Data Exchange in Interorganizational Relationships: Review Through ...

Data Exchange in Interorganizational Relationships: Review Through Multiple Conceptual Lenses1 Wafa Elgarah Al Akhawayn University Natalia Falaleeva Loyola College in Maryland Carol S. Saunders University of Central Florida Virginia Ilie University of Central Florida J.T. Shim University of Central Florida James F. Courtney University of Central Florida

Acknowledgement We would like to thank Diana Boyette for her assistance with coding articles and statistical analysis. 1

An earlier version of this paper was presented at AMCIS 2002 and published in the conference proceedings.

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Abstract This paper explores the theoretical underpinning of data exchange research, specifically Electronic Data Interchange (EDI), over the period from 1993 to 2002. It identifies the underlying research paradigms applied to examination of data exchange, and determines conceptual and theoretical gaps in previous research on data exchange in interorganizational relationships (IORs). Sixty-eight articles are analyzed. Results suggest a predominant concern with the outcomes realized with EDI adoption and use. There appears to be a shift in emphasis from dyadic relationships to networks. As a majority of the studies are of the survey nature using single cross-sectional snapshots, the emphasis appears to be on short-term outcomes of EDI-enabled relationships. Implications of the findings are discussed. ACM Categories: J.1 Keywords: Data exchange, EDI, interorganizational relationships, research paradigms, review

Introduction It has been widely acknowledged that interorganizational information systems and communication technologies reduce coordination costs and improve communication between business partners. These technologies provide the infrastructure for interorganizational relationships (IORs). Perhaps the most common form of technology to support data exchange between business partners is Electronic Data Interchange. EDI is “the movement of business documents electronically between or within firms (including their agents or intermediaries) in a structured, machineretrievable format that permits data to be transferred, without re-keying, from a business application in one location to a business application in another location” (Hansen & Hill, 1989, p. 405). Advantages of electronic data exchange include improved procurement processes, increased customer satisfaction, transaction and production cost reduction, improved information quality and enhanced ties with suppliers and customers (Bergeron & Raymond, 1997; Rassameethes et al., 2000; Sriram et al., 2000). Currently, many trading partners are turning to web-based approaches, including webbased data exchange and procurement. This migration reflects a movement away from a dyadic approach in data exchange, to more flexible costeffective approaches that capitalize on many-to-many relationships. Many firms have adopted e-business

The DATA BASE for Advances in Information Systems - Winter 2005 (Vol. 36, No. 1)

models to improve their collaborative capabilities (Segars & Chatterjee, 2003). A multitude of studies over an extended period of time have examined the adoption of electronic data exchange in organizations and identified its benefits and drawbacks. They also have explored organizational and technological factors promoting data exchange adoption and use. However, the data exchange environment is changing with organizations becoming more distributed and global (Belanger & Collins, 1998) and moving toward Internet-based technologies to exchange information. Those firms that have substantial investments in electronic data exchange are seeking ways to leverage their investments. Many firms are using the web to investigate alternative suppliers or buyers, while maintaining existing data exchange relationships (Carbone, 1999). Others have moved away entirely from traditional VAN-mediated, proprietary EDI to use web-based data exchange (Jones & Beatty, 2001). This study examines the evolution of data exchange from a traditional dyadic EDI approach towards a web-based integrated data exchange environment within a networked economy. In order to achieve this, we conduct a comprehensive multiparadigm review of the existing literature on data exchange to identify the underlying research paradigms applied to examination of electronic data exchange. Our comprehensive review also isolates factors and motives that have been reported to lead to successful data exchange partnerships. Furthermore, we investigate conceptual and theoretical gaps in previous research on data exchange in IORs over the last 10 years, and suggest possible developments in the future of the web-based data exchange. By understanding these gaps, Information Systems (IS) academics can better direct their research efforts to fill in the gaps and learn from previous research streams. As companies increasingly participate in technology-enabled relationships with multiple vendors, identifying grounds for their success, especially over an extended period of time, may be valuable. EDI research, in particular, offers researchers the opportunity to view the maturing exploration of technology-enabled relationship building between buyers and suppliers over nearly two decades. Reviews create “a firm foundation for advancing knowledge” (Webster & Watson, 2002) by providing the research community with a comprehensive metaview of the collection of research, and also setting the theoretical ground for future research in a certain area. Many reviews on different topics such as knowledge management (Alavi & Leidner, 2001),

communication (Te'eni, 2001), Group Support Systems (Fjermestad & Hiltz, 1998-99) and power (Jasperson et al., 2002) have already facilitated theory development and contributed to the progress of the IS field. Thus, a comprehensive review of data exchange can inform future studies when it comes to the new forms of data exchange such as XML-based data exchange or web-based data exchange. While web-based data exchange involves more partners than traditional data exchange, the underlying purpose remains the same, that is, to assist business partners with their transactions and the exchange of information and documents electronically.

Methodology We employed a multiparadigm review to study data exchange in interorganizational relationships. A multiparadigm review examines existing literature to expose researchers’ underlying and often taken-forgranted assumptions about the phenomenon being studied (Lewis & Kelemen, 2002). By making these assumptions explicit, multiparadigm reviews raise paradigm consciousness by distinguishing the selective insights and blinders of the alternative lenses. By highlighting paradigm diversity, more theoretical choices are open to researchers. Our search included all refereed articles that we could find from 1993 through 2002 on the topic of electronic data exchange dealing with some aspect of interorganizational relationships. The articles were identified by searching all peer-reviewed articles in the Business Source Premier database provided by 2 EBSCO using the search terms “EDI,” “Electronic Data Interchange,” “interorganizational relationship,” or “interorganizational systems.” We reviewed each retrieved article’s title, abstract, and key words to ensure that it addressed data exchange in the context of interorganizational relationships. This eliminated articles on data exchange that focused on security, standards or other such issues not directly related to interorganizational relationships. Our final sample consisted of 68 articles from 34 journals. The listing of journals and their associated fields is displayed in Table 1. The sample representation can also be considered very broad in the sense that the articles include studies of data exchange adoption, use, and implementation based on data from a wide range of countries: Australia, Canada, China, Denmark, Finland, Hong Kong, Ireland, Japan, Singapore, the United Kingdom, and the United States. 2

We also searched ABI Inform and obtained an equally long list of potential articles with some overlap from the list obtained from EBSCO. We decided to use the EBSCO list as it was more inclusive and because of the range of journals covered.


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Discipline MIS (41 articles)

Journal Name (Number of Articles Published in Journal) 1.

Computers & Security (2)

2.

Decision Sciences (1)

3.

European Journal of Information Systems (3)

4.

Information & Management (4)

5.

Information Services & Use (1)

6.

INFOR (1)

7.

The Information Society (1)

8.

Information Systems Management (3)

9.

Information Systems Research (5)

10.

International Journal of Electronic Commerce (1)

11.

International Journal of Information Management (2)

12.

International Journal of Technology Management (2)

13.

Journal of Information Systems (1)

14.

Journal of Information Technology (2)

15.

Journal of Management Information Systems (6)

16.

Journal of Organizational Computing and Electronic Commerce (1)

17.

MIS Quarterly (4)

18.

Technovation (1)

Logistics/Transportation/

1.

Forest Product Journal (1)

Marketing (16 articles)

2.

International Journal of Physical Distribution & Logistics Management (3)

3.

International Journal of Purchasing and Materials Management (1)

4.

International Journal of Retail & Distribution Management (1)

5.

Journal of Business & Industrial Marketing (4)

6.

Journal of Business Logistics (5)

7.

Logistics and Transportation Review (1)

Production/Operations

1.

Industrial Management (1)

Management/Management

2.

International Journal of Operations & Production Management (1)

Science (7 articles)

3.

International Journal of Production Economics (1)

4.

Journal of Operations Management (1)

5.

Management Science (2)

6.

Production and Operations Management (1)

Management/General

1.

Academy of Management Executive (1)

Management (4 articles)

2.

Journal of Small Business Management (1)

3.

Organization Science (2)

Table 1. List of journals that published analyzed articles Multiple Paradigms To explore the theoretical underpinnings of data exchange research, we chose four paradigmatic lenses that have been frequently used in the literature to understand the relationships between trading partners. These lenses were causal agency (Markus & Robey, 1988), transactions cost economics

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(Williamson, 1991), IOR motives (Oliver, 1990), and IOR typology (Hall, 1999). Analyzing studies using different paradigmatic lenses allowed us to identify conceptual and/or methodological gaps that have been overlooked by researchers and that have strong potential for future research. Figure 1 summarizes the multiple lenses that we used to analyze the articles.


Figure 1. Multiple Paradigm Lenses. Causal Agency. To understand the underlying causal assumptions made by researchers in studying data exchange partnerships, we adopted the three causal structures (i.e., technological imperative, organizational imperative and emergent perspective) proposed by Markus and Robey (1988). We chose this set of lenses because it has been widely cited (i.e., 168 times in the IS literature according to the Social Science Citation Index) and used in previous MIS research (George & King, 1991; Jasperson et al., 2002; Orlikowski, 1992; Pinnsoneault & Kraemer, 1993). The lenses refer to the beliefs about the nature of causality between power and information technology use. Three perspectives underlie causal agency (Markus & Robey, 1988). The technological imperative posits that change is caused by external forces (i.e., technology). Technology determines or constrains the behavior of both individuals and organizations. Technology is seen as the primary driver of organizational change. In contrast to the technological imperative, the organizational imperative posits that people act purposefully to accomplish intended objectives, therefore determining organizational change. Management and human actors have more control over changes that take place. Technology is merely a tool to achieve the change that they desire. The emergent perspective on the other hand, views change as emerging from complex interactions between technology and its organizational users over time. Transaction Cost Economics. The next set of lenses that we selected for our analysis is based on transaction cost economics (Williamson, 1991). Williamson described three forms of organizational governance: hierarchy, hybrid, and market. In terms

of governance structures, a hierarchy is based on administrative controls. Managerial decisions, and not the interaction of market forces, determine the adoption of data exchange. When a single supplier serves one or more buyers as a sole provider of a good or service, the relationship between the supplier and each buyer is primarily hierarchical, since the buyers are procuring their supplies from a single predetermined supplier rather than choosing from a number of suppliers. The relationship between a single buyer and multiple suppliers serving only that buyer is governed by market forces since the buyer is choosing among a number of possible suppliers (Malone et al., 1987). In between these two types of governance structures is a hybrid, which displays both administrative controls and the adaptation and use of contract law. In hybrid governance, unlike markets, the identity of trading partners is important. We chose this lens because transaction cost economics is frequently used in data exchange studies (Humphreys et al., 2001). Additionally, this paradigm lens allows us to identify prominent forms of governance in data exchange partnerships over a decade. Interorganizational Relationship Motives. EDI is a technology that facilitates data exchange in interorganizational relationships. There are a number of strategic reasons that motivate firms to form interorganizational relationships. To examine the motives for adopting data exchange, we chose to derive the third set of lenses from Oliver’s (Oliver, 1990) motives for interorganizational relationships. Oliver presented six possible motives for organizations to engage in interorganizational relationships: necessity, asymmetry, reciprocity, efficiency, stability, and legitimacy (Table 2). Since firms often have multiple reasons for turning to data exchange, all motives that were mentioned in an article were coded. Interorganizational Relationships Typology. The last set of lenses that we adopted in our analysis is the IOR typology presented by Hall (1999). We chose this paradigm lens to identify the types of IORs most studied in data exchange research. Hall (1999) identified three types of IORs: dyadic (pairwise) relationships, interorganizational sets, and interorganizational networks. A dyad displays a relationship between just two organizations; a set places emphasis on a focal agency and all of its dyadic relationships with other organizations; and, a network consist of multiple organizations linked by a specified type of relation to achieve certain goals or resolve specific problems.


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Necessity Asymmetry Reciprocity Efficiency Stability Legitimacy

IORs are established to meet legal or regulatory requirements. IORs are established in response to power or control of another organization. IORs are based on cooperation, collaboration and coordination among organizations IORs are prompted to improve the internal input/output ratio of an organization and internal efficiency. IORs formation is an adaptive response to environmental uncertainty (generated by resource scarcity or lack of perfect knowledge). IORs are established to appear in agreement with the prevailing norms, rules or expectations of external constituents and/or to improve the image, reputation, prestige. Table 2. IOR motives definitions – source: Oliver, 1990

Other Article Characteristics In addition to the paradigmatic lenses discussed above, we analyzed the articles in terms of epistemology, research approach, time span of data collection, and topic area. Identifying the underlying epistemologies of these studies helped us determine the most dominant epistemology in data exchange partnership studies. We adapted the classification of research epistemologies used by Orlikowski and Baroudi (1991) which consists of positivist, interpretive, and critical studies. They distinguished within the positivist studies between theoretically grounded studies, where researchers were working within a theoretical tradition, and descriptive studies, where researchers "attempted no theoretical grounding or interpretation of the phenomena; rather they presented what they believed to be straightforward 'objective,' 'factual,' accounts of events to illustrate some issue of interest to the information systems community" (Orlikowski & Baroudi, 1991). Additionally, Orlikowski and Baroudi (1991) reported that the most dominant research approach reported in past IS research is surveys, hence we considered research approach, which is the research methodology adopted by the researcher to determine if it is the case in EDI research. We also analyzed the articles by time span of data collection. We used the four categories identified by Orlikowski and Baroudi (1991) which included one-snapshot cross-sectional, multiple snapshots cross-sectional, longitudinal, and process traces. Finally, we classified the articles by topic area. The identified categories of topic areas are presented in the discussion section. Coding Procedure Using the above sets of conceptual lenses, we generated and iteratively refined the coding schema. To ensure the appropriateness of our categories, all seven members of the research team coded five

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articles. Discussions about the rationale for our coding led to the refined coding schema applied in this study. Based on the coding schema, a coding sheet was developed (Appendix A). This coding sheet was used to classify data extracted from the articles. All coders used the same coding sheet to assure uniformity and consistency. All researchers coded articles, with two coders per article. Primary and secondary coders were assigned to each article. To decrease the potential of systematic biases arising from specific pairings of coders, they were randomly assigned to each article. Each coder independently coded every assigned article. Differences were resolved between the two coders, and the resolved coding for each article was entered into a web-enabled database that facilitated data sharing, analysis, and manipulation. If the two coders could not resolve disagreements for a particular article, the issues were discussed among additional or all group members. The intercoder agreement was high, 80%.

Results – Theoretical Underpinnings of Data Exchange A summary of each article’s causal agency, TCE category, IOR typology and IOR motives, in addition to epistemology, research approaches, and time span is provided in Appendix B. Underlying View of Causal Agency As we discussed in the section on theoretical lenses, causal agency refers to the analyst’s beliefs about the identity of the causal agent: whether external forces cause change (technological imperative), whether people act purposefully to accomplish intended objectives (organizational imperative), or whether change emerges from the interaction of people and events (emergent perspective) (Markus & Robey, 1988).


It was often very difficult to ascertain the author’s view of causal agency in the analyzed studies since (1) they usually did not explicitly state their view, and (2) they often appeared to be guided by multiple views of causal agency. Thus, we coded an approximate percentage to represent the author’s reliance on the technological imperative, organizational imperative, or emergent perspective. A very large percentage of articles (73%) adopted, at least in part, the technological imperative perspective that suggested that data exchange was the principal driver in the changes uncovered in the studies or in theoretical models. Of these, 17 (25%) were based purely upon the technological perspective. Eighteen articles (26%) adopted a predominantly organizational imperative lens to suggest that data exchange was used by managers to achieve their own individual or group goals. Only five used an entirely emergent perspective and three used a predominantly emergent perspective, that is, about 11% adopted a completely or predominantly emergent perspective. Transaction Cost Economics (TCE) Categories TCE theory served as the stated theoretical base for 11 of the studies. TCE can be used to assess the impact of data exchange on interorganizational economic efficiency by reducing coordination costs or increasing efficiencies realized to markets. Most studies in the sample (49% or 33) were classified as markets. This is natural because data exchange enables transactions in the marketplace between business partners. Markets are ruled by contracts, and heavy emphasis is placed on selecting from multiple trading partners on the basis of commodity price. Thirty (44%) of the 68 articles were classified as hybrids. Hybrids are characterized by bilateral dependency and long-term contracts that are supported by added contractual safeguards and administrative apparatus (Williamson, 1991). Our analysis uncovered only five (7%) articles that were classified as hierarchies. Hierarchies focus more on internal aspects of the data exchange partnership. In hierarchies, there are low incentives to produce and the production happens within the firm. The internal mechanisms for cooperation and coordination are used to make adjustments in changes in demand. In further exploring the nature of the data exchange partnership in the five studies that were classified as hierarchies, three saw the development of a new organizational entity in the form of a strategic alliance (Humphreys et al., 2001), joint venture (Teo et al., 1997), or joint program

(Boudreau et al., 1998). Because they demand a focus that turns inward to the organization, only two studies that viewed the data exchange relationship as a hierarchy (Daugherty et al., 1995; Maingot & Quon, 2001) considered it to be a buyer-supplier relationship. Most studies in our sample (57 or 84%) viewed the relationship as buyer-supplier. That is, data exchange partnerships in our study mostly involve autonomous companies that use data exchange to communicate with their business partners. Interorganizational Relationship Typology When considering IOR typology, the number of studies of each type decreased with the level of complexity: 32 dyads (47%), 21 sets (31%), and 15 networks (22%). Dyads represent the buyer-supplier relationship that develops between trading partners entering data exchange. When considering long-term relationships, dyads may be especially important. Nakayama (2000) suggests that the TCE-based market view is inappropriate for viewing data exchange relationships because it fails to consider close, long-term relationships with trading partners. An alternative to the dyad is the set, as when considering a “hub and spoke.” In this alternative approach, a powerful hub, typically a larger company, seeks to establish EDI trading relations with all its trading partners to capitalize on process benefits (Brandel, 1997; Jasperson et al., 2000). As data exchange transactions tend to migrate towards more web-based exchanges, more networks may develop. Motives for Data Exchange Adoption and Use Fifty-seven percent (39 articles) of the studies in our sample emphasized the benefits of data exchange. Since there is an overriding concern in discovering the benefits of data exchange, it could be anticipated that the motives for data exchange adoption and use vary substantially over this global sample. Our analysis using the six IOR motives outlined by Oliver (1990) displays surprisingly little variance in our sample. Firms often had several reasons for adopting and using data exchange. Efficiency was mentioned as one motive for adoption or use of data exchange in ninety-seven percent of the studies. Clearly the anticipation of operational cost savings underlies almost all decisions to adopt and to continue to use EDI. Only one study highlighted legitimacy motives (Rassameethes et al., 2000). This study examined first- and second-tier suppliers in the automotive supply chain and found that while first-tier suppliers adopted EDI, they had not been able to persuade the second-tier suppliers to do so.


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IOR Types Dyads Networks Sets

IOR Motives Asymmetry

Efficiency

Legitimacy

Necessity

Reciprocity

Stability

16 2 12

30 15 20

0 0 1

2 4 3

19 6 9

2 0 4

Table 3. Paradigm Interdependencies: IOR Types and IOR Motives Necessity was infrequently discussed as a motive for adoption and use of data exchange. Ironically, though Humprheys, Lai and Sculli (2001) used Oliver’s categorization to understand the benefits of data exchange to upstream and downstream EDI trading partners, necessity alone was not included, perhaps because it was viewed as nonvoluntary. Nonvoluntary adoption and use may occur when mandated by the government or when compelled by strong norms from industry associations or other institutional forces. Networks and sets may be especially useful in enforcing norms. One of the nine studies that focused on necessity (Damsgaard & Lyytinen, 2001), looked at the role of industry associations in compelling organizations to adopt EDI. This study (Damsgaard & Lyytinen, 2001) used a set IOR typology and encountered two deficiencies of data exchange research: (1) failing to consider institutional forces behind data exchange implementations, and (2) failing to consider the international context of data exchange adoption. Asymmetry as a motive for adoption of data exchange partnerships refers to the potential of an organization to exercise power or control over another organization or its resources (Oliver, 1990). Asymmetry was cited as a motive in 30 (44%) studies. Thirteen (19%) of the studies with asymmetry as a motive explicitly discussed bargaining power of one of the trading partners as a reason to adopt electronic data exchange. Nine (13%) of them included power as one of the variables in their model. Reciprocity as a motive for the adoption of electronic data exchange assumes that interorganizational relationships occur for the purpose of pursuing mutual beneficial goals and interests (Oliver, 1990). Reciprocity was identified as a motive in 36 (53%) studies. When data exchange adoption is an adaptive response to environmental uncertainty and its purpose is predictability and dependability of the trading relationship, stability as a motive applies. In fact, stability was found in nine (13%) of the 68 studies examined. Paradigm interdependencies In attempt to better understand how the paradigm lenses relate to and inform each other, we conducted

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a series of cross-tabulations. Most cells in the analysis had counts less than five which prohibited us from conducting any Chi-square independence tests. No clear patterns emerged except when jointly considering IOR types and IOR motives. Not surprisingly, efficiency was a motive for data exchange adoption across all types of IORs (dyads, network and sets). Asymmetry on the other hand appears to be less of a motive in networks, only 2 of 15 studies on networks reported asymmetry as a motive. This might be due to the nature of networks where there is no one dominant organization and IORs are less likely to be formed in response to power and control of an organization. In contrast, asymmetrical power relationships may not only be clear but also very important in persuading lesspowerful trading partners to adopt data exchange in dyads or in hub and spoke (set) relationships. Many studies (e.g., Hart & Saunders, 1997; Hart & Saunders, 1998; Premkumar et al., 1995) have suggested that the main reason a trading partner adopts data exchange is because of the application of power in an asymmetric relationship. Further, asymmetry may be important as one industry B2B hub competes with another in the same industry (Ravichandran & Pant, 2001). Additionally, we found that reciprocity is most often cited as a motive in dyads. This result could be interpreted in two different ways. One interpretation could be that dyads are easier to study than networks or sets. Another interpretation is that in dyadic relationships, reciprocity is more of a motive because it is easier to focus on relationships with one organization and the coordination costs of managing the relationship are far less than in sets or networks. Results of the crosstabulation are shown in Table 3.

Results – Methodological Issues in Data Exchange Types of Studies In addition to viewing the articles through four different conceptual lenses, we explored the topics that were the focus of each particular article. Table 4 lists the topic areas in the sample: General Outcomes (data exchange benefits and shortcomings),


Topics EDI Typologies Strategy

Collaboration/ Coordination Outcomes Data Exchange Adoption

Power/Trust/Risk

Data Exchange Use/Diffusion/ Implementation

General Outcomes (Benefits & Shortcomings)

Studies Vlosky, R., Smith, P. and Wilson, D. (1994); Gottardi, G. and Bolisani, E. (1996); Damsgaard, J. and Truex, D. (2000); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001) Vlosky, R., Smith, P and Wilson, D. (1994); Gottardi, G. and Bolisani, E. (1996); Massetti, B. and Zmud, R. (1996); Barua, A. and Lee, B. (1997); Young, D., Carr, H. H. and Rainer, K. R. (1999); Chan, S. and Davis, T. R.V. (2000); Chatfield, A. T., and Yetton, P. (2000); Sriram, R. S., Arunachalam, V. and Ivancevich, D. M. (2000); Truman, G. (2000); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001); Lim, D. and Prashant, P. (2001) Clemons, E. and Row, M. (1993); Fulk, J. and DeSanctis, G. (1995); Premkumar, G. and Ramamurthy, K. (1995); Wang, E. and Seidmann, A. (1995); Kumar, K. and Van Dissel, H. (1996); Bensaou, M. (1997); Vijayasarathy, L. R. and Robey, D. (1997); Holmes, T. L. and Srivastana, R. (1999); Angeles, R. and Nath, R. (2000); Chatfield, A. T., and Yetton, P. (2000); Angeles, R. and Nath, R. (2001); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001); Downing, C. E. (2002) Williams, L. R. (1994), Banerjee, S. and Sriram, V. (1995); Daugherty, P., Germain, R. and Droge, C. (1995); Iacovou, C. L., Benbasat, I. and Dexter, A. S. (1995); Premkumar, G. and Ramamurthy, K. (1995); Wang, E. and Seidmann, A. (1995); Walton, L. W. and Miller, L. G. (1995); Hart, P.J. and Saunders, C.S. (1997); Premkumar, G., Ramamurthy, K. and Crum, M. (1997); Vijayasarathy, L. R. and Tyler, M. L. (1997); Peffers, K., Dos Santos, B. L. and Thurner, P. F. (1998); Chatfield, A. T., and Yetton, P. (2000); Chwelos, P., Benbasat, I. and Dexter, A. (2001); Damsgaard, J. and Lyytinen, K. (2001); Iskandar, B., Kurokawa, S. and LeBlanc, L. J. (2001) Clemons, E. and Row, M. (1993), Williams, L. R. (1994); Iacovou, C. L., Benbasat, I. and Dexter, A. S. (1995); Walton, L. W. and Miller, L. G. (1995); Kumar, K. and Van Dissel, H. (1996); Hart, P.J. and Saunders, C.S. (1997); Hart, P.J. and Saunders, C.S. (1998); Ratnasingham, P. (1998); Wilson, D. and Vlosky, R. (1998); Ramamurthy, K., Premkumar, G. and Crum, M. R. (1999); Ratnasingham, P. (1999); Riggins, F. and Mukhopadhyay, T. (1999); Nakayama, M. (2000); Sriram, R. S., Arunachalam, V. and Ivancevich, D. M. (2000); Chwelos, P., Benbasat, I. and Dexter, A. (2001); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001) Premkumar, G., Ramamurthy, K. and Nilakanta, S. (1994), Vlosky, R., Smith, P. and Wilson, D. (1994), Banerjee, S. and Sriram, V. (1995); Wang, E. and Seidmann, A. (1995); Marcussen, C. H. (1996); Massetti, B. and Zmud, R. (1996); Bergeron, F. and Raymond, L. (1997); Hart, P.J. and Saunders, C.S. (1997); Premkumar, G., Ramamurthy, K. and Crum, M. (1997); Philip, G. and Pedersen, P. (1998); Crook, C. W. and Kumar, R. L. (1998); Hart, P. and Saunders, C. (1998); Peffers, K., Dos Santos, B. L. and Thurner, P. F. (1998); Williams, L. R., Magee, G. D. and Suzuki, Y. (1998); Fearon, C. and Phillip, G. (1999); Holmes, T. L. and Srivastana, R. (1999); Lee, H., Clark, T. and Tam, K. (1999); Ramamurthy, K., Premkumar, G. and Crum, M. R. (1999); Angeles, R. and Nath, R. (2000); Damsgaard, J. and Truex, D. (2000); Dupuy, C. A. and Vlosky, R. (2000); Johnston, R.B. and Gregor, S. (2000); Nakayama, M. (2000); Rassameethes, B., Kurokawa, S. and LeBlanc, L. J. (2000); Sriram, R. S., Arunachalam, V. and Ivancevich, D. M. (2000); Truman, G. (2000); Ahmad, S. and Schroeder, R. (2001); Angeles, R. and Nath, R. (2001); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001); Iskandar, B., Kurokawa, S. and LeBlanc, L. J. (2001); Maingot, M. and Quon, T. (2001); Hill, C. and Scudder, G. D. (2002) Riggins, F., Kriebel, C. and Mukhopadhyay, T. (1994); Riggins, F. J. and Mukhopadhyay, T. (1994), Banerjee, S. and Sriram, V. (1995); Iacovou, C. L., Benbasat, I. and Dexter, A. S. (1995); Mukhopadhyay, T., Kekre, S. and Kalathur, S. (1995); Wang, E. and Seidmann, A. (1995); Mackay, D. and Rosier, M. (1996); Murphy, P. and Daley, J. (1996); Barua, A. and Lee, B. (1997); Bergeron, F. and Raymond, L. (1997); Hart, P. J. and Saunders, C. S. (1997); Teo, H. H, Tan, B. and Wei, K.K. (1997); Vijayasarathy, L. R. and Tyler, M. L. (1997); Philip, G. and Pedersen, P. (1998); Boudreau, M. C., Loch, K., Robey, D. and Straub, D. (1998); Chen, J-C. and Williams, B. C. (1998); Crook, C. W. and Kumar, R. L. (1998); Peffers, K., Dos Santos, B. L. and Thurner, P. F. (1998); Williams, L. R., Magee, G. D. and Suzuki, Y. (1998); Wilson, D. and Vlosky, R. (1998); Fearon, C. and Phillip, G. (1999); Larson, P. D. and Kulchitsky, J. D. (1999); Lee, H., Clark, T. and Tam, K. (1999); Murphy, P. and Daley, J. (1999); Ramamurthy, K., Premkumar, G. and Crum, M. R. (1999); Young, D., Carr, H. H. and Rainer, K. R. (1999); Chan, S. and Davis, T. R.V. (2000); Damsgaard, J. and Truex, D. (2000); Nakayama, M. (2000); Rassameethes, B., Kurokawa, S. and LeBlanc, L. J. (2000); Shirland, L. E. and Thompson, R. L. (2000); Sriram, R. S., Arunachalam, V. and Ivancevich, D. M. (2000); Ahmad, S. and Schroeder, R. (2001); Chwelos, P., Benbasat, I. and Dexter, A. (2001); Humphreys, P.K., Lai, M.K. and Sculli, D. (2001); Lim, D. and Prashant, P. (2001); Maingot, M. and Quon, T. (2001); Raghunathan, S. and Yeh, A. B. (2001); Downing, C. E. (2002)

Table 4. Topic areas of the data exchange sample of studies


15

Data Exchange Typology

4 11

Strategy

Topics

Collaboration/Coordination Outcomes

13

Data Exchange Adoption

16

Power/Trust/Risk

16

Data Exchange

32

Use/Diffusion/Implementation

39

General Outcomes/Benefits and Shortcomings

0

5

10

15

20

25

30

Number of Articles

35

40

45

Figure 2. Distribution of Articles by Topic

Cross-sectional Multiple Snapshot Cross-sectional Single Snapshot Longitudinal N/A Process Traces Frequency

Survey

Conceptual

0

Frequency

0

Field Experiment 0

0

0

0

45

0 3 0 3

0 1 0 1

0 0 0 0

5 16 1 68

Field Study 0

Review

Essay

0

Case Study 1

0

32

0

6

7

2 0 0 34

0 12 0 12

3 0 1 11

0 0 0 7

1

Table 5. Frequency of the various research approaches within each time span Collaboration/Coordination Outcomes, Power/ Trust/Risk, Data Exchange Adoption, Data Exchange Use/Diffusion/Implementation, Typologies, and Strategy. Thirty-nine (57%) focused on data exchange outcomes, especially economic benefits. Figure 2 shows that a much larger percent focused on implementation and use compared to adoption (38% vs. 21%, respectively). Only 15% were found to be strategy related based on an examination of the articles. A relatively smaller number of articles try to tease out specific aspects of the relationship such as improvements in collaboration, or the counterbalancing forces of power, trust and risk.

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Research Approach, Time Span and Epistemology Forty-five (or 66%) were single-snapshot crosssectional surveys, as compared to only seven (13%) that took a more long-term approach using longitudinal data gathering, multiple cross-sectional surveys or process traces. All but two (Bergeron and Raymond, 1997; Mackay & Rosier, 1996) of the 34 survey studies and all seven field studies used singlesnapshot cross-sectional data (Table 5). In contrast, the case studies used a combination of longitudinal, single-snapshot cross-sectional, multiple crosssections and process traces.


Theoretically Grounded (TG) Descriptive (D) Interpretive (I) Critical (C) Frequency

Survey

Conceptual

27

Frequency

0

Field Experiment 0

3

1

0

21

0 0 3

0 0 1

0 0 0

5 2 68

Field Study 4

Review

Essay

6

Case Study 3

0

7

5

5

0

0 0 34

0 1 12

3 0 11

2 1 7

40

Table 6. Frequency of the various research approaches within each epistemology. Positivism is the dominant epistemology in data exchange research. Forty articles (59%) were theoretically grounded while another 21 (31%) were descriptive. Only five interpretive and two critical studies were represented in this sample. All interpretive studies adopted either a case study or a field study research approach, whereas the theoretically grounded studies used a variety of approaches. The most dominant research approach among the theoretically grounded studies is surveys (67.5%). Table 6 presents the breakdown of articles by epistemology and research approach. An analysis of the theoretically grounded studies revealed that the most frequently-used theories in understanding the benefits of data exchange are diffusion of innovation (20%), organization theory (7%), and transaction cost economics (16%).

Discussion and Future Direction Data exchange research can be distinguished by its broad appeal: it is studied globally across a wide range of disciplines. However, a review of the research suggests both conceptual and methodological gaps in the research that has been performed. These are discussed next, along with some propositions that might be investigated in future studies. Conceptual Gaps Our review suggests five areas where conceptual gaps may inhibit our full understanding of the impact data exchange has on IORs: markets, networks, level of integration, IOR motive, and an international perspective. Figure 3 illustrates these conceptual gaps. Next we derive some propositions regarding conceptual gaps and the future of the data exchange in IORs research. Not all TCE categories and IOR typologies are fully explored in the studies in our sample. It is not surprising that there are relatively few data exchange studies of hierarchies. Hierarchies focus on internal

efficiencies rather than the relationships between trading partners. When studying data exchange in supply chains, the research continues to concentrate on hybrids. However, web-based relationships are better described by market governance. Web-based data exchanges are often premised on many-to-many relationships. They may offer a forum for many suppliers to reach a large number of buyers. Horizontal exchanges, auctions, and other such webbased approaches focus on price and the type of commodity-based transactions that characterize markets. This prediction is also supported by Malone et al. (1987) who posited a shift towards increased use of markets rather than hierarchies to coordinate economic activities. Proposition 1: The market governance structure will dominate the web-based data exchange in IORs research. Data exchange in interorganizational relationships is designed to support interactions across organizational boundaries. The nature of these interactions can vary from limited information exchange to highly integrated operations. For example, Reddy and Reddy (2001) suggest four stages of technology-enabled integration across the supply web: (1) information exchange supported by electronic data exchange and Internet-based communication platforms; (2) knowledge exchange which focuses on knowledge embedded in databases and processes beyond the sharing of basic information; (3) the sharing of customer touch points; and (4) intense collaboration using multiple systems to perform simple to moderately complex transactions. For the most part, the interorganizational relationships that we surveyed were solely based upon information exchange. The interorganizational integration of business processes to reap benefits from electronic data exchange was limited. These studies did not tend to report firms reengineering their business processes to automatically trigger orders, shipment, payment etc, and to make the flow of electronic information across


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2003). However, to date such structural changes are only conjecture because data exchange studies have not adequately studied the impact of long-term relationships on the structures and processes of EDI trading partners. Proposition 3: Long-term, collaborative hybrid trading relationships utilizing data exchange will display greater levels of integration.

Figure 3. Conceptual gaps organizational boundaries as transparent as possible. Such reengineering efforts are expensive to develop and maintain and therefore may be justifiable for only long-term relationships where both parties are willing to invest their time and effort to successfully implement these systems. Firms engaging in marketbased transactions may not have the incentive to invest in these expensive and complex systems and to undertake efforts to integrate processes between trading partners. Proposition 2: Data exchange in marketbased transactions will be based upon information exchange, which represents the lowest form of integration. The long-term aspects of data exchange partnerships also represent an underexplored area of data exchange research. Should IS researchers explore long-term data exchange relationships, they may uncover major organizational changes. Much like the depth dimension of Massetti and Zmud’s (1996) level of EDI implementation, there may be a deeper level of integration in which the structures of the data exchange partnerships become equivalent or isomorphic over time. As collaboration increases and as the trading partners become more dependent upon one another, they are likely to exhibit similar structural features such as formal policies and organizational models (Teo et al., 2003). Their isomorphism facilitates the transactions they share with one another. Further, because of direct and frequent communication with each other, the partners are more likely to think and act alike (Teo et al.,

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In regard to the IOR typology, it is probably no coincidence that all studies published in 2002 and about half (5 of 9) of those published in 2001 use a network typology. That is, the more recent data exchange research appears to be shedding the dyadic or set view of IORs in favor of data exchange relationships in networks. This is consistent with the focus on network externalities in a world that relies increasingly upon the Internet and open standards. As Tapscott and Carson (1993) noticed, “the new enterprise is open and networked,” and heavily reliant on standards. With network externalities, the value to a participating organization increases when another organization joins the network. This focus on networks in data exchange research is consistent with the concept of a networked society. For example, in Castells’ (1998) networked society, organizations are nodes in massive global financial networks and all nodes are necessary to their network. Yet, the power of any node is subordinate to the power that is derived from the flow of information in the network. The collective power of the nodes of the network overwhelms the power of any individual node. Within the context of a networked society, data exchange research needs to reflect this more complex network view of IORs. For example, when looking at power relationships, a more simplistic assumption of dependencies between two organizations (i.e., buyer and supplier) needs to be replaced with a worldview that includes a complex web of interrelationships and information flows. These information flows will increase with the growing requirement for coordination among the networked organizations. Proposition 4: Networked supply chain trading relationships will display higher levels of integration than dyadic relationships. Power may not be used to “persuade” trading partners in networked supply chain trading relationships to adopt and use data exchange. Asymmetry in power relationships is often used to force trading partners in traditional dyadic and huband-spoke (set) EDI relationships (Hart & Saunders,. 1997; Hart & Saunders, 1998; Premkumar & Ramamurthy, 1995). However, in networked supply


chain trading relationships, no one node may be able to exert power over any other node (Castells, 1998). Thus asymmetry may not be a motive for data exchange adoption. Rather, a norm developed by the network that encourages trading partners to use data exchange may be the prime motivator. The norm may be so strong that it even may be perceived as a necessity.

affected the data exchange diffusion process, and the manner in which trade and industry associations can deploy different strategies. Proposition 7: Web-based data exchange will involve more globally dispersed organizations than traditional data exchange. Methodological Gaps

Proposition 5: Networked supply chain trading relationships will mention asymmetry less often as a motive for adopting and using data exchange than will dyadic or set relationships. With today’s networked economy and increased interfirm collaboration, strategic alliances and joint ventures appear to be increasing in numbers rapidly (Tallman, 1999). Additionally, as we move towards web-based data exchange and as open standards become more prevalent, more research will need to focus on trade associations, strategic alliances and joint ventures since it is them, and not buyer-supplier dyads, that promote widespread use of web-based data exchange. Further, as organizations turn increasingly to supply chain management (SCM), more research will be needed on sets and networks (as compared to dyads). Such research may expand upon the research of Kumar and van Dissel (1996) that considers the sub-optimization of adjacent nodes in the value/supply chain. Kumar and van Dissel suggest that an overall design across the entire value system/supply chain may require different structuring and design within the adjacent nodes, as well as open standards to promote communication across nodes within interorganizational sets and networks. Proposition 6: Greater reliance on the supply chain and trade associations will promote greater usage of web-based data exchange. Finally, the international perspective on data exchange is a research area that warrants increased focus for several reasons. Globalization and the new networked economy will require different business partners all over the world to utilize web-based technologies to conduct business. Additionally, differences in the availability, maturity, compatibility, and reliability of IT infrastructure across national boundaries and at the same time differences in customs, business practices, and regulations may subject IORs to additional complexity and risks, also increasing the potential for conflict (Kumar & vanDissel, 1996). The importance of the cultural context was demonstrated in Damsgaard and Lyytinen’s (2001) cross-cultural study that found that local contingencies, history, and cultural traditions

Our study revealed two major methodological gaps that need to be addressed in future research to provide a better understanding of the implications of data exchange: causal agency used by the researchers and the validity and reliability of measures of data exchange benefits. Most studies reviewed in this research employed the technological imperative to understand the short-term benefits and shortcomings of data exchange. While data exchange impacts could certainly be long-term, the data exchange studies to date in this perspective do not typically address the more progressive or evolving nature of the impacts. The theories studied with this stated perspective tend to be conceptually simpler than theories drawing from the organizational or emergent perspectives. They tend to be easier to test empirically because their linear propositions and hypotheses can be more clearly specified. Hence, there was a heavy reliance on cross-sectional surveys when the technological imperative was applied – an approach that is appropriate for studying short-term, direct impacts of data exchange partnerships. Yet, to focus exclusively on this level is to attribute to it more influence than it has (Perrow, 1965). To extend the horizon, the organizational imperative more often looks beyond the immediate impact to understand the use of electronic data exchange. An even longer-term orientation (and research approach) is required to study the evolution suggested by the more complex, albeit less exact, emergent perspective. It is more difficult to develop and test the theoretical predictions of this perspective because of the non-linearity of emergent patterns. Longitudinal studies can be expected to be more useful than cross-sectional and short-term studies in studying the underlying, evolving dynamics of data exchange implementation and use. However, many longitudinal studies are case studies that suffer from generalizability limitations. Thus, the more generalizable surveys and field studies may be augmented with case studies to add richness to our understanding of the evolving nature of data exchange use and its second-order impacts such as improved customer relations or tighter control over the “supply chain.”


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For example, consider trust or interorganizational collaboration among data exchange trading partners. Building collaborative or trustful relationships takes time and the process can best be studied through longitudinal analysis. Further, it can be anticipated that use of the EDI technology impacts coordination and collaboration, and over time the trading partners may learn to appropriate the technology to improve coordination. In their case study, Hart and Saunders (1997) described how a trusted trading partner used information derived from the EDI systems to propose a change in the use of the system when partial orders were delivered. Instead of using EDI to send a transaction that reflected the unfilled portion of the original order, the partners agreed to use information systems to keep track of the unfilled portions without sending a new request for the unfilled order. To fully understand such an evolving use of data exchange, an emergent perspective using a case study with a longitudinal approach is desirable. Similarly, Damsgaard and Truex (2000) suggest that the emergent perspective is particularly important for the topic of data exchange standards. Because partnerships and business relationships are constantly evolving, data exchange standards, as an emergent grammar, are also evolving. One other methodological gap is the limited effort to assess the validity and reliability of measures of data exchange benefits (Jones & Beatty, 1998). Some items for measuring specific benefits appear in multiple studies, suggesting some content and face validity. However, “in the few studies where prior work is drawn upon, measurement properties such as content validity, construct validity, and reliability are rarely reported” (Jones & Beatty, 1998, p. 211). For example, in their extensive survey of data exchange benefits measures, only two studies reported the results of factor analysis, but neither of these reported the internal consistency of items. Inadequate psychometric testing of measures of data exchange benefits is particularly problematic given the high percentage of studies in our survey that focused on benefits and outcomes. Limitations This study suffers from a few limitations. First, our selected sample, though large, is not exhaustive and could possibly have been expanded. We focused mainly on EDI-enabled IORs which excluded many articles on EDI, including those dealing with standards and security. Second, the set of paradigmatic lenses we used could have been extended. For example, we could have adopted a resource-based view and coded the articles using the three categories (i.e., outside-in, spanning, and inside-out) proposed by Wade and Hulland (Wade &

20

Hulland, 2004) or the six categories (i.e., IT/business partnerships, external IT linkages, business IT strategic thinking, IT business process integration, IT management, and IT infrastructure) proposed by Bharadwaj (2000). A third limitation of this research lies in the data itself. We were not able to run a more elaborate statistical analysis to be able to gain further insight into the lenses overlap. This may be due to the fact that there was not much variance in the data. For example, while data in Tables 3, 5 and 6 would appear to lend itself to Chi Square analysis, too many of the cells had counts less than 5. Combining cells to achieve higher counts did not result in insightful findings. Additionally, the results could have been shaped by the researcher’s biases. Although the intercoder agreement scores were high 80%, the researchers had to rely on their own judgments when coding the articles. This was especially true when authors of the articles in our sample did not explicitly state their position with regards to the paradigm lenses studied.

Conclusions This research makes two major contributions to data exchange research. First, it provides a comprehensive review of EDI studies in the area of interorganizational relationships. Second, it identifies conceptual and methodological gaps and suggests propositions which we hope future researchers can draw upon and use to guide their future research in this area. In summary, the 68 data exchange articles that we reviewed demonstrate considerable consistency in terms of epistemology employed (positivist), timespan considered (single-snapshot cross-sectional), and market categories (as opposed to hierarchies). Future research could benefit from adopting a market perspective of web-based data exchange, a more complete understanding of the impact of open standards on data exchange within complex IORs– especially in terms of the role of trade associations– and increased focus of data exchange role in supply chains. Studies abound on data exchange benefits and implementation. What appears to be an especially fertile area for research is the study of ways to improve collaboration and coordination in data exchange relationships among trading partners. As these more complex, evolving areas are studied, the methodology will need to change in order to capture the complexities and extended time spans. Practitioners, on the other hand, should be aware of the long term implications of data exchange, which can lead to major structural changes in their organizations as level of integration increases with


time. Concomitantly, as the trend of data exchange is moving toward web-based, many-to-many complex relationships, managers should prepare their organization’s strategies to better capitalize on this new trends and to understand the implications of such change to their organizations.

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About the Authors Wafa Elgarah is Assistant Professor of MIS at Al Akhawayn University in Ifrane and a Ph.D. candidate in MIS at the University of Central Florida. She holds a B.B.A. in Management Sciences and Computer Information Systems from IIHE, Morocco, and two MBA's in Marketing and Administrative Management from University of North Texas. She has been involved in various IT projects while working in industry. She has published in several conference proceedings. Her research interests include DSS, design methodologies, intelligent agents and egovernment. Email: [email protected] Natalia Falaleeva is an Assistant Professor of MIS at Loyola College in Maryland and a Ph.D. candidate in MIS at the University of Central Florida. She holds a B.S. in Financial Management and a B.S. in English and Linguistics from Udmurt State University, Russia, and an MBA from the University of Central Florida. She has been involved in various research projects examining human-computer interaction, impact of individual psychological differences on remote work, and IS outsourcing. Her dissertation focuses on


termination of IS outsourcing relationships and antecedents of IS backsourcing. Email: [email protected] Carol Saunders is Professor of MIS at UCF. She earned a Ph.D. in organizational behavior and management at the University of Houston. Dr. Saunders was formerly the W.P. Wood Professor of MIS at the University of Oklahoma. Her current research interests include impacts of information systems on power and communication, interorganizational linkages, and virtual teams. She is editor in chief of MIS Quarterly, associate editor of Information Systems Research, Decision Sciences, Information Resources Management Journal and senior editor of e-Services Journal. She was the general conference chair of ICIS ’99. Email: [email protected] Virginia Ilie is a Ph.D. candidate in MIS at the University of Central Florida. She holds a BS in International Business from ASE Bucharest, Romania and a MBA from the University of Central Florida. She published various conference proceedings, one of which gained her the best paper award. Her research interests include decision support systems, IT diffusion and IS outsourcing. One of her papers has appeared in Information Resource Management Journal. Email: [email protected]

J.T. Shim is a fourth-year doctoral student. He holds an MBA from Rollins College. His research interests are diverse but his current focus includes researching the impact of trust and concern for information privacy in an e-commerce setting. A paper on this topic has been accepted to SAIS 2004. His minor in accounting currently aims his research at determining the impact of continuous auditing in accounting information systems to detect and reduce fraud. His decade of industry experience includes, but is not limited to, mainframe, PC and communications industries. Email: [email protected] James F. Courtney is Professor of MIS at UCF. He was formerly Tenneco Professor of Business Administration in the Information and Operations Management Department at Texas A&M University. He earned his Ph.D. in Business Administration from the University of Texas at Austin. His academic experience also includes faculty positions at Georgia Tech, Lincoln University in New Zealand and the State University of New York at Buffalo. He is coauthor of Database Systems for Management (Second Edition, Irwin Publishing Company, 1992). His papers have appeared in top MIS journals, including Management Science, MIS Quarterly, and Communications of the ACM. Email: [email protected]


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Appendix A. Coding Sheet Article Log Authors: Journal Title Year: Month: Article Title: Epistemology: Descriptive Positivist Interpretive Research Approach: Case Study Field experiment Essay Conceptual Time Span of Research: Cross Sectional Longitudinal Single Snapshot Single snapshot

Volume: Pages: Critical

N/A Field Study N/A

Survey Review

Cross Sectional Single Snapshot - Multiple snapshot

Process Traces

N/A Causal Agency: (check all that apply) Technological _____% Organizational _____% Emergent ______% N/A IOR Motives: (check all that apply) Necessity Asymmetry Reciprocity Efficiency Stability Legitimacy N/A TCE Category: Hierarchy Market Hybrid N/A IOR Typology: Dyads

Publishing Outlet: Article Summary:

26

Sets

Management

Networks

IS

Marketing


Appendix B. Summary of paradigmatic lenses underlying EDI studies (1 of 3) Year 1993 1994 1994 1994 1994 1994 1995 1995

Authors

TCE IOR IOR Category Typology Motives Clemons, Eric; Row, Michael Market Dyads A, E Premkumar, G.; Ramamurthy, K.; Market Dyads E, R Nilakanta, Spree Riggins, Frederick; Kriebel, Market Dyads R Charles; Mukhopadhyay, Tridas Riggins, Frederick J.; Hybrid Sets A, E Mukhopadhyay, Tridas Vlosky, Richard; Smith, Paul; Market Dyads A, E Wilson, David Williams, Lisa R. Market Dyads A, E, R Banerjee, Snehamay; Sriram, Ven Market Dyads A, E Daugherty, Patricia; Germain, Hierarchy Network E Richard; Droge, Cornelia

1995 Fulk, Janet; DeSanctis, Gerardine 1995 Iacovou, Charalambos L.; Benbasat, Izak; Dexter, Albert S. 1995 Mukhopadhyay, Tridas; Kekre, Sunder; Kalathur, Suresh 1995 Premkumar, G.; Ramamurthy, K. 1995 Walton, Lisa Williams; Miller, Linda G. 1995 Wang, Eric Seidmann, Abraham 1996 Gottardi, Giorgio; Bolisani, Ettore 1996 Kumar, Kuldeep; van Dissel, Han 1996 Mackay, David; Rosier, Malcolm 1996 Marcussen, Carl H. 1996 Massetti, Brenda; Zmud, Robert 1996 Murphy, Paul; Daley, James 1997 Barua, Anitesh; Lee, Byungtae 1997 Bensaou, M. 1997 Bergeron, F.; Raymond, L. 1997 Hart, Paul; Saunders, Carol 1997 Philip, George; Pedersen, Patricia 1997 Premkumar, G.; Ramamurthy, K.; Crum, M. 1997 Teo, Hock-Hai; Tan, Bernard; Wei, Kwok-Kee

Hybrid Market

Network E, R Sets A, N

Hybrid

Sets

Market Hybrid

Sets Dyads

A, E, S

Causal Agency Org Org, Tech Emerg

Epistemology Research Time Span Approach TG Field Study CSSS TG Survey CSSS TG

Conceptual NA

Org, Tech Org, Tech Org Tech Org, Tech, Emerg Emerg Emerg, Org Tech

TG TG

Case Study Process Traces Survey CSSS

TG TG TG

Survey Survey Survey

D Interpretive

Conceptual NA Case Study CSSS

TG

Case Study Longitudinal

TG TG

Survey CSSS Conceptual NA

TG

Conceptual NA

C

Conceptual NA

D

Conceptual NA

TG

Survey

D

Case Study CSSS

D

Case Study CSSS

D

Survey

TG TG

Conceptual NA Field Study CSSS

TG

Survey

TG

Conceptual NA

D

Survey

CSSS

TG

Survey

CSSS

I

Case Study Longitudinal

A, E, R Org A, E, R, Org S Hybrid Sets A, E Tech, Org Hybrid Sets A, E Emerg, Org, Tech Hybrid Network A, E, N, Org, R, S Tech Market Dyads E Org, Tech Hybrid Network E, R Org, Tech Hybrid Sets E Tech, Emerg, Org Market Sets E, R, S Org, Tech Market Sets A, E Emerg Hybrid Dyads E, R, S Org, Tech Hybrid Dyads A, E Org, Tech Hybrid Dyads A, E Emerg, Org, Tech Market Sets E Org, Tech Market Sets A, E, R, Org S Hierarchy Network E, N Emerg, Org


CSSS CSSS CSSS

Longitudinal

CSSS

Longitudinal

27

Appendix B. Summary of paradigmatic lenses underlying EDI studies (2 of 3) Year

Authors

1997 Vijayasarathy, Leo R; Tyler, Michael L. 1997 Vijayasarathy, Leo R.; Robey, Daniel 1998 Boudreau, Marie Claude; Loch, Karen; Robey, Daniel; Straub, Detmar 1998 Chen, Jui-Chih; Williams, Bernard C. 1998 Crook, Connie W.; Kumar, Ram L. 1998 Hart, Paul; Saunders, Carol

TCE IOR IOR Causal Epistemology Research Time Span Category Typology Motives Agency Approach Hybrid Dyads E, R Org D Survey CSSS Market

Dyads

E, R

Hierarchy Network E Hybrid

Sets

Hybrid

Sets

Hybrid

Dyads

1998 Peffers, Ken; Dos Santos, Brian L.; Thurner, Peter F. 1998 Ratnasingham, Pauline

Hybrid

Dyads

Hybrid

Dyads

1998 Wilson, David; Vlosky, Richard

Market

Dyads

1998 Williams, Lisa R.; Magee, George Hybrid D.; Suzuki, Yoshinori 1999 Fearon, Colm; Phillip, George Hybrid 1999 Holmes; Terence L.; Srivastana, Rajesh 1999 Larson, Paul D.; Kulchitsky,Jack D 1999 Lee, Ho; Clark, Theodore; Tam, Kar 1999 Murphy, Paul; Daley, James

Hybrid

Tech, Org Org, Tech

A, E, N, Org, S Tech A, E, R Org

TG

Survey

D

Conceptual NA

D

Case Study CSSS

D

Case Study CSSS

A, E, R Emerg, TG Org A, E, R Org, D Tech E, R Tech D

CSSS

Field Study CSSS Case Study Longitudinal Conceptual NA

TG

Survey

CSSS

Dyads

A, E, R Tech, Org A, E Tech

TG

Survey

CSSS

Sets

E, R

I

Field Study CSSS

Dyads

Org, Tech A, E, R Org

TG

Survey

CSSS

Hybrid

Sets

E

TG

Survey

CSSS

Market

Sets

E

TG

Market

Sets

A, E, R, S E E, R

TG

Case Study Cross Multiple Survey CSSS

I TG

Case Study CSSS Survey CSSS

D

Review

NA

TG

Survey

CSSS CSSS

Org, Tech Org

1999 Ratnasingham, Pauline 1999 Ramamurthy, K.; Premkumar, G.; Crum, Michael R. 1999 Riggins, Frederick; Mukhopadhyay, Tridas 1999 Young, Dale; Carr, Houston H.; Rainer, Jr., Kelly R. 2000 Angeles, Rebecca; Nath, Ravinder

Hybrid Market

2000 Chan, Steven; Davis, Tim R.V. 2000 Chatfield, Akemi T. & Yetton, Philip 2000 Damsgaard, Jan; Truex III, Duane 2000 Dupuy, Christopher A.; Vlosky, Richard P. 2000 Johnston, R.B.; Gregor, S.

Market Hybrid

Org, Tech Dyads Tech Sets Org, Tech Dyads E, R Org, Tech Sets A, E, R Org, Tech Dyads E, N, R Org, Tech Network E, R Tech Dyads A, E, R Org

Hybrid Market

Dyads Dyads

Market

Network E

Conceptual NA

2000 Nakayama, Makoto

Hybrid

Dyads

Survey

CSSS

2000 Rassameethes, Bordin; Kurokawa, Market Susumu; LeBlanc, Larry J. 2000 Shirland, Larry E; Market Thompson, Ronald L.

Dyads

Survey

CSSS

Review

NA

28

Market Market Market

E E

Tech Org

TG

Survey

D TG

Essay NA Field Study CSSS

C TG

Field Study CSSS Survey CSSS

Emerg, D Tech A, E, R Org, TG Tech E Tech D

Network E, R

Tech

D


Appendix B. Summary of paradigmatic lenses underlying EDI studies (3 of 3) Year 2000 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001 2002 2002

Authors

TCE IOR IOR Causal Epistemology Category Typology Motives Agency Sriram, Ram S.; Arunachalam, Hybrid Dyads A, E Emerg, TG Vairam; Ivancevich, Daniel M. Org, Tech Truman, Gregory Market Dyads E, R Org TG Ahmad, Sohel; Schroeder, Roger Market Network E, N, R Tech TG Angeles, Rebecca; Nath, Ravi Hybrid Dyads E, R Tech D Chwelos, P.; Benbasat, I.; Dexter, Hybrid Dyads A, N Emerg TG A. Damsgaard, Jan; Lyytinen, Kalle Market Sets E, L, N, Tech I S Humphreys, P.K.; Lai, M.K.; Hierarchy Sets E, R Tech D Sculli, D Iskandar, Basuki; Kurokawa, Market Network A, E, N, Tech TG Susumu; LeBlanc. Larry J. R Maingot, Michael; Quon, Tony Hierarchy Dyads E, R Tech D Lim, Don; Prashant, Palvia Hybrid Network E Tech TG Raghunathan, Srinisivan; Yeh, Market Network E Emerg TG Arthur B. Downing, Charles E. Market Network E, R Tech TG Hill, Craig; Scudder, Gary D. Market Network E Tech D

Research Time Span Approach Survey CSSS Survey Survey Survey Survey

CSSS CSSS CSSS CSSS

Field Study CSSS Review

NA

Survey

CSSS

Survey CSSS Survey CSSS Conceptual NA Survey Survey

CSSS CSSS

IOR Motives –A: Asymmetry, E: Efficiency, L: Legitimacy, N: Necessity, R: Reciprocity. Causal Agency –Tech: Technology, Org: Organizational, Emerg: Emergent. Epistemology –TG: Theoretically Grounded, D: Descriptive, I: Interpretive Time Span – CSSS: Cross Sectional Single Snapshot


29