TY - JOUR
T1 - Partial least squares structural equation modeling-based discrete choice modeling
T2 - an illustration in modeling retailer choice
AU - Hair, Joseph F.
AU - Ringle, Christian M.
AU - Gudergan, Siegfried P.
AU - Fischer, Andreas
AU - Nitzl, Christian
AU - Menictas, Con
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Discrete choice modeling
KW - Experiments
KW - Partial least squares
KW - Path modeling
KW - Structural equation modeling
U2 - 10.1007/s40685-018-0072-4
DO - 10.1007/s40685-018-0072-4
M3 - Journal article
AN - SCOPUS:85060632950
SN - 0341-2687
VL - 12
SP - 115
EP - 142
JO - Schmalenbach Journal of Business Research
JF - Schmalenbach Journal of Business Research
IS - 1
ER -