Using partial least squares in operations management research: a practical guideline and summary of past research

Peng, David Xiaosong; Lai, Fujun

doi:10.1016/j.jom.2012.06.002

Cited by 1,532Psychometric Methodologies and TestingBusiness Strategies and Management ResearchAdvanced Statistical Methods and Models

Using partial least squares in operations management research: a practical guideline and summary of past research

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Peng, David Xiaosong, Lai, Fujun·2012·Journal of Operations Management

DOI 10.1016/j.jom.2012.06.002

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Abstract

The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.

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The model has high predictive relevance for BDA-AI (Q2=0.73) and OP (Q2=0.81).
“In our case, we noted the Q² for BDAAI (0.73) and OP (0.81), indicating high predictive relevance (Peng and Lai, 2012).”
From Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations (2020) · cites this paper as Peng and Lai, 2012

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Authors: Peng, D. X., & Lai, F.
Affiliations: Texas A&M University (US)
University of Southern Mississippi (US)
Corresponding author: Peng, David Xiaosong
ORCID: Peng, David Xiaosong
Lai, Fujun
Venue: Journal of Operations Management
Publisher: Wiley
Published: 2012-06-26
Volume: 30
Issue: 6
Pages: 467-480
ISSN: 0272-6963 (ISSN-L)
1873-1317
DOI: 10.1016/j.jom.2012.06.002
OpenAlex: W2097247056
Cited by: 1,532
References: 82
Citations by year: 2026: 77 · 2025: 117 · 2024: 136 · 2023: 162 · 2022: 176 · 2021: 161 · 2020: 167 · 2019: 117 · 2018: 135 · 2017: 80 · 2016: 64 · 2015: 57 · 2014: 57 · 2013: 25 · 2012: 1
FWCI: 59.12
Type: journal article
Language: en
Open access: green
aquila.usm.edu
Licence: cc-by-nc-nd
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Journal of Operations Management (publishedVersion)
Aquila Digital Community (University of Southern Mississippi) (submittedVersion, cc-by-nc-nd)
SSRN Electronic Journal (submittedVersion)
Funders: National Natural Science Foundation of China (71090403/71090400)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Social Sciences › Decision Sciences › Management Science and Operations Research › Business Strategies and Management Research
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: partial least squares, structural equation modeling, operations management research, research methodology guidelines

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Using partial least squares in operations management research: a practical guideline and summary of past research

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