The Empiricism-Modeling Dichotomy in Operations and Supply Chain Management

By Dooley, Kevin J. | Journal of Supply Chain Management, Winter 2009 | Go to article overview

The Empiricism-Modeling Dichotomy in Operations and Supply Chain Management


Dooley, Kevin J., Journal of Supply Chain Management


INTRODUCTION: A FIELD DIVIDED?

A discipline is in part defined by its journals, and a study of journals in the Operations and Supply Chain Management (OSCM) area supports the premise that a divide exists between empiricists and modelers in our discipline. Historically, OSCM has been dominated by mathematical modelers, stemming from the successful application of operations research (OR) in applying optimization techniques to complicated problems in production and logistics design and control. During the 1950s and 1960s, much of the work in OR created inventions that solved fundamental problems that industry could not previously address. Scientists do not stop work once fundamental problems are solved however--they continue to work on more complex problems, as what drives them is the interestingness of the problem, not necessarily its relevance (Kuhn 1962).

As OR researchers continued to work on more challenging but less relevant problems, cries for relevancy (Lee 1966; Miser 1987; Shubik 1987) eventually led to calls for more empirical research, based on the assumption that studies grounded in data would likely have more relevance than studies grounded in mathematics (Buffa 1980; Andrew and Johnson 1982; Meredith, Raturi, Amoako-Gyampah and Kaplan 1989; Flynn, Sakakibara, Schroeder, Bates and Flynn 1990; Swamidass 1991). The call for empirical research was not met however with an immediate increase in empirical publications (Corbett and Wassenhove 1993); the birth of two journals specializing in empirical research helped change that and bring legitimacy to empirical research in the discipline.

The Journal of Operations Management (JOM) was created in 1980 by the American Production & Inventory Control Society Inc. (APICS) with the purpose of giving "high priority ... to papers reporting on actual application of proposed theories or concepts in an organization" (Boyer and Swink 2006, p. 732). As Boyer and Swink (2006) point out in their analysis of the history of JOM, the editors of JOM increasingly interpreted "relevant" as "empirical." Today, JOM's mission is to "publish original, high quality, empirically based research" (Boyer and Swink 2006, p. 732), clearly demarcating empirically based research from modeling-based research. While the editorial statement leaves room open for modeling applications, the journal's mission statement is clear that these are exceptions to the norm.

The Production and Operations Management Society (POMS) was formed in 1989, also with a stated mission of enhancing relevancy, and the first POM journal was subsequently published in 1992. While the POM journal's editorial policy was not explicitly empirical, the editorial policy and board of POM was heavily biased towards empiricism (Gupta, Verma and Victorino 2006). Despite this emphasis, only one-half of POM's published articles are based on data (Gupta et al. 2006).

The latest editorial statement of this journal, Journal of Supply Chain Management (JSCM), also emphasizes empiricism over modeling (Carter, Ellram and Kaufmann 2008). According to Carter et al., the journal's mission is to publish "high-quality, high-impact behavioral research focusing on theory-building and empirical research." Again I note the clear distinction between empiricism and modeling by the statement calling out "empirical research" versus a more general call for "relevancy." JSCM's list of acceptable research methods includes analysis of survey data, structural equation modeling, ANOVA, cluster analysis, regression analysis, case studies, laboratory experiments, secondary data analysis, theory building, and social network analysis. Noticeably absent from the list are OR techniques such as mathematical modeling, optimization, and simulation. Empiricists in OSCM (Boyer and Swink 2008; Carter, Sanders and Dong 2008) have called for more use of multiple methods, but their suggestions only go so far as to suggest use of multiple empirical methods. …

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