This paper presents a critical review of the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in 77 human resource management (HRM) studies published between 1985 and 2014. The authors identify several areas where PLS-SEM can be improved in HRM research. They discuss the utility of PLS-SEM for HRM research across universal, contingency, configurational, and contextual modes of theorizing. The authors also offer guidelines for the appropriate application of PLS-SEM in HRM research. They then use these guidelines to assess how PLS-SEM is used in top HRM and employment relations journals. The review reveals considerable variation in the application of PLS-SEM in HRM research. The authors highlight several areas that offer room for improvement in future HRM studies. They also discuss the theoretical and methodological implications of using PLS-SEM in HRM research. The authors emphasize the importance of using PLS-SEM for its predictive focus and the ability to handle complex models with many indicators and constructs. They also highlight the importance of using PLS-SEM for its ability to handle small sample sizes and derive determinate latent variable scores. The authors conclude that PLS-SEM is a valuable method for HRM research, but there is a need for further research to improve its application in HRM studies.This paper presents a critical review of the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) in 77 human resource management (HRM) studies published between 1985 and 2014. The authors identify several areas where PLS-SEM can be improved in HRM research. They discuss the utility of PLS-SEM for HRM research across universal, contingency, configurational, and contextual modes of theorizing. The authors also offer guidelines for the appropriate application of PLS-SEM in HRM research. They then use these guidelines to assess how PLS-SEM is used in top HRM and employment relations journals. The review reveals considerable variation in the application of PLS-SEM in HRM research. The authors highlight several areas that offer room for improvement in future HRM studies. They also discuss the theoretical and methodological implications of using PLS-SEM in HRM research. The authors emphasize the importance of using PLS-SEM for its predictive focus and the ability to handle complex models with many indicators and constructs. They also highlight the importance of using PLS-SEM for its ability to handle small sample sizes and derive determinate latent variable scores. The authors conclude that PLS-SEM is a valuable method for HRM research, but there is a need for further research to improve its application in HRM studies.