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
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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Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Practical issues in structural modeling
BENTLER, P. M., & CHOU, C.-P. (1987) · Sociological Methods & Research · doi:10.1177/0049124187016001004 · Cited by 5,804
Conventional wisdom on measurement: A structural equation perspective.
Bollen, K., & Lennox, R. (1991) · Psychological Bulletin · doi:10.1037/0033-2909.110.2.305 · Cited by 3,618
On structural equation modeling with data that are not missing completely at random
Muthén, B., Kaplan, D., & Hollis, M. (1987) · Psychometrika · doi:10.1007/bf02294365 · Cited by 883 · cited 2×
“Significant missing data result in convergence failures, biased parameter estimates, and inflated fit indices (Brown, 1994; Muthen et al., 1987 ).”
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Cited by
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014) · European Business Review · doi:10.1108/ebr-10-2013-0128 · Cited by 11,728
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Partial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285
Consistent Partial Least Squares Path Modeling1
Dijkstra, T. K., & Henseler, J. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.2.02 · Cited by 2,898
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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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).
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