Fischer, Andreas, Lichters, Marcel, and Gudergan, Siedfried P. (2023) Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation. In: Springer Proceedings in Business and Economics. pp. 23-29. From: PLS 2022: 2022 International Conference on Partial Least Squares Structural Equation Modeling, 6-9 September 2022, Cluj-Napaca, Romania.

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Abstract

The aim of this chapter is to showcase the effectiveness of partial least squares structural equation modeling (PLS-SEM) in estimating choices based on data derived from discrete choice experiments. To achieve this aim, we employ a PLS-SEM-based discrete choice modelling approach to analyze data from a large study in the German healthcare sector. Our primary focus is to reveal distinct customer segments by exploring variations in their preferences. Our results demonstrate similarities to other segmentation techniques, such as latent class analysis in the context of multinomial logit analysis. Consequently, employing PLS-SEM to examine data from discrete choice experiments holds great promise in deepening our understanding of consumer choices.

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