The DOI matches its Crossref record; 108 of the 111 references it cites were found in a registry or an index.
Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Open accessAbstract
This article addresses Rönkkö and Evermann’s criticisms of the partial least squares (PLS) approach to structural equation modeling. We contend that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann’s study: (a) the adherence to the common factor model, (b) a very limited simulation designs, and (c) overstretched generalizations of their findings. Whereas Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that we, in turn, debunk. By examining their claims, our article contributes to reestablishing a constructive discussion of the PLS method and its properties. We show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, we can conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines.
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In the citing paper
“Finally, the goodness of fit was determined using SmartPLS by calculating the standardized root mean square residual (SRMR; 0.076) and the normed fit index (NFI; 0.866). SRMR values below 0.08 mean that the data fit the model (Hu and Bentler, 1999; Henseler et al., 2014), whereas for NFI values (which are between 0 and 1), the closer the value is to 1, the better the fit (Lohm ¨ oller, 1989).”
From Mastering digital transformation: The nexus between leadership, agility, and digital strategy (2022) · cites this paper as Henseler et al., 2014
In the citing paper
“PLS-SEM does not require the data to meet certain distributional assumptions and can work with non-normal data (Henseler et al., 2014);”
From A consumer behavioural approach to food waste (2018) · cites this paper as Henseler et al., 2014
In the citing paper
“The bootstrap confidence interval for the Squared Root Mean Residual is [0.043, 0.053], lower than the suggested threshold of 0.08 (Henseler et al., 2014).”
From A consumer behavioural approach to food waste (2018) · cites this paper as Henseler et al., 2014
In the citing paper
“so the model fit should be read as adequate rather than strong.”
From Seeing Is Believing, Experiencing Is Deciding: How Augmented Reality Attributes Drive Confidence in Choice and Willingness to Pay (2026) · cites this paper as Henseler et al. (2014)
In the citing paper
“The value of the SRMR was 0.037, which is lower than the threshold of 0.080 [138].”
In the citing paper
“In other words, a confirmatory composite analysis helps determine whether it makes sense to create the proposed formative construct and identifies any model misspecifications [138,139].”
Citations
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Cited by
Sorted by citations
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
Self-citationWhen to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Self-citationUsing pls path modeling in new technology research: updated guidelines
Henseler, J., Hubona, G., & Ray, P. A. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-09-2015-0382 · Cited by 7,173
Self-citationAssessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446
Self-citationAn updated and expanded assessment of PLS-SEM in information systems research
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-04-2016-0130 · Cited by 4,043
Self-citation
Metadata
- Authors
- Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J.
- Affiliations
- Radboud University Nijmegen (NL)
- University of Groningen (NL)
- University of Newcastle Australia (AU)
- University of Vienna (AT)
- University System of Georgia (US)
- Auburn University (US)
- Kennesaw State University (US)
- Michigan State University (US)
- Corresponding author
- Henseler, Jörg
- ORCID
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2014-04
- Volume
- 17
- Issue
- 2
- Pages
- 182-209
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2100739170
- Cited by
- 3,332
- References
- 111
- Citations by year
- 2026: 327 · 2025: 396 · 2024: 433 · 2023: 458 · 2022: 424 · 2021: 349 · 2020: 261 · 2019: 230 · 2018: 177 · 2017: 116 · 2016: 106 · 2015: 40 · 2014: 14
- FWCI
- 115.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Organizational Research Methods (publishedVersion, cc-by-nc)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by-nc)
- University of Twente Research Information (publishedVersion, cc-by)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion, cc-by-nc)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by-nc)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by-nc)
- University of Twente Research Information (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- partial least squares, structural equation modeling, common factor model, simulation study, exploratory research, methodological considerations
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