2004/11/01 by Michael Haenlein, Andreas Kaplan · 2 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Text Analysis Techniques #Analysis of covariance #Applied mathematics #Computer science #Covariance #Econometrics #Engineering #LISREL #Limit (mathematics) #Machine learning #Management science #Mathematics #Multi-Criteria Decision Making #Partial least squares regression #Statistics #Structural equation modeling #Technology Adoption and User Behaviour
paper · doi:10.1207/s15328031us0304_4
openalex publication_date 2004/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Since the introduction of covariance-based structural equation modeling (SEM) by Joreskog in 1973, this technique has been received with considerable interest among empirical researchers. However, the predominance of LISREL, certainly the most well-known tool to perform this kind of analysis, has led to the fact that not all researchers are aware of alternative techniques for SEM, such as partial least squares (PLS) analysis. Therefore, the objective of this article is to provide an easily comprehensible introduction to this technique, which is particularly suited to situations in which constructs are measured by a very large number of indicators and where maximum likelihood covariance-based SEM tools reach their limit. Because this article is intended as a general introduction, it avoids mathematical details as far as possible and instead focuses on a presentation of PLS, which can be understood without an in-depth knowledge of SEM.