Mathematical Tool PLS-SEM Aids in Analyzing Cause-Effect Relationships to Explain and Predict Future Behavior in Business Research and Other Disciplines

People are complex and the factors that drive their decision-making are manifold. Teasing out the reasoning behind why people think and act the way they do is the crux to human resource management research. The question is now, how do researchers develop new concepts and theories in this field? One highly favored method is partial least squares structural equation modeling (PLS-SEM). The idea behind SEM is simple—researchers construct a model for a phenomenon wherein the different features within the particular context are related to one another. Each general and not directly observable construct (or latent variable) is determined by a measurement model of observable indicators (e.g., determining intelligence through test scores). This structure is thereafter solved by using mathematical equations. Hence, PLS-SEM provides a way to solve such a model with data and to determine the cause-effect relationships between the chosen constructs for explanation and prediction.

The myriad advantages of PLS-SEM are obvious to Prof. Dr. Christian M. Ringle, the Director of the Institute of Human Resource Management and Organizations (HRMO), at the Hamburg University of Technology (TUHH). Prof. Ringle’s research focuses on helping researchers and decision-makers to fully exploit the potential of PLS-SEM, be it through highlighting common errors and biases in the technique or through the application of the SmartPLS software, which provides a user-friendly modelling interface. From business and marketing research to evaluating human resources within an organization, PLS-SEM offers a powerful solution to explaining what makes people tick and predicting what they might do next.