This paper critically reviews the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in 77 Human Resource Management (HRM) studies published over a 30-year period. PLS-SEM is a multivariate analysis technique that has become increasingly popular in HRM research due to its ability to handle complex models, estimate formatively specified constructs, and manage small sample sizes. The review identifies several areas where improvements can be made in the application of PLS-SEM in HRM studies, including the appropriate use of reflective and formative measurement models, the evaluation of construct validity, and the assessment of model predictive power. The findings offer guidance for future research, emphasizing the importance of adhering to established guidelines and incorporating recent methodological advancements. The study also highlights the need for better reporting practices, such as providing detailed information on computational options and software used. Overall, the review underscores the relevance of PLS-SEM in HRM research and suggests ways to enhance its application and interpretation.This paper critically reviews the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in 77 Human Resource Management (HRM) studies published over a 30-year period. PLS-SEM is a multivariate analysis technique that has become increasingly popular in HRM research due to its ability to handle complex models, estimate formatively specified constructs, and manage small sample sizes. The review identifies several areas where improvements can be made in the application of PLS-SEM in HRM studies, including the appropriate use of reflective and formative measurement models, the evaluation of construct validity, and the assessment of model predictive power. The findings offer guidance for future research, emphasizing the importance of adhering to established guidelines and incorporating recent methodological advancements. The study also highlights the need for better reporting practices, such as providing detailed information on computational options and software used. Overall, the review underscores the relevance of PLS-SEM in HRM research and suggests ways to enhance its application and interpretation.