Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233139
Authors: 
Hair, Joseph F.
Ringle, Christian M.
Gudergan, Siegfried P.
Fischer, Andreas
Nitzl, Christian
Menictas, Con
Year of Publication: 
2019
Citation: 
[Journal:] Business Research [ISSN:] 2198-2627 [Volume:] 12 [Year:] 2019 [Issue:] 1 [Pages:] 115-142
Abstract: 
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.
Subjects: 
Discrete choice modeling
Experiments
Structural equation modeling
Partial least squares
Path modeling
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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