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

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Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “Endogeneity may substantially bias one’s research findings, so it is critical that authors explicitly address endogeneity in their manuscripts from both a theoretical and an empirical standpoint (Ketokivi & McIntosh, 2017).”
Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data (2018, methodology): “As there are no direct tests for endogeneity, the choices of indirect tests and precautionary measures can help to guide relevant insights and conclusions (Ketokivi & McIntosh, 2017).”
Impact of supply base structural complexity on financial performance: Roles of visible and not‐so‐visible characteristics (2017, methodology): “Instrumental variables and panel datasets are commonly used to address the omitted variable bias (Ketokivi and McIntosh, 2017).”
On doing relevant and rigorous experiments: Review and recommendations (2018, methodology): “…the fields lagging behind our statistical validity suggestions: Perhaps because the field has started to consider some of these statistical issues only recently (see Ketokivi & McIntosh, 2017), the profession still needs to discount the effect of incorrectly estimating models on academic impact.”