Partial least squares path modeling: Time for some serious second thoughts

Mikko Rönkkö*, Cameron N. McIntosh, John Antonakis, Jeffrey R. Edwards

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

129 Citations (Scopus)

Abstract

Partial least squares (PLS) path modeling is increasingly being promoted as a technique of choice for various analysis scenarios, despite the serious shortcomings of the method. The current lack of methodological justification for PLS prompted the editors of this journal to declare that research using this technique is likely to be deck-rejected (Guide and Ketokivi, 2015). To provide clarification on the inappropriateness of PLS for applied research, we provide a non-technical review and empirical demonstration of its inherent, intractable problems. We show that although the PLS technique is promoted as a structural equation modeling (SEM) technique, it is simply regression with scale scores and thus has very limited capabilities to handle the wide array of problems for which applied researchers use SEM. To that end, we explain why the use of PLS weights and many rules of thumb that are commonly employed with PLS are unjustifiable, followed by addressing why the touted advantages of the method are simply untenable.

Original languageEnglish
Pages (from-to)9-27
Number of pages19
JournalJournal of Operations Management
Volume47-48
DOIs
Publication statusPublished - 1 Nov 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Partial least squares
  • Statistical and methodological myths and urban legends
  • Structural equation modeling

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