A new approach for modelling consumers’ preferences

Cognitive Dissonance in the Self-assessed Health in Brazil: A CUB Model Analysis Using 2013 National Health Survey Data

This study ai ms to verify and analyze the existence of cognitive dissonance in the self-assessment of health by individuals in Brazil, that is, the difference between self-rated health and the health status of individuals. To accomplish this, we use data from the 2013 National Health Survey, which collected the self-assessments that individuals made of their health and information about their health status. This information was used to build indices that seek to represent a person's health status in relation to chronic illnesses, physical and mental well-being, eating habits and lifestyle. To identify the presence of cognitive dissonance, the CUB (Combination of a discrete Uniform and shifted Binomial distributions) model was used, which relates self-assessed health with the developed indices. Cognitive dissonance was identified in self-assessed health in relation to eating habits and lifestyle, and this dissonance may be associated with a present bias in the self-assessment of health in Brazil.

Consumer perception of attributes of organic food in Italy: A CUB model study

Organic food, consumers and their buying behaviour are well examined fields of research, although there is a lack of consistent findings on consumers' perception about organic food's quality, in terms of healthiness, safety, and environmental sustainability, and on determinants of perceived quality. This study investigates how consumers perceive the quality of organic food, in terms of environmental sustainability, safety, and healthiness. The study also analyses how and to what extent perceived quality of organic food is influenced by the presence of information related to quality on food products' labels and consumers' socio-demographic profile. A survey has been conducted on a convenience sample of Italian consumers, recruited through a snowball sampling technique. An approach based on a Combination of Uniform and shifted Binomial random variables, named CUB model, is adopted to analyse consumers' perceptions in terms of two latent components, feeling and uncertainty. The CUB model approach is suitable for analyses that involve consumers perception. The results suggest that consumers perceive safety of organic food better than healthiness and environmentally sustainable attributes. Findings also highlight that the presence of specific information on food's label contributes to perceive organic food as healthier, safe, and environmentally sustainable: the more the details on food labels, the higher the consumers' perception. Furthermore, consumers' socio-demographic profile plays a significant role: males and females have a different perception of organic food and younger consumers tend to be more prone to buy and consume organic product.

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Consumers are more confident with healthiness and sustainability of organic food.

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Males and females have a different perception of organic food.

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Food labels increase the perception of organic food as healthy, safe, sustainable.

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More details on labels of organic food enhance consumers' perception.

Analyzing bivariate ordinal data with CUB margins

Statistical modelling for ordinal data has received a considerable attention in the literature, and a consolidated theory relying on Generalized Linear Model approach has been developed. In this article, we present an innovative technique for modelling bivariate ordinal data. In particular, we consider the method introduced by Plackett for constructing a one-parameter bivariate distribution from given margins, and we apply it in order to represent correlated ordinal variables which individually follows a CUB model. This is a univariate mixture distribution defined by the convex Combination of a Uniform and a shifted Binomial distribution whose parameters may be related to rater's covariates. The article shows how the bivariate distribution can be defined and how its characterizing parameter, which describes the association between the component random variables, can be related to the subject's covariates. The proposed approach is applied to the study of two key drivers of extra virgin olive oil consumption in Italy. The technique allows a representation of the data whose meaning can be easily interpreted providing useful information for management support.