Regret Theory as an Alternative Framework in Consumer Food Choice: an Application of the Random Regret Minimization Model

In consumer behaviour literature, discrete choices of food are usually assumed to be driven by maximization of utility, and modelled through the Random Utility Maximization (RUM) choice model. Nevertheless, other behavioural paradigms have been proposed in marketing literature, which account for loss aversion and regret minimization. Hence, this study investigates the usefulness and potential in the food domain of a discrete choice model that follows the regret minimization principle, the Random Regret Minimization (RRM) model, as an alternative and complement to existing RUM models. The study also investigates whether and to what extent a number of personality traits influence the use of a utility-maximizing, or regret-minimizing decision rule. To the best of our knowledge, this is the first attempt to explore whether and how anticipated regret affect consumers’ choice of food products, while in general a direct and significant impact on future choices has been found. The thesis begins with a conceptual model for the food choice process, which takes into account how values related to food and personal factors contribute to drive food choices. Then, consumer choice strategies developed in the literature are introduced and discussed. Based on data gathered from a discrete choice experiment, the RRM discrete choice model is applied and compared with the RUM classical model. Results show that at the aggregate level the two models have similar goodness of fit to the data and prediction ability. Still, each of them shows better fit for particular subgroups of consumers, based on personality traits. Hence, the present study reveals a potential for the RRM model applications in the food domain. Nonetheless, the two models are subject to different interpretations and feature distinct behavioural properties. As such, the RRM can be seen as a valuable addition to existing methodologies in consumer choice modelling, specifically in the food domain.