EDI have lower shipment errors Asset specificity is positively related to the degree of electronic integration Trust is positively related to the degree of electronic integration Reciprocal investment is negatively related to the degree of electronic integration Large carriers are expanding EDI usage more than small carriers EDI is used more without contracts than with contracts Carriers implement EDI to meet customer requirements and improve customer service Warehousing firms offering EDI services
Survey questionnaire • • • •
(Johnson et al. 1992)
Empirical – Survey
(Crum and Allen 1990)
Empirical – Survey
are better able to accommodate customer requests Warehousing firms offering EDI technology provide more services to their customer than non-EDI firms 56% used firm-specific (custom) formats Meeting customer requirements/customer service was greatest benefit of implementation EDI usage positively correlated with supplier base reductions
3.2 Formatting Standards and Electronic Intermediaries EDI provides standard communication formats that allow companies to universally exchange data. An EDI standard is a specific format for translating discrete business documents into electronic messages. Each business document type is defined using an EDI standard format. Purchase orders, invoices, shipping notices, demand data and hundreds of other business documents are specifically defined for transfer between companies. Through the use of standardized format, all purchase orders will have the same electronic layout. This standardization allows the purchase order to be created and interpreted by all firms using the format standard. Firms that use the EDI standards can more easily exchange information with external firms since the published standards specify how the data is interpreted for use between firms. There are currently two organizations recognized for developing EDI message standards. The American National Standards Institute (ANSI) develops domestic standards and the United Nations (UN) creates international standards. The UN standards establish criteria for standard EDI messages for Administration, Commerce, and
37
Transport (EDIFACT) (Massetti and Zmud 1996). The ANSI X12 and the United Nations EDIFACT standards are both used for the formatting of electronic documents using EDI (Zuckerman 2004). Domestically, the ANSI X12 standard for EDI business documents is most popular as it is specifically designed to support business transactions in North America. The United Nations EDIFACT standard is used for international EDI transactions. Alternative formatting methods include XML and relation specific messaging formats (Zuckerman 2004). Although low set-up and implementation costs are making alternative formatting methods more popular, their lack of standardization limits their effectiveness. 3.2.1 EDI Exchange Networks For the purposes of this study, the sponsoring firm will be called the technology champion firm which corresponds to other research that identifies the sponsor as the relationship initiator (Iacovou et al. 1995; Truman 1998). Sponsor firms participate in the exchange of information and take a leadership role in the development, maintenance, and expansion of the IOS with their trading partners. Their customers and suppliers that join the EDI network are identified as participants, adopters or simply trading partners (Iacovou et al. 1995). For the purposes of this study, trading partner will be used to describe the firms that were invited to join the network by the technology champion firm. 3.2.2 Establishing and Maintaining an Electronic Network The exchange of EDI documents can be arranged directly between firms or by using an EDI service provider as an intermediary. The service provider offers technical support and operates the exchange network. Many firms seek the services of EDI
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intermediaries to avoid the development and maintenance costs related to creating proprietary communications networks. Researchers note that EDI implementation costs create a technology barrier as EDI champion firms attempted to expand the technology to their trading partners (Walton and Marucheck 1997). Value-added network providers (VANs) reduce the technology barrier by becoming electronic post offices for firms that do not want to build and maintain their own proprietary EDI communication networks. A firm choosing to use an integrator to implement EDI-based transactions with its trading partners would select a VAN, which in turn, works with the sponsor’s trading partners to form electronic linkages. Once the trading partner joins the electronic network, the VAN serves as a clearing-house for all EDI transactions between the sponsor firm and its trading partners. The VAN is responsible for the high-speed connectivity, security, training, and data format support to ensure compliance with various EDI standards. EDI is subject to network externalities such that the benefits to sponsors are increased as additional firms adopt the technology (Shapiro and Varian 1998). Growth of the EDI network allows sponsors to transact with more trading partners and reduce the cost of maintaining alternate parallel systems to transact with non-EDI-capable trading partners (Iacovou et al. 1995). Johnson et. al (1992) aptly note that firms are increasing their use of EDI by getting more trading partners to use EDI and by expanding the types of information exchanged using EDI. Although adoption of EDI and growth of the EDI network are in the best interest of the technology sponsors, many trading partners are reluctant to adopt the technology. Technology diffusion research recognizes that smaller firms may be at a disadvantage in adopting EDI. Studies focused on the diffusion of EDI technology recognize that the cost
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of EDI implementation can deter the adoption of EDI by some firms (Iacovou et al. 1995). Three factors have been identified for their effect on EDI adoption for small firms: perceived EDI benefits, organizational readiness, and external pressure (Crum and Allen 1990; Iacovou et al. 1995). Perceived EDI benefits are the recognition by a firm’s management that implementing EDI provides a relative competitive advantage. Organizational readiness refers to the firm’s technical and financial resources that can be allocated to EDI implementation. External pressure refers to influences outside of the firm that encourage the adoption of EDI. These external influences can originate with competitors or trading partners. Through an empirical study of seven companies, researchers found that the strongest influence of small firm adoption of EDI was external pressure from trading partners (Iacovou et al. 1995). Firms that were highly dependent on their EDI champion trading partners showed the highest likelihood of EDI adoption. In the same study, results indicated a positive relationship between perceived benefits and adoption but mixed results for the relationship of organizational readiness and EDI adoption. The pressure from sponsor firms to implement EDI has often been identified as a driver of EDI adoption by trading partners. As one might expect, meeting a customer requirement was one of the most important reasons that trucking firms implement EDI (Allen et al. 1992; Johnson et al. 1992). Moreover, firms often couple the use of EDI with other strategic initiatives such as the reduction of trading partners and the use of longer-term contracts (Allen et al. 1992; Crum and Allen 1990). Although trading partners are increasingly migrating to the Internet to support EDI transactions, their interactions with the technology champion firms are still captured
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in this dissertation. The full-service integrator providing the data for this study supports both proprietary communications links as well as communication through existing Internet channels using the AS2 specification. AS2 is a relatively new specification that is becoming the standard for securely transporting EDI data through the Internet. Firms have been expanding their use of AS2 as a way to reap the benefits of standardized EDI formatting while avoiding the cost of using a proprietary communications network. Existing EDI integrators often offer software and hosting services for Internet EDI in order to meet customer demands for hybrid solutions. The technology champion firms in this study are exchanging EDI documents through either the proprietary VAN network or are using Internet EDI. Whether a technology champion firms uses the proprietary communication network or the Internet to connect with their trading partners is immaterial since the data is captured as it is routed to the electronic mailbox. In other words, regardless of the network that transports the EDI information exchange (VAN or Internet), the interaction with a technology champion firm and its trading partners is captured by examining information sent to and from the technology champion’s EDI mailbox. 3.3 Units of Observation This dissertation adopts two distinct units of observation. The first unit of observation is used in Chapter 4 and focuses on the exchange dyad. Each dyad consists of a technology champion firm and one trading partner. Using this unit of analysis allows for the exploration of information exchange within a specific supply chain relationship. The second unit of observation is adopted in Chapter 5. This unit of observation is the
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technology champion firm. This single firm level unit of observation supports the exploration of how a firm’s use of the technology affects its performance. Both analyses focus on the inter-firm information exchanged through an EDIbased trading network. The information exchange transactions on this trading network during a twenty-four month period provide the data for both analyses. This proprietary longitudinal dataset includes observations of the electronic transactions for thirty-nine publicly traded technology champion firms and their EDI connected trading partners. These thirty-nine focal firms are the EDI technology champions that formed initial relationships with the EDI integrator and then enlisted the participation of their trading partners. The EDI integrator coordinates with each of the technology champion firm’s trading partners to create a telecommunications link either through a proprietary communications network or an alternate public network such as the Internet. 3.3.1 Firms in the Network The technology champion firms in this trading partner network represent a broad range of industries. Based on the two-digit SIC code, the thirty-nine technology champion firms are distributed across three echelons of the supply chain (manufacturing, wholesale trade, and retail trade). Of the thirty-nine firms, twenty-three of them are manufacturers, eleven are retailers, and five are wholesalers. The dataset provided by the EDI integrator includes all EDI transactions for the years 2004 and 2005 for the technology champion firms. Researchers have noted that firms can employ multiple methods and technologies to exchange information with their trading partners (Vickery et al. 2004). This being the case, the EDI network which
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provided the data may not be the primary exchange technology for a particular technology champion firm. In situations where the focal EDI network is not the primary exchange technology, changes in the data exchanged through the network may be confounded by the use of alternate channels. Since the services provided by the EDI integrator are scalable, technology champion firms use the network in various ways to support their business needs. Some technology champion firms use the network for only specific transaction types or relationships. Other technology champion firms use the network as their primary mode of interfirm communications. The EDI integrator was not able to fully assess the champion firm’s use of alternate methods or strategy in using this network. To minimize the effects of including firms where alternate technologies may be in place, technology champion firms were eliminated from the study if they did not transact order cycle data through the observed network. An assumption was made that if order cycle transactions are not being exchanged on this network then the firm must be using at least one other primary channel to transact in the supply chain. A test was conducted to verify that order related data was transacted by the technology champion firm during the study period. If the champion firm did not transact any order cycle documents with its trading partners at any time during the study period, they were eliminated from study. The resulting 39 technology champion firms included in this study represent 23 manufacturers, 5 wholesalers, and 11 retailers. The EDI integrator noted that technology champion firm relationships may vary by trading partner. As such, the potential exists that for an individual trading partner relationship, alternate technologies and communications methods may be in place. If a particular technology champion firm-trading partner dyad does not use this network as
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their primary exchange channel, any measures made from observations of this network may be biased. To minimize any potential confounding effects from unobserved information exchange, each dyadic relationship was analyzed to ensure that order cycle data was transacted by the dyad and that the information exchange was reciprocal. The presence of order cycle information exchange within the dyad provides an indication that the observed network at least supports the foundational electronic data processing functions of the dyad. By confirming the reciprocal exchange of information within the dyad, there is an increased likelihood that the observations represent a fully functioning supply chain relationship where information is shared by both parties. For the purposes of this analysis, all observed trading partner relationships include order cycle transactions and a reciprocal sharing of information across the dyad. The study period covers twenty-four months, starting in January 2004 and ending in December 2005. Transactional data are collected and transferred to a data warehouse by the EDI integrator at the close of each calendar month. Monthly extracts were made from the EDI integrator’s data warehouse using Business Objects. Each observation in the extracted files contains unique technology champion firm-trading partner volumes for each transaction type during the period. A sample of the data is shown in Table 3.2; the EDI integrator transaction database provides observations at a summary level for each dyadic relationship on the network.
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Table 3.2 Sample of Trading Partner Network Data Observation Technology Send / Trading Period Champion Receive Partner Firm 1 X S A Jan. 2005 2 X S A Feb. 2005 3 Y R B Feb. 2005 …n
EDI Transaction ID 850 304 610
Transaction Count 23 30 341
As illustrated in Table 3.2, each observation identifies the technology champion firm and its exchange with a specific trading partner during the period. The identities of the technology champion firms are masked throughout the analysis under the terms of the non-disclosure agreement with the EDI integrator. The identities of the trading partner firms were not supplied by the network provider. Each observation then identifies whether the technology champion firm received ( R ) the EDI document or sent ( S ) the EDI document. The EDI integrator assigns unique mailbox numbers to each of the trading partners to facilitate the routing of EDI messages. The period is the abbreviated month and year of the transaction. The EDI Transaction ID is an alpha numeric value that identifies either an ANSI X12 standardized message, an EDIFACT standardized message, or a generic value identifying a firm specific custom message. Examples of standard EDI transactions under the ANSI X12 and United Nations EDIFACT message types are included in Appendix A. The termination study developed in Chapter 4 uses the monthly observations to support the analysis of information exchange characteristics and relationship termination. For the technology champion firm performance analysis developed in Chapter 5, the monthly information exchange data is aggregated to the calendar quarter. This
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aggregation allows for the matching of firm quarter information exchange measures to the publicly available firm financial data. Technology champion firm’s financial and operational measures were obtained from Standard and Poor’s Compustat financial database. Public companies provide selected operational and financial information on a quarterly and annual basis. The Compustat data have been extracted and matched for all thirty-nine technology champion firms for each of the eight quarterly EDI observation periods. 3.3.2 Key Constructs The three dimensions of transactions outlined by Williamson (1975 and 1985) are found within the measures commonly used in the research of information exchange and EDI adoption. Researchers have identified some characteristics of information exchange including information frequency, information diversity, and degree of customization (Crum et al. 1998; Massetti and Zmud 1996; Srinivasan et al. 1994; Williams et al. 1998). These richer measures of information exchange have extended research beyond the foundational level of binary measures which only measured if trading partners shared information. Greater interest is now focused on what information is exchanged and how the exchange affects supply chain performance (Saeed et al. 2005). This dissertation builds on these empirical measures and extends them to recognize the directional properties of information exchange and the diversity of information exchange.
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Directionality of Information Exchange Since this dataset provides a unique view of supply chain information exchange, the standard frequency measures of TCT and information exchange research are being combined and augmented to recognize not only the number of exchanges each month (frequency) but also the direction of the exchange. Since direction of exchange is sensitive to the position of the focal party within the exchange relationship, this study will maintain all statements about direction of exchange based on the perspective of the technology champion firm. Information exchange noted as “Send” represents information transferred from the technology champion firm to the trading partner. Information exchange noted as “Receive” represents information transferred from the trading partner firm to the technology champion firm as illustrated in Figure 3.1. Figure 3.1 Information Exchange Directionality
Trading Partner A
RE CE
IVE
SE ND
I VE CE E R ND SE
Trading Partner D
Trading Partner B
RECEIVE SEND
Technology Champion Firm
RECEIVE SEND
Trading Partner E
Trading Partner C
VE EI C ND RE SE
REC EI
SEN D
VE
Trading Partner F
Suppliers
Focal Firm
Customers
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Figure 3.1 has been labeled to associate the electronic exchange network with a traditional supply chain configuration. Specifically, the roles of supplier, focal firm and customer have been noted to illustrate the flow of information across the network. The roles of participants in the electronic exchange network used in this dissertation are not specified since the roles are based on each unique transaction rather than on the specific location of a firm within the supply chain. Potentially, a trading partner may supply raw materials to a focal firm and then in a later transaction purchase finished goods from the same focal firm, thus the trading partner’s role could change from supplier to customer depending on the transaction. What is unique in the structure of this network is that technology champion firms that are retailers predominately interact only with their suppliers through the exchange network. Consumers do not use EDI value added networks to transact with retailers. Information Type The level of detail available in an EDI enabled exchange network allows for the categorization of information by type as well as volume. Each transaction is identified with a transaction code to identify the functional process to which the information belongs. The sending firm identifies the transaction type so that the receiving firm knows where to route the information and which EDI standard to use when accepting the data into their system. The standardized formatting and identification of EDI transactions allows the transactions to be identified by the type of information they include. A preliminary categorization of the EDI standard transaction types into the two previously defined
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categories was provided to the EDI network integrator. Account executives then responded individually based on their knowledge of how the transaction types are used on the network. Modifications were made to the categorization and the revised categories were returned to the network integrator for review. The final form of the grouping used for all subsequent analysis is included in Appendices B and C. The use of standardized transaction types is a key element of EDI networks. In an EDI enabled network, purchase orders are identified with transaction code 850. The EDI ANSI X12 standard for transaction code 850 provides details of field locations and formats such that the 850 transactions from any trading partner can easily be incorporated into the host firm’s computerized order processing system. As described in Chapter 2, these transaction codes are used in this study to group information exchange documents as being either transactional or enhanced. 3.4 Descriptive Statistics 3.4.1 Descriptive Statistics of the Exchange Network The EDI exchange network provides a unique view of the electronic information exchanges for thirty-nine technology champion firms and their 18,644 trading partner relationships. Over a two-year period, there are 320,788,026 business documents exchanged in this network. Based on the categorization of information exchange provided in Chapter 2, these exchanges are representative of both the transactional and enhanced types. As described in Table 3.3, a small percentage of the documents exchanged (2%) could not be identified with either category. This occurs when nonstandard EDI transactions are defined by a technology champion firm or within a specific dyad. EDI participants may use non-standard transaction codes to identify documents 49
that are unique to their trading partner relationships. Seventy-five percent of the transactions are categorized as transactional exchanges. Twenty-three percent of the documents exchanged are categorized as enhanced information exchange. The remaining two percent were not mapped to either primary group and are identified as miscellaneous. Table 3.3 Network Descriptive Statistics Technology Champion Firms Trading Partner Relationships Transactional Information Exchanges Enhanced Information Exchanges Miscellaneous Information Exchanges Unique Document Types
39 18,644 241,014,613 72,927,949 6,845,464 352
Since the data aggregated monthly by the EDI integrator is oriented to the technology champion firms, the number of trading partners on the network will be slightly overstated. This occurs when a technology champion firm is a trading partner of another technology champion firm. When a technology champion firm is a supplier or customer of another technology champion firm, they will be included as both a technology champion firm and a trading partner. This “double counting” is not be an issue for two reasons. First, the firm should be treated differently in each observation because of the role it plays in the information exchange—either as the technology champion or as the trading partner. Second, the double counting has minimal impact on the study outcomes due to the large number of trading partners and the relatively small number of technology champion firms. 3.4.2 Descriptive Statistics of the Supply Chain Echelons Within the network, technology champion firms represent three distinct echelons of the supply chain (manufacturing, wholesale trade, and retail trade). Using the
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information exchange matrix introduced in Figure 2.2, the portfolio of trading partner relationships can be characterized for each echelon of the supply chain. By considering the types of information exchanged with each trading partner, a descriptive analysis provides some understanding of how each echelon uses information exchange to interact with their pool of trading partners. Trading partner relationships can be categorized using the typology identified in Figure 2.2. By comparing the volume of transactional and enhanced information exchange for each trading partner relationship to the mean for the technology champion firm, each trading partner relationship can be associated with a quadrant in the information exchange matrix. Figure 3.2 shows the distribution of the trading partner relationships in the network based on the information exchange quadrants.
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Figure 3.2 Trading Partner Relationship by Information Exchange Quadrant
Transactional High Information mean Exchange Volume Low
I. Transactional Relationships N=1,170
II. Close Relationships N=1,345
III. Arms-length Relationships N=15,111 Low
mean
IV. Enhanced Relationships N=1,018 High
Enhanced Information Exchange Volume
Each trading partner relationship is associated with a quadrant of the matrix based on the mean exchange volume of the technology champion firm in the relationship. As shown in Figure 3.2, eighty percent of the trading partner relationships in the network are characterized as arms-length based on their relatively low volumes of transactional and enhanced information exchange. Conversely, seven percent of the trading partner relationships in the network are characterized by a high volume of both transactional and enhanced information exchange as identified by the III Close Relationships quadrant. This distribution of relationships supports research which suggests that firms are showing a tendency toward managing their trading partner relationships as a portfolio consisting of a wide array of types (Krapfel et al. 1991). These relationship types are often characterized by their closeness and their ability to support strategic initiatives.
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Technology champion firms vary in their use of close and arms-length relationships. From a supply chain echelon perspective, manufacturing firms have the lowest percentage of arms-length relationships of any of the three echelons but it still represents seventy-five percent of their relationships. Retail trade firms have the highest percentage of arms-length relationship at eighty-two percent. Expanding this analysis to consider the performance of trading partner relationships operating in each quadrant may provide insight into a relationship between information exchange characteristics and relationship performance. Strategy research has recognized survival as a measure of firm performance (Shaver and Flyer 2000). The importance of relationship survival is echoed in marketing literature which recognizes that firms are reducing the number of trading partners they exchange with in order to develop competitive benefits with selected trading partners (Spekman 1988). Additionally, researchers have noted that over time, dissatisfied exchange partners will leave the relationship to seek new partners (Hirschman 1970). Subsequently, trading partners can benefit by avoiding termination. Performance in an electronic exchange network can be observed as exchange continued over time. As illustrated in Table 3.4, trading partner relationships characterized by low levels of transactional and enhanced information exchange experience the highest termination rates. Termination here is measured by exchange occurring in 2004 but not in 2005. The unit of observation is limiting since it creates the possibility that a trading partner relationship may actually be terminated in 2004 which would generate low volumes of information exchange which could potentially assign the relationship incorrectly to the III Arms-length quadrant. Table 3.4 does provide some 53
initial insight into the variation of relationship termination rates across the network. It should be noted that the absence of information exchange through the network is assumed to indicate the termination of the business relationship. The potential to assign relationships to incorrect quadrants is addressed in subsequent chapters by using multiple methods which evaluate information exchange characteristics and performance on a month-to-month and quarter-by-quarter basis. The use of smaller discrete time period observations allows for a closer association of information exchange characteristics and the performance outcomes. Retailers on average show the highest termination rate with trading partners characterized as arms-length (41%) and lowest termination rate with trading partners characterized as close relationships (8%). Manufacturers and wholesalers show similar differences between arms-length and close relationships however, wholesalers on average have the lowest termination rate with trading partner relationships characterized as transactional (3%). Additional analysis will be conducted in each study to explore how the effects of information exchange may vary based on the location of a firm within the supply chain.
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Table 3.4 Average Termination Rates by Firm (From 2004 to 2005)
Firm I. Transactional Relationships Termination Rate Manufacturer A Manufacturer B Manufacturer C Manufacturer D Manufacturer E Manufacturer F Manufacturer G Manufacturer H Manufacturer I Manufacturer J Manufacturer K Manufacturer L Manufacturer M Manufacturer N Manufacturer O Manufacturer P Manufacturer Q Manufacturer R Manufacturer S Manufacturer T Manufacturer U Manufacturer V Manufacturer W Mfg Average Wholesaler A Wholesaler B Wholesaler C Wholesaler D Wholesaler E Whslr Average Retailer A Retailer B Retailer C Retailer D Retailer E Retailer F Retailer G Retailer H Retailer J Retailer K Retailer L Rtlr Average Overall 4% 0% 0% 0% 0% 0% 7% 4% 23% 0% 100% 22% 11% 20% 0% 0% 2% 0% 0% 11% 20% 67% 0% 12% 0% 2% 10% 0% 3% 3% 13% 0% 0% 12% 17% 20% 43% 12% 11% 4% 4% 10% 10% N 47 6 4 3 11 1 15 23 22 4 14 28 18 5 1 7 62 2 4 9 10 3 6 13 23 41 21 24 64 35 23 63 17 265 18 5 7 222 9 51 12 63 1,170 II. Close Relationships Termination Rate 16% 0% 0% 0% 0% 33% 0% 0% 0% 0% 100% 18% 18% 17% 0% 0% 2% 0% 0% 0% 13% 0% 0% 8% 3% 5% 18% 2% 3% 4% 5% 2% 0% 14% 11% 0% 0% 7% 19% 3% 0% 8% 7% III. Arms-length Relationships Termination Rate 41% 10% 14% 58% 19% 29% 57% 17% 35% 14% 100% 49% 32% 41% 11% 10% 9% 25% 19% 29% 22% 8% 10% 30% 32% 35% 34% 17% 16% 23% 23% 14% 0% 48% 28% 46% 90% 49% 33% 27% 28% 41% 36% N 354 41 69 45 223 52 83 88 83 22 110 205 146 63 19 96 481 81 31 21 91 13 41 107 324 251 219 418 827 408 397 303 189 2,203 198 99 68 4,484 255 1,050 1,368 965 15,111 IV. Enhanced Relationships Termination Rate 0% 0% 33% 0% 4% 0% 33% 7% 33% 0% 100% 20% 57% 0% 25% 0% 0% 0% 0% 33% 25% 20% 0% 10% 0% 29% 18% 2% 4% 7% 7% 5% 0% 29% 0% 0% 40% 9% 8% 1% 0% 10% 10% N 12 4 3 4 49 2 3 14 18 3 5 5 7 6 4 6 57 5 26 3 4 10 4 11 9 14 11 55 53 28 15 41 1 90 1 1 5 316 12 113 27 57 1,018 Overall Termination Rate 35% 8% 12% 44% 14% 28% 45% 13% 32% 10% 100% 44% 30% 34% 12% 9% 7% 22% 8% 24% 21% 12% 8% 25% 27% 29% 30% 13% 14% 19% 20% 10% 0% 42% 26% 43% 80% 41% 30% 23% 27% 35% 31% N 19 1 10 7 23 6 8 8 3 1 5 11 6 11 1 3 96 6 9 1 15 0 1 11 32 19 17 57 58 37 42 63 14 185 18 3 3 492 27 31 33 83 1,345 N 432 52 86 59 306 61 109 133 126 30 134 249 177 85 25 112 696 94 70 34 120 26 52 142 388 325 268 554 1,002 507 477 470 221 2,743 235 108 83 5,514 303 1,245 1,440 1,167 18,644
Termination Rate is based on relationship termination prior to January 1, 2005
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One might assume that the trading partner relationships in the III Arms-length quadrant provide sporadic materials on an as-needed basis since they are characterized by low levels of transactional exchange, however, there are other trading partner relationships with similar low levels of transactional information exchange that experience significantly lower levels of termination. Trading partner relationships in the IV Enhanced quadrant are characterized by similar low levels of transactional information exchange but experience termination rates below ten percent across any of the three echelons. From an information exchange perspective, the difference for trading partner relationships in the IV Enhanced quadrant also includes the exchange of relatively high levels of information beyond what is exchanged to support the routine order cycle. This dissertation will develop this relationship between termination rates and the closeness of trading partner relationships. 3.5 Research Question Theory and extant research recognize the critical role of information exchange in supply chains. The descriptive analysis in this chapter indicates that technology champion firms vary in how they use information to interact with their trading partners. Firms differ in their use of distinct types of information, volumes of information exchange, and their balance between sending and receiving information. Theory supports that information exchange affects the performance of both relationships and individual firms. This descriptive analysis supports that based on relationship termination, performance varies with information exchange characteristics
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when relationship performance is defined as survival. The literature is not clear on what characteristics of information exchange affect supply chain performance. This study proceeds with Chapter 4 by developing hypotheses and empirically testing relationship performance based on relationship termination. This approach focuses specifically on how information exchange affects trading partner relationship survival.
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Chapter 4: Trading Partner Relationships
4.1 Introduction This part of the dissertation contributes to the literature by providing an empirical examination of the effects of information exchange on trading partner relationship termination. Specifically, this study provides a longitudinal examination of trading partner relationship termination in an electronically-mediated B2B network. The network includes observations of trading partner relationships across the manufactures, wholesalers and retailers. The level of analysis of this study is the trading dyad. Dyadic studies have been of great interest to researchers since it is the smallest relational unit in the supply chain where interfirm actions can enhance or cripple the supply chain (Anderson et al. 1994; Dwyer et al. 1987; Dyer and Singh 1998; Svensson 2004; Whipple et al. 2002). Research into supply chain relationships often takes either a buyer’s or seller’s perspective and focuses on the performance of one party. This dyadic study uses termination as the performance measure recognizing that both participants invest in the business relationship and neither achieves future relational benefits if the relationship is terminated. By adopting a dyadic level of analysis, this study captures the unique characteristics that are often overlooked in a firm-level or industry-level analysis. The performance measure of interest in this study is relationship termination. Relationship termination is particularly pertinent as a performance measure when the unit of analysis is a relational dyad. Research has recognized that there is value in supply chain relationships that endure over time. Relationships between supply chain
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participants are noted for creating sustainable competitive advantage because of their ability to develop causal ambiguity and time compression diseconomies (Dierickx and Cool 1989; Dyer and Singh 1998). Causal ambiguity develops in relationships due to the complex nature of the relationship whereby competitors can observe the relationship but still not fully duplicate it such that they can obtain the same competitive results. From an interfirm process orientation, time compression diseconomies recognize that the benefits of the relationship are built over time and that if a competitor desires to duplicate the relationship they will incur greater costs to develop the relationship quickly. Additionally, firms often allocate resources to develop relationships and there is an expectation that the initial investment will provide benefits for an extended period of time (Jackson 1985). When firms invest in relationships and those relationships are terminated, they return to the market to invest in developing new relationships. Firm survival is often the focus of strategic management research due to the inherent interest academically and practically in avoiding termination or exit from a market (Cottrell and Nault 2004; Disney et al. 2003). This study focuses on relationship termination as a performance measure and applies it to the analysis of dyadic supply chain relationships. There are several contributions of this examination into the use of information exchange in supply chains and its impact on relationship termination. First, this study differentiates between the types of information exchanged within B2B industrial supply chain relationships. The effects of transactional information exchange are separated from the effects of enhanced information exchange. Second, objective measures of longitudinal information exchange are captured and used for empirical research. Third, the use of two statistical methodologies, provides an improved understanding of how
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individual firms and their exchange of information is associated with the termination of their trading partner relationships. These results are expanded by associating the relational outcomes of firms in different echelons of the supply chain (i.e. manufacturers, wholesaler, and retailers) with their use information exchange in their trading partner relationships. The remainder of this chapter is organized as follows. Section 4.2 provides a development of hypotheses; section 4.3 develops the research methodology; section 4.4 provides the model results and hypotheses tests; section 4.5 discusses the contribution and limitations; and section 4.6 concludes this portion of the dissertation. 4.2 Development of Hypotheses From TCT, transactional volume is an important part of understanding the closeness of a supply chain relationship. Webster (1992) recognized that interfirm transactions were migrating from discrete market transactions toward relational exchange where firms experience recurring transactions over time. As firms move away from discrete market transactions, they have the potential to identify new opportunities through coordination with their trading partners as they understand each other’s needs more through repeated exchanges (Webster 1992). In an arms-length market transaction, there is minimal expectation of future exchange so the motivation to perform is limited. Under such conditions, trading partners may choose to not fulfill the contract with limited ramifications. Arms-length transactions are characterized by: limited investment of specific assets, minimal information exchange, low levels of interdependence, low transaction costs, and minimal
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investment in governance structures (Dyer and Singh 1998; Williamson 1985). The result is that the buyer experiences minimal costs related to selecting a new supplier for subsequent purchases – there is nothing unique about the relationship. In this situation there is no commitment or loyalty to the relationship which allows either party to exit the relationship without expression of cause or justification (Hirschman 1970). As transactions move from discrete to recurring, the foundations of a relationship emerge (Webster 1992). A distinction between discrete and relational exchange then is the repetition of procurement exchanges over time. As transactions move away from discrete market events, there is the potential to develop relational aspects including trust, dependence, and loyalty (Hirschman 1970; Morgan and Hunt 1994). The replacement of market transactions with multi-year contracts further creates an environment of lock-in whereby switching costs are legally imposed to minimize exit (Shapiro and Varian 1998). Moving away from market transactions introduces a longer-term orientation and closer relationships (Kalwani and Narayandas 1995). As dependence increases, the opportunity for trading partners to act in their own best interests increases. A sole supplier of an input has the opportunity to extract additional margin with less chance of repercussions in the short-run. To ensure against such actions, firms may increase monitoring mechanisms or realign their relationships in order to minimize risk (Williamson 1975). At higher levels of transactional volume, termination may actually become a greater risk such that a non-linear relationship exists between transactional volume and relationship termination.
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To test the combination of these opposing forces, a non-linear hypothesis is put forth as, Hypothesis 1: There is a u-shaped relationship between transactional information exchange volume and relationship termination. As relationships move beyond a basic transactional focus, firms can create unique competitive advantages (Dyer and Singh 1998). By exchanging information, firms identify opportunities to combine complementary resources. A manufacturer, The Campbell Soup Company, formed ties with their retailers beyond the sales-to-purchasing interface (Lee et al. 1999). These additional linkages provided information that supported the development of processes whereby Campbell’s could monitor end customer demand and provide inventory management services for their retailers. Campbell’s enhanced their relationship by embedding interfirm routines that provided unique value to the retailer. The retailer experienced improved performance in the form of lower stockouts and higher inventory turnover. Research has also recognized that too much information can be detrimental to performance. Through a simulation, researchers allowed for large volumes of point-ofsale information to flow upstream in the supply chain (Steckel et al. 2004). Results of their study indicate that depending on the demand variability, additional information may not aid the planning process but actually diminish performance by distracting upstream decision makers. One might recognize the potential to overload a relationship with information when IT is used to mediate the exchange. The marginal cost of sending additional information is low once the fixed costs of formatting the data and establishing
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the network are incurred. One might argue that the additional information could be ignored but the filtering of information required to identify the pertinent information may still diminish performance as previously found. Receiving an overload of information can be detrimental to performance either because the receiver must filter the information to identify the pertinent parts or may become frustrated and ignore all the information from the sender. This diminishing affect of too much information suggests that there is a non-linear relationship between the volume of enhanced information exchange and relationship performance. Thus, Hypothesis 2: There is a u-shaped relationship between enhanced information exchange volume and relationship termination. Enhanced information may be more indicative of a close relationship and, therefore, will decrease the likelihood of relationship termination. Strategy research recognizes the value of information exchange beyond the sales-to-purchasing interface (Dyer and Singh 1998). This enhanced information exchange occurs when multiple interorganizational functions integrate. In practice, this occurs when the serial interface between the marketing function of the selling firm and the sales function of the buying firm is augmented by the creation of parallel exchanges between multiple operational areas in each firm. These multiple functional interfirm interfaces are noted as a source innovation such that new products, services, and technologies are developed (Dyer and Singh 1998). The sales-to-purchasing interface is supported by routine order cycle document exchanges (requisitions, purchase orders, invoices, etc.). Exchanges between
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the functional areas of two firms would be supported through the exchange of additional information described in this study as enhanced (demand data, forecasts, inventory levels, forecasts, production schedules, etc.). Given the additional functionality supported by the exchange of enhanced information, one could expect that a single procurement transaction might be the result of the multiple exchanges of enhanced information. A purchase order could be preceded by the exchange of demand forecasts, production schedules, and inventory positions which improve the quality of the purchase decision. After ordering, additional information on shipment schedules, production sequences, and quality control tests may be provided. This post-sale information provides additional value by smoothing the integration of the purchased input. The order cycle information is then preceded and followed by enhanced information which improves the quality of the decision and the use of the delivered input. Research has recognized that early implementations of IT for information exchange in the supply chain were oriented toward transactional efficiency (Narasimhan and Kim 2001; Williams et al. 1997). These studies acknowledge that the use of information exchange goes beyond supporting the foundational processes of the support activities in the supply chain. Specifically, IT can be used to affect the primary activities of the value creation which include: operations, inbound logistics, outbound logistics, marketing, sales, and service functions (Porter 1980). As firms look to implement justin-time (JIT) and other strategic programs with their suppliers, key information is exchanged in order to synchronize interfirm processes (Bardi et al. 1994). Through the exchange of enhanced information, firms are able to integrate similar functions,
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consolidate redundant functions, and eliminate unnecessary activities thus improving their performance beyond low cost transactional processing. Thus, Hypothesis 3: The ratio of enhanced to transactional information exchange volume is negatively associated with relationship termination. The three proposed hypotheses are summarized in Figure 4.1. Hypothesis 1 and Hypothesis 2 identify u-shaped relationships between the information exchange characteristics and relationship termination. Hypothesis 3 proposes a negative relationship between the ratio of enhanced to transactional information exchange and relationship termination. Figure 4.1 Information Exchange Hypotheses
Information Exchange Characteristics Transactional Information Exchange Volume
H1 (u-shaped)
Enhanced Information Exchange Volume Ratio of Enhanced To Transactional Information Exchange Volume
H2 (u-shaped)
Relationship Termination
H3 (-)
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4.3 Research Methodology The goal of this study is to explore the relationship between information exchange and relationship termination in a business-to-business dyad. Using the electronicallymediated trading partner network detailed in Chapter 3, specific measures of information exchange are defined to support hypothesis testing to associate dyadic information exchange characteristics with relationship termination. Since previous research has not addressed information exchange characteristics and supply chain performance at this level of granularity, a multi-level analysis is provided. First, a logistic regression model is developed to support an event history analysis using the information exchange characteristics as independent variables and relationship termination as a dichotomous dependent variable. Second, an alternative event history analysis is developed using the Cox Proportional Hazards Model. 4.3.1 Data The data provided for this study support multiple levels of analysis. Analysis of trading partner relationship termination is best understood through longitudinal data at the dyadic relationship level. The dataset includes monthly observations of information exchange between each of the thirty-nine technology champion firms and their electronically-mediated trading partners. Each dyadic relationship is characterized by its information exchange and its potential termination. Using multiple analysis tools, this portion of the dissertation will evaluate trading partner relationship termination at a dyadic level.
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A key aspect of event history analysis is identifying the group of study participants that are at risk of the hazard occurring during a given period. Trading partner relationships that are at risk during a given time period are referred to as the “risk set” (Allison 1985). In a supply chain context, trading partner relationships that are terminated in January are no longer at risk in February. At the end of each period in the study, the set of study participants is reduced by the number of relationships that experience termination. Event history studies require knowledge of the number of participants that are at risk each period and the number of participants that experience the hazard (termination) each period. Event history analysis then attempts to associate explanatory variables with the variation in the period-to-period hazard occurrences. 4.3.2 Measures Trading Partner Relationship Termination The termination of a trading partner relationship is operationalized through the data of an electronically mediated information exchange network. Within a given period of time, the technology champion firm either transacts with the trading partner or they do not. When information ceases to be exchanged, the relationship is considered to be terminated. A binary variable is used to identify whether the relationship has been terminated. For each month, the hazard variable is coded as ‘0’ if information is exchanged within the dyad. When the dyadic relationship ceases to exchange information, the hazard variable is coded ‘1’ to recognize the termination of the relationship. Due to the potential seasonality of some trading partner relationships, gaps may occur in the month-to-month data. These gaps would make it appear that a dyadic
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relationship was terminated and then re-started one or more months later. In a supply chain context, seasonality of some relationships may create instances where a dyad does not exchange information for a month or more but the relationship itself is continuing with transactions appearing later in the study period. A supplier of winter coats may not receive orders from a specific retailer during the spring months, but as the planning for winter sales begins, information will start being exchanged and the relationship will continue. Trading partner relationships are only considered terminated if they stop transacting and do not resume transacting in a later month during the study. The termination variable is set to ‘1’ to identify termination of a relationship only if the information exchange does not resume during the study period. Relationship termination is identified by the absence of transactions in future periods. Since the dataset includes data for twenty-four months, there are no relationships identified as terminated in the last month of the study (month 24). Without data for the 25th month, there is no way to ascertain whether relationships continued or were terminated. This situation is common in longitudinal studies where variables are based on differences between observation periods. As a result, there is no variation in the dependent variable for the twenty-fourth month of the study and those observations are all dropped prior to estimating the model. Transactional Information Exchange Volume The volume of transactional information exchange is measured as a count variable (in millions). Information is identified as transactional based on the categorization of EDI transaction types described in Chapter 2 and specifically listed in Appendix B. Each
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EDI transaction type is identified as containing either transactional information or enhanced information. EDI transactions identified as containing transactional information are aggregated by the month for each dyad. In a longitudinal analysis, the use of a cumulative measure of transactional volume is critical due the nature of termination in a supply chain context. Practitioners recognize that when a relationship is targeted for termination, the exchange of information does not stop immediately but instead decreases over time. This situation occurs either through an intentional “weaning” of the trading partner or simply because the invoices and payments from recent shipments may require additional time before all outstanding transactions are reconciled. Use of a single month measure of information exchange volume would relate the final termination event to the prior month’s potentially miniscule transactional exchanges made to reconcile a relationship whose primary exchange potentially ended months earlier. To address this issue, this study uses aggregate exchange volumes from the start of the study to the final month prior to relationship termination. This treatment of information exchange as a cumulative measure allows for termination to be explained based on a full measure of information exchange characteristics rather than potentially minimal end-of-life information exchanges that would not be representative of the true characteristics of the relationship. To capture the non-linear relationship between transactional information exchange and trading partner relationship termination, a squared variable is introduced. The study of information and knowledge often recognize an increasing or decreasing effect at higher levels of exchange which creates a non-linear relationship. In such studies, the predictor variable causing the non-linear relationship is modeled using both
69
its calculated value and its squared value (Berman et al. 2002; Steckel et al. 2004) For each dyadic monthly observation, the mathematical square of the cumulative transactional information volume is calculated. Enhanced Information Exchange Volume The volume of enhanced information exchange is measured as a count variable (in millions) and is analogous to the transactional information exchange measure described above. Based on the categorization of enhanced information described in Chapter 2 and listed in Appendix C, EDI transaction types are identified as containing either enhanced information or transactional information. EDI transactions identified as containing enhanced information are aggregated by month and trading dyad. To capture the non-linear relationship between the exchange of enhanced information and trading partner relationship termination, a squared variable is introduced as described previously for the transactional exchange volume measure. For each dyadic monthly observation, the mathematical square of the enhanced information volume is calculated. Transactional to Enhanced Information Exchange Ratio To measure the relationship between the exchange of enhanced information and transactional information, a simple ratio is calculated. The ratio addresses the comparative effect of enhanced information to transactional information on the termination of a trading partner relationship. As shown in 4.1, the ratio is calculated by
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dividing the enhanced information exchange volume by the transactional information exchange volume for each unique trading dyad and month combination.
RATIO ijt =
Enhanced_Volume ijt Transactional_Volume ijt
[ 4.1]
where ij represents a technology champion firm-trading partner dyad and t is a specific time period
Control Variable – Firm Dummy The data for this analysis includes observations of thirty-nine technology champion firms. To control for the firm specific variation in the dataset, a series of dummy variables are included to identify the technology champion firms associated with each observation. The thirty-nine technology champion firms require the inclusion of an additional thirty-eight binary variables. This control variable is required in both the Logistic regression and the Cox Proportional Hazards Model to control for firm effects. Control Variable – Month The data includes monthly observations for each technology champion-trading partner dyad with the potential for each dyad to include up to twenty-four monthly observations. A series of dummy variables are included to identify the month of each observation and control for variation due to time. The twenty-four monthly observation periods require the use of an additional twenty-three binary month dummy variables. This variable is used in the logistic regression to control for the time effects. Control variables for time are not required in the Cox Proportional Hazards Model since the time factor is controlled using the DURATION variable.
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4.3.3 Modeling Event History The study of events and their causes have been the source of great interest in many academic fields. The event of interest is often failure in the form of death or termination with the studies addressing the potential causes of the event. In the context of supply chain relationships, the hazard encountered is that the trading partner relationship is terminated. This section examines how the hazard (termination of the supply chain relationship) is associated with the characteristics of information exchange. In studying events, the data include the history of the event occurrence and measures of potential explanatory variables. Typically, these explanatory variables change over time creating the need to model the history as a series of longitudinal observations. Using standard regression techniques to model the data often causes unnecessary loss of information (Allison 1985). Similarly, loss of information can occur if the event of interest does not occur during the study period. If the relationship does not terminate during the study period, the length of the relationship is unknown and could be assumed to be the full length of the study, but that too would underestimate its true value in that the relationship could continue for years beyond the study. In event studies, this is referred to as censoring. Censoring is the situation where variables are measured within the range of the study period but their values are unknown prior to the study and after the study. Left-censoring refers to the unknown measures of variables prior to the study. Right-censoring refers to the unknown values after the study. Censoring also occurs when a participant in the study leaves the study prior to its completion and without experiencing the hazard event. In an electronically mediated trading partner network, firms may migrate to new technologies or change network providers which could 72
potentially eliminate them from the study. Models that allow for censoring of data keep the observations of hazard and non-hazard relationships up to the point when the participant leaves the study thus minimizing the effects of lost data points. In either case, the analysis of event history requires unique treatment to avoid the effects of both censored data and time varying explanatory variables. Each of these situations can be addressed using either a logistic regression or a proportional hazard model to estimate the likelihood of the event (Allison 1985). This study presents results for hypotheses tests using both methods. Both the logistic regression and proportional hazards model estimate the likelihood of an event occurring. This allows for an additional verification of the predictive power of the models by holding back a portion of the data for post-hoc analysis of the estimated model coefficients. To facilitate this test, ten percent of the observations are not included in the data used to estimate the coefficients. This randomly generated “hold-out” sample will be used for a post-hoc analysis which is presented in the discussion of the study results. 4.3.4 Logistic Regression Modeling A logistic regression may be used to specify how the probability of an event depends on selected explanatory variables. First, a logistic regression supports the use of a binary dependent variable to identify the event occurrence. The event of interest in this study is the termination of the trading partner relationship. Second, the model estimates the probability of the hazard (termination) occurring during a specific time period given the levels of the explanatory variables.
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Using a discrete-time model, variation in the hazard in each time period is allowed by letting each intercept term (?) identify a unique time period. When using a logistic regression, these time period constants are modeled as a set of dummy variables, one for each of the observed time periods (Allison 1985). Additionally, this dataset contain observations of multiple trading partner relationships for each firm so a vector of firm dummy variables are also included in the model as shown in 4.2.
log ( Pt / (1 ? Pt ) ) = ? t + ?1TRANSACTIONAL_VOLUME t-1 +
?2TRANSACTIONAL_VOLUME2 t-1 + ?3 ENHANCED_VOLUME t-1 + ?4 ENHANCED_VOLUME 2 t-1 +?5 RATIO t-1 + ? ? jMONTH t +? ? i FIRMi + ?
j=0 i=0 n n
[4.2]
where Pt is the probability of termination of a trading partner relationship
A maximum likelihood estimation is used to calculate the coefficients. The overall effect is that the data are modeled such that a unique observation is created to represent each period that a trading partner relationship is at risk. For example, trading partner relationships that terminated after four months contribute four trading partnermonths of observations. Trading partner relationships that have not terminated by the end of the twenty-four months of the study are considered to be censored. Censoring in this context refers to the fact that the relationships may have terminated after the study period but that information is unknown and not within the range of study. Censored trading partner relationships then contribute the maximum twenty-four trading partnermonths. For each trading partner-month, the dependent variable is coded ‘1’ if the trading partner relationship terminated that month, otherwise it is coded zero. The information 74
exchange characteristics then serve as explanatory variables and take on their cumulative values up through the month prior to termination. This lagging of the explanatory variables is critical in the analysis of electronically mediated information exchange. Since the dependent variable (termination) is defined as the absence of any information exchange on the network, the month in which the trading partner relationship is absent will show zero values for both the transactional and enhanced information exchange volumes. A simple lagging of one month then relates the termination in one month to the information exchange characteristics cumulative to the month prior to termination. For example, a trading partner relationship that shows no transactions in October is coded as terminated for the month of October and the information exchange characteristics are measured as cumulative up to the termination occurrence. Since the relationship had no transactions in October, the transactional and enhanced information exchange volumes are cumulative through September. The variables for the logistic regression analysis are summarized in Table 4.1.
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Table 4.1 Definition of Logistic Regression Model Variables Variable Definition Dependent Variable 1 if terminated in that month, 0 otherwise TERMINATIONit Independent Variables TRANSACTIONAL_VOLUMEijt Cumulative volume of transactional information exchange between a trading partner and the technology champion firm 2 TRANSACTIONAL_VOLUME ijt The mathematical square of the cumulative volume of transactional information exchange between a trading partner and the technology champion firm Cumulative volume of enhanced information ENHANCED_VOLUMEijt exchange between a trading partner and the technology champion firm 2 The mathematical square of the volume of enhanced ENHANCED_VOLUME ijt information exchange between a trading partner and the technology champion firm The ratio of cumulative enhanced information RATIOij exchange volume to the cumulative transactional information exchange volume between a trading partner and the technology champion firm A series of binary dummy variables to identify the MONTHt month of observation A series of binary dummy variables to identify the FIRMi technology champion firm As noted by Allison (1985), the logistic regression procedure adequately handles both the censoring and time-varying explanatory variable issues that can be problematic in estimating the probability of a hazard occurring. Censoring is addressed in this study by including trading partner relationship observations for all periods in which their termination was at risk. Variances in the explanatory variables in each period are captured by including each trading partner-month combination as a separate observation and including a series of dummy variables to identify the month. This application of a logistic regression is specifically adapted to account for changes in the hazard rate over time. By including a set of dummy variables for the time periods, the intercept is adapted for each discrete time (Allison 1985). This specification
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supports the inclusion of time-varying explanatory variables which recognize that the probability of a hazard occurring is explained through the cumulative exchange of information within a relationship while controlling for the effects of time. 4.3.5 Cox Proportional Hazards Model An alternative method to model event history is the Cox Proportional Hazards regression analysis. This technique addresses the occurrence or nonoccurrence of an event and its timing. The Cox Proportional Hazards Model (hereafter simply called the hazards model) has been used in empirical research to study the likelihood of market leader dethronement (Ferrier et al. 1999), manufacturing firm survival in the UK (Disney et al. 2003), and the likelihood of a customer purchasing additional financial products given demographic factors (Thomas et al. 2005). In this study, the event of interest is the termination of the trading partner relationship. The hazards model is one of a series of methods to estimate hazard rates but is often preferred by researchers because it efficiently estimates the model even in studies with large numbers of observations and censored observations (Ferrier et al. 1999; Tuma and Hannan 1984). As discussed previously, observations are considered censored when the event of interest occurred either before the study began or after the study period ended. Censoring of observations can be a concern in the study of supply chain relationships where many of the trading partner relationships do not experience termination during the study period. The hazards model uses each trading partner-month observation since the trading partner relationship was at risk during each month of the study regardless of whether they experienced the hazard event or not.
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Two critical inputs to the hazards model are whether the relationship terminated and how long the relationship lasted. The dependent variable of the model is the dichotomous measure of relationship termination described earlier for the logistic regression model. TERMINATION is a binary measure of relationship termination and is set to ‘1’ if the relationship terminated during the month. DURATION is a count of the number of months that the relationship existed since the beginning of the study period. The variable DURATION is entered as the time function in the model (?). The remaining explanatory variables are as described previously and are summarized in Table 4.2. Table 4.2 Definition of Cox Proportional Hazards Model Variables Variable Definition Dependent Variable 1 if terminated, 0 otherwise TERMINATIONijt Time Function Variable Number of months the trading partner relationship has DURATIONijt existed since the beginning of the study period Independent Variables TRANSACTIONAL_VOLUMEijt Cumulative volume of transactional information exchange between a trading partner and the technology champion firm 2 TRANSACTIONAL_VOLUME ijt The mathematical square of the cumulative volume of transactional information exchange between a trading partner and the technology champion firm Cumulative volume of enhanced information ENHANCED_VOLUMEijt exchange between a trading partner and the technology champion firm 2 The mathematical square of the volume of enhanced ENHANCED_VOLUME ijt information exchange between a trading partner and the technology champion firm The ratio of cumulative enhanced information RATIOijt exchange volume to the cumulative transactional information exchange volume between a trading partner and the technology champion firm A series of binary dummy variables to identify the FIRMi technology champion firm
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The only assumption of the Cox model is that the hazard is proportional in that for any two individual observations, at any point in time the ratio of their hazard is a constant. Specifically, for any point in time (t), hi(t)/ hj(t) = c where i and j refer to distinct trading partners and c is the constant which depends on the explanatory variables but not on time. Proportionality is tested using the scaled Schoenfeld residuals of the
covariates on functions of time and a visual observation of the plotted residuals. A nonzero slope is an indication of a violation of the proportionality assumption (Allison 1985).
(log)TERMINATIONijt = ?(t-1) + ?1TRANSACTION_VOLUME(t-1) +
2 ?2TRANSACTION_VOLUME(t-1) + ?3ENHANCED_VOLUME(t-1) + 2 ?4ENHANCED_VOLUME(t-1) + ?5RATIO(t-1) +?? i FIRMi + ? i=0 n
[ 4.3]
where i is the technology champion firm, j is the trading partner, and t is the month
4.4 Results This study seeks to expand the understanding of how relationship termination varies according to information exchange practices. Extending the strategic management literature’s use of relationship survival as a key measure of relational performance (Dyer and Singh 1998), the information exchange matrix (Figure 2.2) provides a framework to associate information exchange practices with trading partner relationship performance in the form of relationship termination. 4.4.1 Descriptive Statistics The dataset used for this analysis includes observations of the information exchange transactions for thirty-nine technology champion firms and their electronicallymediated trading partner relationships. These data capture EDI-based electronic
79
document exchanges over a twenty-four month period beginning January 2004 and ending December 2005. Descriptive statistics on the overall dataset are included in Table 4.3. Table 4.3 Descriptive Statistics
Obs TERMINATION 255,076 TRANSACTIONAL_VOLUME 255,076 TRANSACTIONAL_VOLUME^2 255,076 ENHANCED_VOLUME 255,076 ENHANCED_VOLUME^2 255,076 ENHANCED-TRANSACTIONAL_RATIO 255,076 Variable Mean Std. Dev. 0.0252 0.1567 0.0107 0.0802 0.0065 0.4903 0.0033 0.0315 0.0010 0.0536 12.9318 942.7700 Min 0 0 0 0 0 0 Max 1.00 10.56 111.54 3.16 9.98 283,562.50
Monthly information exchange within each technology champion–trading partner dyad results in a large number of observations on which to test the hypotheses. These 237,021 observations are used in both the logistic and Cox Proportional Hazard models. The measures of information exchange described in Chapter 2 and developed into specific measures for the termination models. Transactional volume is measured cumulatively up to the observation month and varies between zero and eleven million document exchanges. The mean of cumulative transactional volume is approximately eleven thousand with a relatively large standard deviation of eighty-three thousand. This indicates that the dyads included in this dataset vary greatly in the amount of transactional information exchanged. Similarly, the measure of cumulative enhanced information exchange varies across the observed dyads. The cumulative volume of enhanced information exchange ranges from zero to approximately three million documents exchanged. The mean is approximately three thousand which is relatively low due to the fact that many dyads
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have an observed value of zero enhanced documents exchanged indicating that they do not exchange information beyond the transactional information. The measure of the ratio of enhanced to transactional information exchange is affected by the variation in both the cumulative transactional and enhanced measures. The mean ratio is approximately thirteen with many dyads reporting a ratio of zero resulting from not exchanging any enhanced information documents. The mean of thirteen indicates an average ratio of thirteen enhanced documents to every transactional document. The ratio measure ranges from zero to 283,563 indicating great variation in the ratio across the dyads. The pairwise correlations provided in Table 4.4 indicate a negative and statistically significant relationship between both the transactional volume and enhanced volume measures with relationship termination. In interpreting the relationship, it is important to remember that termination is coded as ‘1’ if the relationship is terminated and ‘0’ if the relationship is not terminated. These negative correlations indicate that at higher levels of exchange volume, the trading partner relationships are not terminated.
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Table 4.4 Pairwise Correlations
TRANSACTIONAL TRANSACTIONAL _VOLUME _VOLUME^2 TRANSACTIONAL_VOLUME ENHANCED _VOLUME ENHANCEDENHANCED TRANSACTIONAL_ _VOLUME^2 RATIO
1 255,076 0.7651 0.0000 255,076 0.1854 0.0000 255,076 0.0522 0.0000 255,076 -0.0018 0.7087 255,076
TRANSACTIONAL_VOLUME^2
1 255,076 0.0159 0.0000 255,076 0.0053 0.0080 255,076 -0.0002 0.9268 255,076
ENHANCED VOLUME
1 255,076 0.8162 0.0000 255,076 0.0867 0.0000 255,076
ENHANCED_VOLUME^2
1 255,076 0.0346 0.0000 255,076
ENHANCED-TRANSACTIONAL_RATIO
1 255,076
Statistically significant correlations are highlighted in bold and italics
As expected, the linear and non-linear (squared) measures show a positive and statistically significant correlation for both the transactional and enhanced measures. This is not unusual in models that use squared measures for the testing of non-linear relationship. A positive and statistically significant relationship also is identified between the ratio measure (ENHANCED-TRANSACTIONAL_RATIO) and both the linear enhanced information exchange measure (ENHANCED_INFORMATION_VOLUME) and the non-linear enhanced information exchange measure (ENHANCED_INFORMATION_VOLUME^2). The pairwise correlations provide further support for the distinct nature of the transactional and enhanced dimensions of information exchange. Transactional information exchange volume (TRANSACTIONAL_VOLUME) and enhanced information exchange volume (ENHANCED_VOLUME) are positively and statisticallysignificantly correlated at 18%. This positive relationship suggests that the levels of each
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dimension move in the same direction. However, their relatively low level of correlation suggests that they can distinctively measure two unique types of information exchange. 4.4.2 Logistic Regression Results Results for the explanatory variables of the logistic regression model are provided in Table 4.5. The model fit is statistically significant based on the Likelihood Ratio (LR) Chi-Square test statistic. Although logistic regression does not have an equivalent to the R-squared that is found in ordinary least squares (OLS) regression, the McFadden pseudo R-squared is a provided as an indicator of the explanatory power of the model. The pseudo R-square reported for this model is 10.01%. Full results including the coefficient estimates of the control variables are included in Appendix D. The estimated coefficients of the transactional information exchange and transactional information exchange squared variables are used to test Hypothesis 1. A negative estimated coefficient for the transactional information exchange volume variable (TRANSACTIONAL_VOLUME) indicates a decrease in the likelihood of termination with each increase in transactional information exchange volume. A negative coefficient for the squared transactional information exchange variable (TRANSACTIONAL_VOLUME^2) indicates the non-linear u-shaped relationship that is hypothesized. The negative coefficient for the non-linear term specifies that at higher levels of the transactional exchange volume, the likelihood of termination actually increases. The results support Hypothesis 1.
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Table 4.5 Logistic Regression Results
TERMINATION TRANSACTIONAL_VOLUME TRANSACTIONAL_VOLUME^2 ENHANCED_VOLUME ENHANCED_VOLUME^2 ENHANCED-TRANSACTIONAL_RATIO Coef. (Std Err) -6.2809 (0.7646) 0.6102 (0.0764) -10.2760 (2.1512) 3.2679 (0.8111) 4.41E-06 (0.00002) P>| t | 0.000 0.000 0.000 0.000 0.824 sig ** ** ** ** ns H3: Not Supported H2: Supported Hypotheses Testing H1: Supported
? ?? (technology champion dummy variables)
??
t
i
t
(month dummy variables)
. .
-3.9163 (0.1402) 255,076 5,684.44 0.0000 0.0948 -27,150.29
. .
0.000
. .
**
constant observations LR chi2 Prob > chi2 Pseudo R2 Log Likelihood
**| t | 0.000 0.000 0.000 0.000 0.885 sig ** ** ** ** ns
??
i
?
(technology champion dummy variables)
??t (month dummy variables)
t
. .
-2.0442 (0.1416) 51,420 3,169.00 0.0000 0.2468 -4,851.56
. .
0.000
. .
**
. .
-2.9665 (0.2050) 43,321 902.26 0.0000 0.1265 -3,114.46
. .
0.000
. .
**
. .
-2.2066 (0.1547) 160,335 3,523.42 0.0000 0.0886 -18,130.01
. .
0.000
. .
**
constant observations LR chi2 Prob > chi2 Pseudo R2 Log Likelihood
** chi2 Log Likelihood
**chi2 Log Likelihood Z -3.25 3.45 -2.63 1.64 -0.71 18.88 --24.95 -3.00 13.54 13.42 -3.18 0.70 --2.07 ---0.32 31.09 7.69 12.33 9.02 ---0.55 15.42 --4.90 --1.34 -1.11 16.51 22.20 --6.53 20.06 P>|z| 0.0010 0.0010 0.0090 0.1010 0.4770 0.0000 --0.0000 -0.0030 0.0000 0.0000 -0.0010 0.4850 --0.0390 --0.7460 0.0000 0.0000 0.0000 0.0000 ---0.5820 0.0000 -0.0000 --0.1820 0.2690 0.0000 0.0000 --0.0000 0.0000 sig * * * ns ns ** --** -* ** ** -* ns --* --ns ** ** ** ** ---ns ** -** --ns ns ** ** --** **
51,420 2,636 0.0000 -11,757.96
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Table E.3 Stratified Hazard Model Results: Wholesalers
TERMINATION Coef. Std. Err TRANSACTIONAL_VOLUME -34.7517 5.4582 TRANSACTIONAL_VOLUME^2 3.2472 0.5900 ENHANCED_VOLUME -8.7122 5.2078 ENHANCED_VOLUME^2 2.6181 1.8341 ENHANCED_TRANSACTIONAL RATIO 2.33E-05 1.49E-05 Control Variables FIRM_DUMMY1 --FIRM_DUMMY2 -0.9459 0.1547 FIRM_DUMMY3 0.0500 0.1269 FIRM_DUMMY4 --FIRM_DUMMY5 --FIRM_DUMMY6 --FIRM_DUMMY7 --FIRM_DUMMY8 --FIRM_DUMMY9 --FIRM_DUMMY10 --FIRM_DUMMY11 --FIRM_DUMMY12 --FIRM_DUMMY13 --FIRM_DUMMY14 --FIRM_DUMMY15 --FIRM_DUMMY16 --FIRM_DUMMY17 --FIRM_DUMMY18 --FIRM_DUMMY19 --FIRM_DUMMY20 --FIRM_DUMMY21 --FIRM_DUMMY22 --FIRM_DUMMY23 --FIRM_DUMMY24 --FIRM_DUMMY25 --FIRM_DUMMY26 --FIRM_DUMMY27 --FIRM_DUMMY28 -1.6967 0.1517 FIRM_DUMMY29 --FIRM_DUMMY30 --FIRM_DUMMY31 --FIRM_DUMMY32 --FIRM_DUMMY33 --FIRM_DUMMY34 --FIRM_DUMMY35 -0.5020 0.1070 FIRM_DUMMY36 --FIRM_DUMMY37 --FIRM_DUMMY38 --Model Statistics Observations LR chi2 Prob>chi2 Log Likelihood Z -6.37 5.50 -1.67 1.43 1.56 --6.11 0.39 -------------------------11.18 -------4.69 ---P>|z| 0.0000 0.0000 0.0940 0.1530 0.1190 -0.0000 0.6930 ------------------------0.0000 ------0.0000 ---sig ** ** + ns ns -** ns ------------------------** ------** ----
43,321 329 0.0000 -6,419.96
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Table E.4 Stratified Hazard Model Results: Retailers
TERMINATION Coef. Std. Err TRANSACTIONAL_VOLUME -15.0735 1.4312 TRANSACTIONAL_VOLUME^2 4.5713 0.4082 ENHANCED_VOLUME -51.6863 7.4789 ENHANCED_VOLUME^2 30.8093 4.1358 ENHANCED_TRANSACTIONAL RATIO 1.67E-04 0.000321 Control Variables FIRM_DUMMY1 --FIRM_DUMMY2 --FIRM_DUMMY3 --FIRM_DUMMY4 --FIRM_DUMMY5 -0.7456 0.1563 FIRM_DUMMY6 --FIRM_DUMMY7 --FIRM_DUMMY8 --FIRM_DUMMY9 -1.1698 0.1604 FIRM_DUMMY10 --FIRM_DUMMY11 --FIRM_DUMMY12 2.5306 0.1520 FIRM_DUMMY13 -0.4184 0.1386 FIRM_DUMMY14 --FIRM_DUMMY15 -0.5060 0.1697 FIRM_DUMMY16 --FIRM_DUMMY17 --FIRM_DUMMY18 --FIRM_DUMMY19 --FIRM_DUMMY20 --FIRM_DUMMY21 --FIRM_DUMMY22 --FIRM_DUMMY23 -0.1775 0.2989 FIRM_DUMMY24 -0.3869 0.1358 FIRM_DUMMY25 --FIRM_DUMMY26 --FIRM_DUMMY27 1.2830 0.1475 FIRM_DUMMY28 --FIRM_DUMMY29 --FIRM_DUMMY30 -1.4972 0.1484 FIRM_DUMMY31 --FIRM_DUMMY32 --FIRM_DUMMY33 --FIRM_DUMMY34 --FIRM_DUMMY35 -1.1059 0.1413 FIRM_DUMMY36 --FIRM_DUMMY37 --FIRM_DUMMY38 --Model Statistics Observations LR chi2 Prob>chi2 Log Likelihood Z -10.53 11.20 -6.91 7.45 0.52 -----4.7700 ----7.2900 --16.6500 -3.0200 --2.9800 --------0.5900 -2.8500 --8.7000 ---10.0900 -----7.82 ---P>|z| 0.0000 0.0000 0.0000 0.0000 0.6030 ----0.0000 ---0.0000 --0.0000 0.0030 -0.0030 -------0.5530 0.0040 --0.0000 --0.0000 ----0.0000 ---sig ** ** ** ** ns ----** -----** * -* -------ns * --** --** ----** ----
160,335 2,549 0.0000 45,616.05
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APPENDIX F Calculating Sales Surprise
This study evaluates two standard forecasting methods based on historical data to calculate the sales surprise. Although alternative forecasting methods are available, the two methods selected provide robust forecasts for evaluation and are commonly used in practice. A moving average forecast uses actual data values from prior periods to generate a forecast. A moving average forecast is generated using the following equation:
n
F t = MAn =
?
?A
i =1
t ?i
n
=
At ? n + ..... + At ? 2 + At ?1 n
[ F.1]
?
Where F t is the forecast for time period t , MAn identifies the number of periods included in the moving average window, At-1 is the actual value in period t-1, and n is the number of data points in the moving average (Stevenson 2007). The moving average forecast can include as many data points as desired; however, since it is averaging or smoothing the historic values increasing the number of data points decreases the responsiveness of the forecast to recent changes. Similarly, decreasing the number of data points increases the sensitivity of the forecast to recent changes. Moving average forecasts are popular in practice but require the decision maker to determine the appropriate number of periods to include in the forecast. For the purposes of this study, moving average forecasts were calculated for all firms based on the inclusion of two, four, and eight periods.
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Exponential smoothing (also called Single Smoothing or noted “SS”) is a more sophisticated averaging forecast method which can incorporate large amounts of historic data points while placing emphasis on the more recent events. The exponential smoothing forecast adds a percentage of the difference between the prior period forecast and the actual value observed during the prior period. An exponential smoothing forecast is generated using the following equation:
? ? ? ? ? F t = F t ?1 +? ? A t-1 ? F t ?1 ? ? ?
[ F.2]
Where Ft is the forecast for period t, F t ?1 is the forecast for the prior period, ? is a smoothing constant, and At-1 is the actual value observed during the prior period (Stevenson 2007; Tersine 1993). This forecast method is generated over time, such that the forecasts and forecasting error in each of the previous periods are incorporated into the current forecast, the model is more generally stated as:
? t ?
?
F t = ? ? (1 ? ? )
k =1
k ?1
At ? k + (1 ? ? ) F 0
t
[ F.3]
?
?
Where F t is the forecasted demand level for period t , F 0 is the forecasted demand level for the initial period, and At ?1 is the actual demand for period t ? 1 . The smoothing constant represented by ? incorporates a percentage of the previous forecast error into the new forecast. The responsiveness of the forecast is then driven by the magnitude of the smoothing constant. The closer the smoothing constant is to zero, the
164
slower the forecast will adjust for forecasting error in the prior period. A smoothing constant closer to 1.00 will be very responsive to recent forecast error. In practice, the smoothing constant is selected based on judgment of the decision maker or trial and error. Smoothing constants commonly range from 0.05 to 0.50 depending on the underlying demand behavior and business responsiveness needs. For the purposes of this study, smoothing constants of 0.05, 0.25, and 0.50 were used to estimate forecasts for all firms. Results of these forecasts will be discussed at the end of this section. Since the exponential smoothing forecast uses the historic period error to calculate the current forecast, a starting forecast must be identified. An average of the first several periods is often used to generate the starting forecast (Stevenson 2007). For the purposes of this study, a moving average forecast based on the four quarters of 2003 was used to generate a starting forecast for first quarter of 2004. The exponential smoothing forecast was then run starting in first quarter (Q1 2004) through the next seven periods to the end of the study (Q4 2005). The resulting six forecasts (MA2, MA4, MA8, SS?=0.05, SS?=0.25, SS?=0.50) are then compared to identify the best fit for estimating sales. A key measure used to select forecast methods is forecast accuracy (Stevenson 2007). Forecast accuracy is operationalized as the difference between the actual observed value during a period and the forecasted value for the period (error).
?
et = At - F t
[ F.4]
165
Where et is the forecasting error for period t based on the difference between the actual
?
observed value for the period (At) and the forecast for the period ( F t ). A larger value of error indicates greater inaccuracy in the forecast. Positive error then is the result of a forecast that was too low, negative error is the result of a forecast that was too high relative to the actual observed value for the period. Forecast error is aggregated using one of two standard measures. The two measures are; the mean squared error (MSE) and mean absolute deviation (MAD).
MAD =
?e
n
[ F.5]
[ F.6]
MSE =
?e
n
2
The MAD is the average absolute difference between the forecast and observed values for each period-firm combination. By using the absolute value of the difference, the canceling effects of negative and positive error are eliminated. Similarly, the MSE method eliminates the canceling effects of positive and negative error by squaring the period-firm error. The difference in the two methods is that the MAD treats all error equally where the MSE weights the errors based on their squared values. In either case, lower aggregate results identify less error in the forecasting method (Stevenson 2007). As shown in Table F.1, the moving average methods produce lower error than the exponential smoothing methods. The lowest overall error is produced by the moving average 4-period and moving average 4-period specifications. This holds for both the mean squared error and mean absolute deviation methods of calculating forecast error.
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TABLE F.1 Forecast Accuracy by Forecast Method
Forecast Method Moving Average - 2 Periods Moving Average - 4 Periods Moving Average - 8 Periods Exponential Smoothing ?=0.05 Exponential Smoothing ?=0.25 Exponential Smoothing ?=0.50 Mean Squared Error 262,994,910 211,310,507 351,056,626 648,074,453 1,436,286,821 4,312,283,372 Mean Absolute Deviation 170,127 156,971 210,372 274,312 371,650 547,632
For the purposes of this study, moving average 4-period is used to calculate the firmperiod forecasted sales, which is used in deriving the per-period sales surprise.
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APPENDIX G Alternative Dependent Variable Results
The following tables provide model fit statistics and coefficient estimates for two alternative model specifications. Table G.1 provides output for the alternative Net Income dependent variable. Table G.2 provides output for the alternative Receivables Turnover dependent variable. Table G.3 includes the pairwise correlations for the primary dependent variable (INVENTORY_TURNOVER) and both alternative dependent variables.
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TABLE G.1 Alternative Dependent Variable: Net Income
n groups R-square within between overall 170 39 0.2036 0.5991 0.4371 3.12 0.0014
F Prob>F
(log)NET_INCOME
Coef. (Std Err) 0.5085 (0.2819) -0.2039 (0.1342) 0.1429 (0.3047) -0.0917 (0.0949) 25.8130 (10.5323) -2.3305 (1.1113) 2.7638 (0.6295) 0.0004 (0.0976) 0.5500 (0.2019) 0.2888 (0.1767) 0.3513 (0.1760)
P>| t |
sig
Explanatory Variables (log)CLOSE (log)ASYMMETRY (log)CONCENTRATION (log)CHURN_RATE constant Control Variables (log)FIRMSIZE (log)SALES_SURPRISE (log)NET_INCOME_LAG SEASON1DUMMY SEASON2DUMMY SEASON3DUMMY
**