PLS-SEM in the SmartPLS 4 Era
Partial least squares structural equation modeling, commonly known as PLS-SEM, allows researchers to examine relationships among constructs that cannot always be measured directly. These latent constructs may include concepts such as trust, technology acceptance, organizational readiness, behavioral intention, or perceived risk.
PLS-SEM is particularly useful when a study emphasizes prediction, explanation, or the identification of important drivers within a complex model. It can also accommodate formative measurement models, mediation, moderation, higher-order constructs, and other relationships commonly encountered in behavioral and social science research.
The fourth edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) brings the widely used methodological guide into the SmartPLS 4 environment. Written by Joseph F. Hair Jr., G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt, the updated edition covers the complete analytical process, from model specification and data examination to measurement-model evaluation, structural-model assessment, and the reporting of results.
One of the edition’s most important developments is its stronger emphasis on prediction. Traditional assessments such as path coefficients and explained variance remain important, but they do not independently establish that a model will predict observations outside the data used to estimate it. Updated coverage of PLSpredict and the cross-validated predictive ability test, or CVPAT, reflects the growing expectation that predictive claims should be evaluated directly.
The new edition also expands its treatment of mediation and moderation, model fit, control variables, measurement invariance, endogeneity, latent classes, necessary condition analysis, and importance-performance map analysis. These additions demonstrate how PLS-SEM has developed beyond a basic alternative to covariance-based structural equation modeling.
That development does not mean PLS-SEM is appropriate for every study. Method selection should follow the research question, theoretical assumptions, measurement approach, and intended conclusions. A small sample, nonnormal data, or convenient software may support a methodological decision, but none is a sufficient justification by itself. When strict theory confirmation and global model fit are central, covariance-based SEM may be more appropriate. In some studies, the two approaches may be complementary.
The fourth edition therefore represents more than a software update. It reflects a broader movement toward clearer methodological justification, stronger predictive assessment, and more transparent reporting. For researchers studying technology, behavior, organizations, and other complex systems, that evolution is worth following.
References
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2027). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (4th ed.). Sage.
SmartPLS. When to Use PLS-SEM (and When Not).