Partial least squares structural equation modeling-based discrete choice modeling
An illustration in modeling retailer choice
Bibliographic Data
| ID | 23344736 |
|---|---|
| Authors | Joseph F Hair (0000-0002-9226-9693, University of South Alabama), Christian M Ringle (0000-0002-7027-8804, Universität Hamburg, corresponding author), Siegfried P Gudergan (0000-0002-5493-4664, University of Waikato), Andreas Fischer (0000-0001-7349-7722, Universität Hamburg), Christian Nitzl (0000-0001-8480-3916, Universität der Bundeswehr München), Con Menictas |
| Year | 2019 |
| Volume | 12 |
| Issue | 1 |
| Pages | 115-142 |
| Publication date | 2019-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Business Research (JOURNAL) |
| Journal identifiers | ISSN: 2198-3402 • E-ISSN: 2198-2627 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s40685-018-0072-4 |
| OpenAlex | W2889471592 |
| Language | EN |
| Citations received | 78 |
| References cited | 78 |
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.
Discrete choice · Econometrics · Heuristic · Latent variable · Logistic regression · Logit · Mathematical optimization · Mixed logit · Multinomial logistic regression · Multinomial probit · Partial least squares regression · Probit · Probit model · Statistics · Structural equation modeling · Variable (mathematics) · Computer Science · Consumer Retail Behavior Studies · Customer Service Quality and Loyalty · Economic and Environmental Valuation · Mathematics
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| Unique citing works | 78 |
|---|---|
| Citations per year | 13 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 65 |