Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares

Abstract

Partial least squares (PLS) estimates of structural equation model path coefficients are believed to produce more accurate estimates than those obtained with covariance structure analysis (CVA) using maximum likelihood estimation (MLE) when one or more of the MLE assumptions are not met. However, there exists no empirical support for this belief or for the specific conditions under which it will occur. MLE-based CVA will also break down or produce improper solutions whereas PLS will not. This study uses simulated data to estimate parameters for a model with five independent latent variables and one dependent latent variable under various assumption conditions. Data from customer satisfaction studies were used to identify the form of typical field-based survey distributions. Our results show that PLS produces more accurate path coefficients estimates when sample sizes are less than 500, independent latent variables are correlated, and measures per latent variable are less than 4. Method accuracy does not vary when the MLE multinormal distribution assumption is violated or when the data do not fit the theoretical structure very well. Both procedures are more accurate when the independent variables are uncorrelated, but MLE estimations break down more frequently under this condition, especially when combined with sample sizes of less than 100 and only two measures per latent variable.