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Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice
Citation Link: https://doi.org/10.15480/882.2575
Publikationstyp
Journal Article
Date Issued
2019-08-28
Sprache
English
TORE-DOI
TORE-URI
Journal
Volume
12
Start Page
115
End Page
142
Citation
Business Research 12: 115-142 (2019)
Publisher DOI
Scopus ID
Publisher
Springer
Commonly used discrete choice model analyses (e.g., probit, logit and multinomial logit models) draw on the estimation of importance weights that apply to different attribute levels. But directly estimating the importance weights of the attribute as a whole, rather than of distinct attribute levels, is challenging. This article substantiates the usefulness of partial least squares structural equation modeling (PLS-SEM) for the analysis of stated preference data generated through choice experiments in discrete choice modeling. This ability of PLS-SEM to directly estimate the importance weights for attributes as a whole, rather than for the attribute’s levels, and to compute determinant respondent-specific latent variable scores applicable to attributes, can more effectively model and distinguish between rational (i.e., optimizing) decisions and pragmatic (i.e., heuristic) ones, when parameter estimations for attributes as a whole are crucial to understanding choice decisions.
DDC Class
330: Wirtschaft
More Funding Information
Ringle acknowledges a financial interest in SmartPLS. Gudergan also acknowledges the support provided by the Australian Research Council (ARC LP0455822).
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