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

By Joseph F. Hair, Christian M. Ringle, Siegfried P. Gudergan, Andrea Fischer, Christian Nitzl, C. Menictas. Business Research, 2018

546 citations recorded by the source corpus.

AbstractCommonly 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.

Subfields: Economics and Econometrics, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/e01a83e5fa5e6afbbfb67020578cf0faa0d4cb59

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Citations in the corpus

Structural model robustness checks in PLS-SEM (2020, methodology): “When the relationship between two constructs is nonlinear, the size of the effect between two constructs not only depends on the magnitude of the change in the exogenous construct but also on its value (Hair et al., 2018b).”
Multigroup Analysis using SmartPLS: Step-by-Step Guidelines for Business Research (2020, unknown): “…of correlations (Henseler et al., 2015), the development of consistent PLS (PLSc) (Dijkstra and Henseler, 2015), discrete choice modelling in PLS (Hair et al., 2019b), weighted PLS (Cheah et al., 2020), necessary condition analysis (Richter et al., 2020), the endogeneity test in PLS (Hult et…”