The Elephant in the Room: Evaluating the Predictive Performance of Partial Least Squares (PLS) Path Models

Abstract

Attempts to introduce predictive performance metrics into Partial Least Squares (PLS) path modeling have been slow and fall short of demonstrating impact on both practice and scientific development in PLS. This study contributes to PLS development by offering a comprehensive framework that identifies different dimensions of prediction and their effect on predictive performance evaluation with PLS. We use this framework to contextualize prior efforts in PLS and prediction, and to highlight potential opportunities and challenges. Our second contribution is to formally propose procedures to generate and evaluate different types of predictions from PLS models. The procedures account for the best practices identified in our framework. Our third contribution is outlining the many powerful ways in which predictive PLS methodologies can strengthen theory­ building research. Our framework, procedures, and research guidelines will hopefully form the basis for a more informed and unified development of the rigorous theoretical and practical applications of PLS.