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Partial least squares path modeling: Time for some serious second thoughts
Author(s) -
Rönkkö Mikko,
McIntosh Cameron N.,
Antonakis John,
Edwards Jeffrey R.
Publication year - 2016
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2016.05.002
Subject(s) - partial least squares regression , computer science , rule of thumb , structural equation modeling , path (computing) , desk , scale (ratio) , industrial engineering , operations research , econometrics , management science , data mining , artificial intelligence , algorithm , machine learning , mathematics , economics , engineering , programming language , physics , quantum mechanics , operating system
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 desk‐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.