When to use and how to report the results of PLS-SEM

Bibliographic Data

ID23313342
AuthorsJoseph F Hair (0000-0002-9226-9693, University of South Alabama), Jeffrey J Risher (0000-0002-0359-2668, University of Mobile), Marko Sarstedt (0000-0002-5424-4268, Otto-von-Guericke-Universität Magdeburg), Christian M Ringle (0000-0002-7027-8804, Universität Hamburg)
Year2019
Volume31
Issue1
Pages2-24
Publication date2019-01-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEuropean Business Review (JOURNAL)
Journal identifiersISSN: 0955-534X • E-ISSN: 1758-7107
PublisherEmerald (PUBLISHER)
DOI10.1108/ebr-11-2018-0203
OpenAlexW2905811773
LanguageEN
Citations received3.639
References cited91

Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

Algorithm · Data mining · Machine learning · Originality · Partial least squares regression · Robustness (evolution) · Rule of thumb · Sample (material) · Sample size determination · Statistics · Structural equation modeling · Advanced Text Analysis Techniques · Artificial Intelligence · Computer Science · Customer Service Quality and Loyalty · Mathematics · Psychology · Technology Adoption and User Behaviour

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