Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice

Joseph F. Hair (), Christian Ringle, Siegfried P. Gudergan (), Andreas Fischer (), Christian Nitzl () and Con Menictas ()
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Joseph F. Hair: University of South Alabama
Siegfried P. Gudergan: University of Waikato
Andreas Fischer: Hamburg University of Technology (TUHH)
Christian Nitzl: University of the German Federal Armed Forces Munich
Con Menictas: Strategic Precision Pty Ltd

Business Research, 2019, vol. 12, issue 1, No 7, 115-142

Abstract: 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.

Keywords: Discrete choice modeling; Experiments; Structural equation modeling; Partial least squares; Path modeling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (32)

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DOI: 10.1007/s40685-018-0072-4

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