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

In: State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM)

Author

Listed:
  • 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
    Vienna University of Economics and Business
    Aalto University School of Business, Aalto University)

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.

Suggested Citation

  • Andreas Fischer & Marcel Lichters & Siegfried P. Gudergan, 2023. "Partial Least Squares Structural Equation Modeling-Based Discrete Choice Modeling: An Illustration in Modeling Hospital Choice with Latent Class Segmentation," Springer Proceedings in Business and Economics, in: Lăcrămioara Radomir & Raluca Ciornea & Huiwen Wang & Yide Liu & Christian M. Ringle & Marko Sarstedt (ed.), State of the Art in Partial Least Squares Structural Equation Modeling (PLS-SEM), pages 23-29, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-34589-0_4
    DOI: 10.1007/978-3-031-34589-0_4
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