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

      Hair, Joseph; Ringle, Christian M.; Gudergan, Siegfried P.; Fischer, Andreas; Nitzi, Christian; Menictas, Con
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      Hair2018_Article_PartialLeastSquaresStructuralE.pdf
      Published version, 720.5Kb
      DOI
       10.1007/s40685-018-0072-4
      Link
       link.springer.com
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      Citation
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      Hair, 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/s40685-018-0072-4
      Permanent Research Commons link: https://hdl.handle.net/10289/12375
      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.
      Date
      2018
      Type
      Journal Article
      Publisher
      Springer
      Rights
      © The Author(s) 2018
      Collections
      • Management Papers [1135]
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