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

Hair, Joseph F., Ringle, Christian M., Gudergan, Siegfried P., Fischer, Andreas, Nitzl, Christian, and Menictas, Con (2019) Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Business Research, 12 (1). pp. 115-142.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1007/s40685-018-0072-...
112


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.

Item ID: 70779
Item Type: Article (Research - C1)
ISSN: 2198-2627
Keywords: Discrete choice modeling, Experiments, Partial least squares, Path modeling, Structural equation modeling
Copyright Information: © The Author(s) 2018
Date Deposited: 19 Jan 2022 01:21
FoR Codes: 35 COMMERCE, MANAGEMENT, TOURISM AND SERVICES > 3506 Marketing > 350606 Marketing research methodology @ 100%
SEO Codes: 15 ECONOMIC FRAMEWORK > 1503 Management and productivity > 150303 Marketing @ 100%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page