Press briefing
Highlighting the Significance of the Mathematical Tool PLS-SEM in Predicting Customer Behavior
13th September, Hamburg – understanding why customers act the way they do and predicting their future actions is one of the biggest questions of business and market researchers. But how do you bridge the gap between explaining in hindsight the factors that led to their decisions and predicting the outcome of new decisions? The answer is simple: Partial least squares structural equation modeling (PLS-SEM). This methodology is a powerful mathematical technique and one of the mainstays in business research. Just ask Prof. Dr. Christian M. Ringle, Director of the Institute of Human Resource Management and Organizations (HRMO), at the Hamburg University of Technology (TUHH). Dr. Ringle’s research is focused 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 user-friendly SmartPLS software. He has co-authored most of the seminal texts on PLS-SEM.
The idea behind SEM is simple—researchers construct a model for a phenomenon wherein the different features of the particular context are related to one another . This structure is thereafter solved by using mathematical equations. Hence, PLS-SEM provides a way to solve such a model, and explain and predict the cause-effect relationships between its many variables. Furthermore, PLS-SEM is especially useful for including latent variables. Those are variables that are indirectly determined from another measure (e.g., determining intelligence through test scores).
Unlike most conventional models, PLS-SEM can work efficiently with complex models and allows to assess their predictive power. It also uses less restrictive assumptions for the modeling, which allows it to address a broader range of problems than most conventional modelling techniques. With the proper application, PLS-SEM provides manifold benefits to the pursuit of understanding complex human decision-making. From business to marketing research, evaluating human resources within an organization , understanding what makes people tick, and predicting what they might do next, by using PLS-SEM, is sure to be a breeze!
