Files
Abstract
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
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.