The Impact of Electronic Data Interchange on Delivery Performance

Ahmad, Sohel; Schroeder, Roger G.

doi:10.1111/j.1937-5956.2001.tb00065.x

Cited by 112 publications

(75 citation statements)

References 38 publications

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“…In creating the e-collaboration scale, we asked respondents to specify how routinely they use the primary B2B marketspace to facilitate collaborative activities with the primary customer, including product design, forecasting/ production planning, and logistics planning (Ahmad and Schroeder, 2001;Rosenzweig et al, 2009;Zhu and Kraemer, 2002); see Appendix for details regarding the measures used in the analysis. Importantly, our e-collaboration scale captures the extent to which the manufacturer's systems, routines, and procedures are linked to its primary customer, which is the B2B marketspace customer representing the highest percentage of the manufacturer's sales or the manufacturer's most significant relationship for competing in the future.…”

Section: E-collaborationmentioning

confidence: 99%

A contingent view of e‐collaboration and performance in manufacturing

Rosenzweig¹

2009

J of Ops Management

191

165

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Manufacturers are increasingly utilizing Internet‐based tools to more readily conduct collaborative activities with key business customers. While the emerging conventional wisdom suggests that the greater the extent to which manufacturers engage in Internet‐enabled commerce with downstream business customers the better the performance, we espouse an alternative view. Consistent with the relational view of competitive advantage and contingency theory, we develop a model and a series of hypotheses that specify how various product and market characteristics may influence the nature of the expected positive relationship between e‐collaboration and performance.To test the model, we collected data from 50 manufacturers using a Web‐based survey. Our partial least squares (PLS) analysis results do indeed support the notion that e‐collaboration is related to better operational and business performance. However, we go on to show that the strength of the relationship between e‐collaboration and operational performance diminishes as the level of environmental munificence increases. Notably, we found no such moderating effect with respect to the level of product complexity or market variability. Our findings contribute to the operations strategy literature on supply chain relationships in the e‐business arena and offer managers a framework for understanding the conditions under which investments in e‐collaboration may be more appropriate and therefore more beneficial.

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Section: E-collaborationmentioning

confidence: 99%

A contingent view of e‐collaboration and performance in manufacturing

Rosenzweig¹

2009

J of Ops Management

191

165

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“…They rely on their quasi-experimental setting to directly estimate the effect of the number of competitors on innovation outcomes. The well-cited empirical papers in IT management include those by Ahmad and Schroeder (2001) and McAfee (2002). McAfee (2002) exploits a natural experiment conducted at a U.S. high-tech manufacturer and documents the longitudinal effect of IT adoption on operational performance.…”

Section: Miscellaneous Topics (Online Appendix Table A6)mentioning

confidence: 99%

OM Forum—Causal Inference Models in Operations Management

Lim

Reza

et al. 2017

M&SOM

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Abstract. Operations management (OM) researchers have traditionally focused on developing normative mathematical models that prescribe what managers and firms should do. Recently, there has been increased interest in understanding what managers and firms actually do and the factors that drive these decisions. To advance this understanding, empirical investigation using causal inference models is critical. However, in many contexts, the ability to obtain causal inferences is fraught with the challenges of endogeneity and selection bias. This paper describes five empirical tools that have been widely used in economics to address these challenges and how they can be adopted by OM researchers. We also present an example that illustrates how the various attributes of big data-variety, velocity, and volume-can be useful in addressing the endogeneity bias.

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“…Lim and Palvia (2001) find that EDI integration is associated with a decrease in inventory levels; yet, Teo, Tan, Wei, and Woo (1995) find no significant relationship. Finally, Ahmad and Schroeder (2001) find that EDI use significantly improves delivery performance, but Walton and Marucheck (1997) find no significant support for that benefit.…”

Section: Introductionmentioning

confidence: 90%

Electronic Data Interchange: Research Review and Future Directions*

2009

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For nearly two decades, electronic data interchange (EDI) has been widely viewed as a technology pivotal to supply chain management that has also provided benefits to firms on multiple levels. Despite a substantial body of literature, there are a number of conflicting and inconclusive research results in this field. In this study, we synthesize the diverse body of research in EDI by organizing the literature into an initial theoretical framework. Based on a meta-analysis of results from the empirical literature, we seek to clarify conflicting results from the literature in order to develop a more unified theoretical framework of contextual variables associated with EDI adoption factors and outcomes. From a managerial standpoint, our literature-based framework offers a set of guidelines for making successful EDI adoption and implementation decisions.

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The Impact of Electronic Data Interchange on Delivery Performance

Cited by 112 publications

References 38 publications

A contingent view of e‐collaboration and performance in manufacturing

A contingent view of e‐collaboration and performance in manufacturing

OM Forum—Causal Inference Models in Operations Management

Electronic Data Interchange: Research Review and Future Directions*

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