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

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          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.

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          An Introduction to the Bootstrap

          Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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            Prospect Theory: An Analysis of Decision under Risk

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              An assessment of the use of partial least squares structural equation modeling in marketing research

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Business Research
                Bus Res
                Springer Science and Business Media LLC
                2198-3402
                2198-2627
                April 2019
                August 28 2018
                April 2019
                : 12
                : 1
                : 115-142
                Article
                10.1007/s40685-018-0072-4
                6b3ed81d-30a9-4f52-9da8-c25f90784cdb
                © 2019

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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