Common Beliefs and Reality About PLS

By Jörg Henseler, Theo K. Dijkstra, Marko Sarstedt, Christian M. Ringle, Adamantios Diamantopoulos, Detmar W. Straub Jr., Dave J. Ketchen, Joseph F. Hair, G. Tomas M. Hult, Roger J. Calantone. 2014

3,243 citations recorded by the source corpus.

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.

Subfields: Ecology, Evolution, Behavior and Systematics, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/a78c912853eafb15bd6fe1ec404211dad8cd2715

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In Ecology, Evolution, Behavior and Systematics

In Organizational Behavior and Human Resource Management

Citations in the corpus

Partial least squares path modeling: Time for some serious second thoughts (2016, methodology): “Particularly, even though the BCa intervals that correct for skewness in the estimates are promising (Henseler et al., 2014), it is not clear how the approach performs with the bimodal distributions that plague PLS estimates.”
On the adoption of partial least squares in psychological research: Caveat emptor (2015, methodology): “…intervals could be used to overcome the issue noted above, asserting in particular that users of PLS-Graph need not be concerned, despite the evidence that the confidence intervals produced by the software are severely biased (Lu, Kwan, Thomas, & Cedzynski, 2011; cited in Henseler et al., 2014)6.”
Access-Based Services as Substitutes for Material Possessions: The Role of Psychological Ownership (2020, methodology): “For both studies, we used partial least squares structural equation modeling (PLS-SEM) that is particularly suitable when the main research objective is theory development (Hair, Ringle, and Sarstedt 2011; Henseler et al. 2014).”
An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research (2017, methodology): “However, such an analysis is based on the implicit assumption that factor-based SEM is the correct estimator that delivers true results as a SEM benchmark, which several researchers view critically (e.g., Henseler et al., 2014; Rigdon, 2012; Rigdon et al., 2017).”
Mastering digital transformation: The nexus between leadership, agility, and digital strategy (2022, background): “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).”
Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! (2020, methodology): “However, composite-based methods such as PLS-SEM and generalized structured component analysis (GSCA) also correct for measurement error, even when estimating factor models (Henseler et al., 2014; Sarstedt et al., 2016).”
The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective (2020, methodology): “The discriminant validity was assessed by cross-loadings (Hair and Lukas 2014), the Fornell-Larcker criterion (Fornell and Larcker 1981) and the heterotrait-montrait ratio (HTMT) (Henseler et al. 2014).”