The elephant in the room: Predictive performance of PLS 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 either practice or 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. This framework contextualizes prior efforts in PLS and prediction and highlights potential opportunities and challenges. A second contribution to PLS development lies in proposed procedures to generate and evaluate different types of predictions from PLS models. These procedures account for the best practices that the new framework identifies. An outline of the many powerful ways in which predictive PLS methodologies can strengthen theory-building research constitutes a third contribution to PLS development. The framework, procedures, and research guidelines hopefully form the basis for a more informed and unified development of the rigorous theoretical and practical applications of PLS.