Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2575
Publisher DOI: 10.1007/s40685-018-0072-4
Title: Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice
Language: English
Authors: Hair, Joseph F. 
Ringle, Christian M.  
Gudergan, Siegfried 
Fischer, Andreas 
Nitzl, Christian 
Menictas, Con 
Issue Date: 28-Aug-2019
Publisher: Springer
Source: Business Research 12: 115-142 (2019)
Journal or Series Name: Business Research 
Abstract (english): 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.
URI: http://hdl.handle.net/11420/2816
DOI: 10.15480/882.2575
ISSN: 2198-3402
Institute: Personalwirtschaft und Arbeitsorganisation W-9 
Type: (wissenschaftlicher) Artikel
Funded by: Ringle acknowledges a financial interest in SmartPLS. Gudergan also acknowledges the support provided by the Australian Research Council (ARC LP0455822).
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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