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B0464
Title: Modelling perceived choice variety by a mixture model for rating data Authors:  Marica Manisera - University of Brescia (Italy) [presenting]
Paola Zuccolotto - University of Brescia (Italy)
Eugenio Brentari - University of Brescia (Italy)
Abstract: In consumer research, marketing, public policy and other fields, individuals choice depends on the number of possible alternatives. In addition, according to the literature, the choice satisfaction is influenced not only by the number of options but also by the perceived variety. The aim is to apply a novel statistical approach to model perceived variety, in order to better understand the perceptions of individuals about the variety of the possible choice options. We resort to the class of CUB (Combination of Uniform and Binomial random variables) models, in particular to the Nonlinear extension of CUB, in order to (i) provide a measure for perceived variety, (ii) add a measure of uncertainty, (iii) give insights on the state of mind of respondents toward the response scale. The application of the Nonlinear CUB to real data shows interesting results.