How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

• Published in: Information & management Vol. 57; no. 2; p. 103168
• Main Authors: Benitez, Jose, Henseler, Jörg, Castillo, Ana, Schuberth, Florian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020; Elsevier
• Subjects: Composite model; Computer Science; Confirmatory and explanatory information systems research; Guidelines; Humanities and Social Sciences; Information Theory; Library and information sciences; Model validation; Partial least squares path modeling; Confirmatory and explanatory information systems research; Model validation; Partial least squares path modeling; Composite model; Guidelines
• ISSN: 0378-7206; 1872-7530
• DOI: 10.1016/j.im.2019.05.003

•PLS-PM has been subject to many improvements in last years.•Prior PLS guidelines have not covered the entire recent developments.•We explain how to perform and report an up-to-date empirical analysis with PLS.•We provide a fictive illustrative example on business value of social media. Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.