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

Jose Benitez (1, 2) , Jörg Henseler (3, 4) , Ana Castillo (2) , Florian Schuberth (4)

1 ESC [Rennes] - ESC Rennes School of Business
2 UGR - Universidad de Granada = University of Granada
3 University of Twente
4 NOVA - Universidade Nova de Lisboa = NOVA University Lisbon

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.

Mots clés: Partial least squares path modeling; Guidelines; Model validation; Composite model; Confirmatory and explanatory information systems research

Domaines: Théorie de l'information [cs.IT]; Sciences de l'information et de la communication

Jose Benitez, Jörg Henseler, Ana Castillo, Florian Schuberth. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information and Management, 2020, 57 (2), pp.103168. ⟨10.1016/j.im.2019.05.003⟩. ⟨hal-02567449⟩