To read this content please select one of the options below:

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

Joseph F. Hair (Department of Marketing and Quantitative Methods, University of South Alabama, Mobile, Alabama, USA)
Jeffrey J. Risher (Department of Marketing, Supply Chain Logistics and Economics, University of West Florida College of Business, Pensacola, Florida, USA and Department of Business, University of Mobile, Mobile, Alabama, USA)
Marko Sarstedt (Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany and Monash University of Malaysia, Bandar Sunway, Malaysia)
Christian M. Ringle (Institute of Human Resource Management and Organizations, Hamburg University of Technology (TUHH), Hamburg, Germany and the University of Waikato, Hamilton, New Zealand)

European Business Review

ISSN: 0955-534X

Article publication date: 14 January 2019




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.


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.


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.


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.



Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019), "When to use and how to report the results of PLS-SEM", European Business Review, Vol. 31 No. 1, pp. 2-24.



Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Related articles