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dc.contributor.authorHair, Josephen_NZ
dc.contributor.authorRingle, Christian M.en_NZ
dc.contributor.authorGudergan, Siegfried P.en_NZ
dc.contributor.authorFischer, Andreasen_NZ
dc.contributor.authorNitzi, Christianen_NZ
dc.contributor.authorMenictas, Conen_NZ
dc.date.accessioned2019-02-25T20:22:24Z
dc.date.available2018en_NZ
dc.date.available2019-02-25T20:22:24Z
dc.date.issued2018en_NZ
dc.identifier.citationHair, J., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzi, C., & Menictas, C. (2018). Partial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choice. Business Research, online. https://doi.org/10.1007/s40685-018-0072-4en
dc.identifier.issn2198-3402en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12375
dc.description.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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s40685-018-0072-4
dc.rights© The Author(s) 2018
dc.subjectDiscrete choice modeling
dc.subjectExperiments
dc.subjectStructural equation modeling
dc.subjectPartial least squares
dc.subjectPath modeling
dc.titlePartial least squares structural equation modeling-based discrete choice modeling: An illustration in modeling retailer choiceen_NZ
dc.typeJournal Article
dc.identifier.doi10.1007/s40685-018-0072-4en_NZ
dc.relation.isPartOfBusiness Researchen_NZ
pubs.elements-id235463
pubs.publisher-urlhttps://link.springer.com/article/10.1007/s40685-018-0072-4en_NZ
pubs.volumeonlineen_NZ


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