How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research
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- ISIDORE Id: 10670/1.aa7092...
- hal: hal-02567449
- doi: 10.1016/j.im.2019.05.003
- PII: S0378-7206(17)30939-4
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.im.2019.05.003
http://creativecommons.org/licenses/by-nc/ , info:eu-repo/semantics/OpenAccess
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Partial least squares path modeling Guidelines Model validation Composite model Confirmatory and explanatory information systems researchCiter ce document
Jose Benitez et al., « How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1016/j.im.2019.05.003
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Résumé
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.
