Evaluating the use of structural equation modeling in online purchase intention research: A comprehensive review (2000−2023)

Technological advances—and, more recently, the COVID-19 pandemic—have accelerated the growth of online purchasing, prompting a surge in empirical research on online consumer behavior. A substantial share of these studies rely on structural equation modeling (SEM) for data analysis. However, inadequate or improper application of SEM can produce unreliable results and misleading conclusions, undermining scientific progress and managerial decision-making. To address this critical concern, the present study provides, to the best of our knowledge, the first comprehensive assessment of SEM applications—encompassing both covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM)— in online purchase intention (OPI) research. Our review covers 120 empirical articles published between 2000 and 2023 and reveals that methodological requirements of SEM are often overlooked, which risks invalidating both theoretical contributions and managerial implications. In response, we offer practical recommendations and a results-based guide to assist researchers and reviewers in enhancing the rigor, reliability, and decision-oriented value of SEM studies in this field. • E-commerce is growing rapidly, and likewise, empirical research on online purchasing • 120 articles in the last 24 years using SEM for data analysis are analyzed • Misuse of SEM could undermine the validity of theoretical and practical implications • The findings show that very often some SEM requirements are overlooked • A set of guidelines for correctly implementing SEM methodology is offered