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Cited by 976

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Use of structural equation modeling in operations management research: Looking back and forward

Shah, Rachna, Goldstein, Susan Meyer·2006·Journal of Operations Management

Abstract

This paper reviews applications of structural equation modeling (SEM) in four major Operations Management journals (Management Science, Journal of Operations Management, Decision Sciences, and Journal of Production and Operations Management Society) and provides guidelines for improving the use of SEM in operations management (OM) research. We review 93 articles from the earliest application of SEM in these journals in 1984 through August 2003. We document and assess these published applications and identify methodological issues gleaned from the SEM literature. The implications of overlooking fundamental assumptions of SEM and ignoring serious methodological issues are presented along with guidelines for improving future applications of SEM in OM research. We find that while SEM is a valuable tool for testing and advancing OM theory, OM researchers need to pay greater attention to these highlighted issues to take full advantage of its potential.

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Metadata

Authors
Shah, R., & Goldstein, S. M.
Venue
Journal of Operations Management
Publisher
Wiley
Published
2005-07-05
Volume
24
Issue
2
Pages
148-169
ISSN
  • 0272-6963 (ISSN-L)
  • 1873-1317
OpenAlex
W2146434748
Cited by
976
References
14
FWCI
25.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Use of structural equation modeling in operations management research: Looking back and forward. https://app.cochat.ai/papers/10.1016/j.jom.2005.05.001
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Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management. https://doi.org/10.1016/j.jom.2005.05.001

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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.