The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Addressing the endogeneity dilemma in operations management research: Theoretical, empirical, and pragmatic considerations
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Specification Tests in Econometrics
Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Microeconometrics: Methods and Applications
Cameron, A. C., & Trivedi, P. K. (2005) · doi:10.1017/cbo9780511811241 · Cited by 8,092
The Matthew Effect in Science: The reward and communication systems of science are considered.
Merton, R. K. (1968) · Science · doi:10.1126/science.159.3810.56 · Cited by 6,720
Introduction to Statistical Mediation Analysis
MacKinnon, D. P. (2012) · doi:10.4324/9780203809556 · Cited by 5,618
A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
On making causal claims: A review and recommendations
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010) · The Leadership Quarterly · doi:10.1016/j.leaqua.2010.10.010 · Cited by 2,441
The Sources of Four Commonly Reported Cutoff Criteria: What Did They Really Say?
Lance, C. E., Butts, M. M., & Michels, L. C. (2006) · Organizational Research Methods · doi:10.1177/1094428105284919 · Cited by 2,053
Latent Variable Modeling of Differences and Changes with Longitudinal Data
McArdle, J. J. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.60.110707.163612 · Cited by 1,952
Counterfactuals and Causal Inference: Methods and Principles for Social Research
Morgan, S. L., & Winship, C. (2007) · doi:10.1017/cbo9780511804564 · Cited by 1,146
Cited by
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
Dealing with endogeneity bias: the generalized method of moments (GMM) for panel data
Ullah, S., Akhtar, P., & Zaefarian, G. (2018) · Industrial Marketing Management · doi:10.1016/j.indmarman.2017.11.010 · Cited by 1,101
A critique of the Leader-Member Exchange construct: Back to square one
Gottfredson, R. K., Wright, S. L., & Heaphy, E. D. (2020) · The Leadership Quarterly · doi:10.1016/j.leaqua.2020.101385 · Cited by 205
Bridging the gap: why, how and when HR analytics can impact organizational performance
McCartney, S., & Fu, N. (2022) · Management Decision · doi:10.1108/md-12-2020-1581 · Cited by 173
‘Better together’: Evidence on the joint adoption of circular economy and industry 4.0 technologies
Lopes de Sousa Jabbour, A. B., Chiappetta Jabbour, C. J., Choi, T.-M., & Latan, H. (2022) · International Journal of Production Economics · doi:10.1016/j.ijpe.2022.108581 · Cited by 122
Wang, Y., Zhu, Q., Krikke, H., & Hazen, B. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120275 · Cited by 89
The Signals that Potential Contributors Look for When Choosing Open-source Projects
Qiu, H. S., Li, Y. L., Padala, S., Sarma, A., & Vasilescu, B. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359224 · Cited by 75
Metadata
- Authors
- Ketokivi, M., & McIntosh, C. N.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2017-05
- Volume
- 52
- Issue
- 1
- Pages
- 1-14
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W2617813711
- Cited by
- 429
- References
- 12
- FWCI
- 20.35
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
