Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation

Andreas Fischer (), Marcel Lichters () and Siegfried P. Gudergan ()
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Andreas Fischer: Institute of Human Resource Management and Organizations, Hamburg University of Technology
Marcel Lichters: Chemnitz University of Technology
Siegfried P. Gudergan: College of Business, Law & Governance, James Cook University

A chapter in State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), 2023, pp 23-29 from Springer

Abstract: 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.

Keywords: PLS-SEM; Choice modeling; Segmentation; Latent class analysis; Choice-based conjoint analysis; Hospital choice (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-34589-0_4

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DOI: 10.1007/978-3-031-34589-0_4

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