Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research

Guanyi Lu, Xin (David) Ding, David Xiaosong Peng, Howard Hao-Chun Chuang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Addressing endogeneity can be a challenging task given the different sources of endogeneity and their impacts on empirical results. While premier business journals typically expect authors to rigorously address endogeneity, this expectation is relatively new to many Operations Management (OM) scholars, as exemplified by a recent editorial in Journal of Operations Management that calls for more rigorous treatment for endogeneity. This study serves two purposes. First, we summarize recent OM literature with respect to the treatment for endogeneity by reviewing studies published in leading OM journals between 2012 and 2017. The review provides evidence that endogeneity problems have received increasing attention from OM scholars. However, we also find some common problems that may render the chosen techniques for addressing endogeneity less effective and potentially lead to biased analysis results. Second, since instrumental variable regression is the most prevalent technique for dealing with endogeneity in the OM literature according to our review, we provide an empirical illustration tailored to OM researchers for using instrumental variable regression in the post-design (data analysis) phase. Using variables from a publicly available healthcare dataset, our analysis sheds light on the importance of examining instruments’ quality and triangulating results based on more than one test/estimator.

Original languageEnglish (US)
Pages (from-to)53-64
Number of pages12
JournalJournal of Operations Management
Volume64
DOIs
StatePublished - Nov 2018

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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