Partial least squares is an estimator for structural equation models: A comment on Evermann and Rönkkö (2021)

Florian Schuberth*, Sam Zaza, Jörg Henseler

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

4 Citations (Scopus)
19 Downloads (Pure)

Abstract

In 2012 and 2013, several critical publications questioned many alleged PLS properties. As a consequence, PLS benefited from a boost of developments. It is, therefore, a good time to review these developments. Evermann and Rönkkö (2023) devote their paper to this task and formulate guidelines in the form of 14 recommendations. Yet, while they identified the major developments, they overlook a fundamental change, maybe because it is so subtle: the view on PLS. As mentioned by Evermann and Rönkkö (2023, p. 1), “[PLS] is a statistical method used to estimate linear structural equation models” and consequently should not be regarded as a standalone SEM technique following its own assessment criteria. Against this background, we explain which models can be estimated by PLS and PLSc. Moreover, we present the Henseler-Ogasawara specification to estimate composite models by common SEM estimators. Additionally, we review Evermann and Rönkkö's (2023) 14 recommendations one by one and suggest updates and improvements where necessary. Further, we address their comments about the latest advancement in composite models and show that PLS is a viable estimator for confirmatory composite analysis. Finally, we conclude that there is little value in distinguishing between covariance-based and variance-based SEM—there is only SEM.

Original languageEnglish
Pages (from-to)711-729
JournalCommunications of the Association for Information Systems
Volume52
DOIs
Publication statusPublished - 19 Jun 2023

Keywords

  • 2023 OA procedure

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