Introducing an Optimal (and a Simpler) Approach to Partial Least Squares Analyses

Abstract

Abstract-The purpose of this study is to show the many possibilities that partial least squares (PLS) analysis offers, as well as its ease of use. This analysis is a useful tool, because it brings an additional point of view to statistical analysis beyond that of structural equation modeling analysis. Here, the authors suggest using a different approach to PLS, called "optimal PLS." It combines principal component analysis and PLS analysis to compute the data; by convergent iterations, this approach produces an optimal model not based on a reference model to best explain a given situation. The study illustrates this approach with two practical applications that create optimal models from the ground up: one in management controlling and the other in marketing. The software, which is used as a computational tool, has an algorithm based on optimal PLS. The study is original, because it chooses two opposing fields of research, namely management controlling (a quantitative discipline) and consumer behavior research (a qualitative discipline), in an attempt to understand when optimal PLS provides reliable results. The authors conclude that the use of PLS is multifaceted, and optimal PLS has a high capacity to explain the actual components, which helps researchers and analysts reach appropriate strategic decisions. With regard to the study's practical implications, the overview and the accompanying explanations will enable academics and analysts to use PLS analysis more easily by means of optimal PLS approach's five steps. They can add PLS and optimal PLS to their list of analytical tools to bring fresh points of view to their research.Index Terms-Partial least squares path modeling, optimal PLS, optimal strategy, marketing research, consumer behavior, management controlling, algorithm, software.