Design of PLS-Based Satisfaction Studies

This chapter expands on contributions from previous studies on design of PLS structural equation modeling with respect to satisfaction studies in general to provide the reader with recommendations on all aspects included in designing PLS-based satisfaction studies.

Abstract

In this chapter we focus on design of PLS structural equation modeling with respect to satisfaction studies in general. Previous studies have found the PLS technique to be affected by things as the skewness of manifest variables, multicollinearity between latent variables, misspecification, question order, sample size as well as the size of the path coefficients (Cassel et al. ; Auh et al. ; Eskildsen and Kristensen 2005; Kristensen and Eskildsen ). In this chapter we expand on these contributions in order to provide the reader with recommendations on all aspects included in designing PLS-based satisfaction studies.The recommendations are based on an empirical PLS project conducted at the Aarhus School of Business, Center for Corporate Performance. Within this project five different studies have been conducted that cover a variety of aspects of designing PLS-based satisfaction studies.The data used in subsequent sections comes from a variety of sources. In relation to the empirical PLS project at the Aarhus School off Business the following five different studies have been conducted: Scale study Empirical experiment Simulation study – data collection Simulation study – missing values Empirical study of model specification for a customer satisfaction model