Random Regret Minimization for consumer choice research.
This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.
Chorus, C. G., van Cranenburgh, S. & Dekker, T. (2014), Random Regret Minimization for consumer choice research. Journal of Business Research, 67, pp 2428-2436.
Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives.
An increasing number of studies of choice behaviour are looking at Random Regret Minimisation (RRM) as an alternative to the well established Random Utility Maximisation (RUM) framework. Empirical evidence tends to show small differences in performance between the two approaches, with the implied preference between the models being dataset specific. In the present paper, we discuss how in the context of choice tasks involving an opt out alternative, the differences are potentially more clear cut. Specifically, we hypothesise that when opt out alternatives are framed as a rejection of all the available alternatives, this is likely to have a detrimental impact on the performance of RRM, while the performance of RUM suffers more than RRM when the opt out is framed as a respondent being indifferent between the alternatives on offer. We provide empirical support for these hypotheses through two case studies, using the two different types of opt out alternatives. Our findings suggest that analysts need to carefully evaluate their choice of model structure in the presence of opt out alternatives, while any a priori preference for a given model structure should be taken into account in survey framing.
Hess, S., Beck, M. & Chorus, C. (2014), Contrasts between utility maximisation and regret minimisation in the presence of opt out alternatives. Transportation Research Part A, 66, pp 1-12.
Incorporating needs-satisfaction in a discrete choice model of leisure activities
In this paper we extend the behavioural scope of discrete choice models for leisure activity-travel choices. More specifically, we investigate to what extent choices for leisure activities and related travels are driven by the satisfaction of needs. In addition to conventional attributes (such as activity costs), our regret based discrete choice model incorporates latent variables representing the anticipated level of individual needs-satisfaction by a particular leisure activity. The latent variables are calibrated with the help of subjective indicators of needs-satisfaction associated with the leisure activities. Results show that needs-satisfaction allows us to decompose a substantial share of the unobserved heterogeneity in leisure activity-travel decisions across respondents. Identifying the structural drivers of anticipated needs-satisfaction also enables a better prediction of leisure activity choice.
Dekker, T., Hess, S., Arentze, T. & Chorus, C.G. (2014), Incorporating needs-satisfaction in a discrete choice model of leisure activities, Journal of Transport Geography, 21, pp 36-41.
