Partial least squares structural equation modeling-based discrete choice modeling : an illustration in modeling hospital choice with latent class segmentation

Start Page
23
End Page
29
Citation
2022 International Conference on Partial Least Squares Structural Equation Modeling (PLS2022)
Contribution to Conference
2022 International Conference on Partial Least Squares Structural Equation Modeling, PLS2022  
Publisher DOI
10.1007/978-3-031-34589-0_4
Scopus ID
2-s2.0-85174464599
Publisher
Springer International Publishing
ISBN
978-3-031-34588-3
978-3-031-34589-0
978-3-031-34590-6
978-3-031-34591-3
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.
Subjects
Choice-based conjoint analysis
Choice modeling
Hospital choice
Latent class analysis
PLS-SEM
Segmentation
DDC Class
300: Social Sciences
330: Economics