| Summary: | <p class="03Abstract" style="margin: 0cm 0cm 0pt;"><span lang="EN-GB"><span style="font-family: Calibri; font-size: x-small;">D'Elia and Piccolo (2005) have recently proposed a mixture distribution, named CUB, for ordinal data. The use of such a mixture distribution for modelling ratings is justified by the following consideration: the judgment that a subject expresses is the result of two components, uncertainty and selectiveness. The possibility of relating the parameters of CUB models to covariates makes the formulation interesting for practical applications</span></span></p><span style="font-family: "Times New Roman","serif"; font-size: 12pt; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: IT; mso-fareast-language: IT; mso-bidi-language: AR-SA;" lang="IT">In this case study, a sample of 224 fair-trade coffee consumers were interviewed at stores. With this data-set, CUB model split consumers, according to their preferences, in two different segments: one showing high price elasticity, and one with a low price elasticity. As regards the potential of the CUB model, it showed a considerable integration capacity with stochastic utility models, namely latent class models. Indeed, by using the segmentation factors emerging from the CUB as covariates of segmentation in a latent class model and setting the number of classes equal to those emerging from the CUB, it was possible to estimate a model which not only validated the findings of the CUB but also allowed estimation of the WTP for the fair trade characteristic in the different groups.</span>
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