Robustness testing of PLS, LISREL, EQS and ANN-based SEM for measuring customer satisfaction

Abstract Researchers have shown the Customer Satisfaction Index (CSI) can serve as a predictor for companies' profitability and market value. To measure a CSI model, we have to use a Structure Equation Model (SEM) technique. There are two types of SEM techniques – covariance-based (e.g. LISREL, EQS or AMOS) and component-based SEM techniques (e.g. Partial Least Square). With the growing importance of a CSI model, we must determine which SEM technique can better measure a CSI model. In addition, with the increasing complexity of a theoretical model (e.g. non-linear relations between variables), researchers have called for new SEM techniques that could address this issue. Hackle & Westlund (2000) Hackle, P. and Westlund, A. 2000. On structural equation modelling for customer satisfaction measurement. Total Quality Management, 11: 820–825. Taylor & Francis Online , Google Scholar contended that the Artificial Neural Network (ANN)-based SEM technique could be superior to traditional SEM techniques because it can measure non-linear relations by using different activity functions and layers of hidden nodes. Thus, this study extends previous research in several directions. First, we conduct the robustness testing of both covariance-based (LISREL and EQS) and component-based (PLS) SEM techniques, not only on a simulated CSI-like model but also on a real-life CSI model. Second, we explore the feasibility of an ANN-based SEM technique.