Addressing the endogeneity dilemma in operations management research: Theoretical, empirical, and pragmatic considerations

TLDR

Existing research on endogeneity in operations management focuses mainly on statistical techniques that rely on unrealistic zero‑correlation assumptions, which are untestable in practice and can unduly constrain substantive inquiry, highlighting the need for a broader theoretical and pragmatic approach. The study investigates realistic expectations for handling endogeneity in empirical operations management research, revisiting technical foundations and proposing reasonable criteria tailored to the field.

Abstract

Abstract In this paper, we examine the problem of endogeneity in the context of operations management research. Whereas the extant literature has focused primarily on the statistical aspect of the problem, a comprehensive treatment requires an examination of theoretical and pragmatic considerations as complements. The prevailing problem with the focus on statistical techniques is that the standards tend to be derived from idealizations: the correlation between a regressor and a disturbance term must be exactly zero, or the analysis will be invalid. In actual empirical research settings, such a knife‐edge assumption can never be satisfied, indeed it cannot even be directly tested. Idealizations are useful in helping us understand what it would take to eliminate endogeneity, but when applied directly and unconditionally, they lead to unreasonable standards that may unnecessarily stifle substantive inquiry. We believe that it is far more productive and meaningful to ask: “What can we realistically expect empirical scientists to be able to achieve?” To this end, we cover and revisit some of the general technical material on endogeneity, paying special attention to the idiosyncrasies of operations management research and what could constitute reasonable criteria for addressing endogeneity in empirical operations management studies.