This chapter, authored by Jörg Henseler and Georg Fassott, focuses on testing moderating effects in Partial Least Squares (PLS) path models. As scientific disciplines, particularly social sciences, have become more complex, researchers are increasingly interested in examining moderating effects, which are influenced by variables that alter the strength or direction of relationships between exogenous and endogenous variables. The authors provide a comprehensive guide on how to identify and quantify moderating effects within PLS path models, addressing common questions such as how to represent moderating effects in models, the impact of measurement model types, data preparation, coefficient estimation, and significance testing. They also introduce a flow chart that outlines the necessary steps and decisions for testing moderating effects, from data analysis to bootstrapping and model interpretation. The chapter emphasizes the importance of moderating effects in causal models and provides a structured approach to their detection and estimation, drawing on existing knowledge from multiple regression modeling.This chapter, authored by Jörg Henseler and Georg Fassott, focuses on testing moderating effects in Partial Least Squares (PLS) path models. As scientific disciplines, particularly social sciences, have become more complex, researchers are increasingly interested in examining moderating effects, which are influenced by variables that alter the strength or direction of relationships between exogenous and endogenous variables. The authors provide a comprehensive guide on how to identify and quantify moderating effects within PLS path models, addressing common questions such as how to represent moderating effects in models, the impact of measurement model types, data preparation, coefficient estimation, and significance testing. They also introduce a flow chart that outlines the necessary steps and decisions for testing moderating effects, from data analysis to bootstrapping and model interpretation. The chapter emphasizes the importance of moderating effects in causal models and provides a structured approach to their detection and estimation, drawing on existing knowledge from multiple regression modeling.