A critical look at partial least squares modeling

By George A. Marcoulides, Wynne W. Chin, Carol Stoak Saunders. 2009

336 citations recorded by the source corpus.

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d36f3e3c6b0e7e7bea963cc4aaafb6ad2eac3b24

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In Applied Mathematics

In Statistics and Probability

Citations in the corpus

PLS-SEM: Looking Back and Moving Forward (2014, methodology): “After special issues on the PLS-SEMmethod appeared in journals such asMIS Quarterly (Marcoulides et al., 2009) and the Journal of Marketing Theory and Practice (Hair et al., 2011b), Long Range Planning – as a primary journal in the strategic management field – devoted two special issues (Hair et al.”
The role of privacy fatigue in online privacy behavior (2018, neutral): “Moreover, it is suggested that PLS is especially useful for developing models that include higher-order factors (Lowry & Gaskin, 2014; Marcoulides, Chin, & Saunders, 2009).”
Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly (2012, methodology): “By relating Gefen et al.’s (2011) reporting guidelines to actual practice, we attempt to identify potential problematic areas in PLS-SEM use, problems which may explain some of the criticism of how it has been applied (e.g., Marcoulides et al. 2009; Marcoulides and Saunders 2006).”
When to use and how to report the results of PLS-SEM (2019, background): “This practice has unfortunately damaged the reputation of PLS-SEM to some extent (Marcoulides et al., 2009).”
SMEs' adoption of enterprise applications: A technology-organisation-environment model (2013, methodology): “Also, researchers in this field rely greatly on PLS for testing path models (Marcoulides et al., 2009) and theory confirmation (Chin, 1998).”
Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It (2014, methodology): “However, this heuristic has recently been criticized as too lenient and used too often in PLS research [52].”
On the use of partial least squares path modeling in accounting research (2011, background): “As noted by McDonald (1996) and Marcoulides et al. (2009), this can be confusing in that latent variables in covariance-based SEM are true latent variables that cannot be formed as weighted sums of its indicators, while component-based SEM form representations of a theoretical construct through a…”
Incorporating Formative Measures into Covariance-Based Structural Equation Models (2011, methodology): “Specifically, “sample size for PLS models requires a power analysis based on the portion of the model with the largest number of predictors” (Marcoulides et al. 2009, p. 174; see also Chin and Newsted 1999).”