A novel approach to detect attribute by covariate interactions in discrete choice models
Date
2016
Authors
Kwak, K.
Wang, P.
Louviere, J.J.
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Journal article
Citation
Journal of Choice Modelling, 2016; 21(SI):42-47
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Abstract
This paper introduces a novel and simple method to identify attribute by covariate interactions in discrete choice models. This is important because incorporating such interactions in choice models can be an effective way to account for systematic taste variation or “observable preference heterogeneity” across individuals. Using simulated data sets to mimic a well-known phenomenon of selective attention to design attributes, we tested our proposed approach in a banking service context. Our proposed approach was successful in detecting the attribute by covariate interactions implied by the data generation process and outperformed a model with all covariate interactions. The proposed method contributes to the choice modelling literature by providing one of the “tricks of trade” to model observed preference heterogeneity. The simplicity of this approach has advantages for both academics and practitioners in marketing, transportation, healthcare and other fields that use choice modelling.
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Link to a related website: https://opus.lib.uts.edu.au/bitstream/10453/54964/4/2-s2.0-84979608614%20am.pdf, Open Access via Unpaywall
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Copyright 2016 Elsevier Ltd