It is common opinion that traditional approaches used to interpret and model users’ choice behaviour in innovative contexts may lead to neglecting numerous nonquantitative factors that may affect users’ perceptions and behaviours. Indeed, psychological factors, such as attitudes, concerns, and perceptions may play a significant role which should be explicitly modelled. By contrast, collecting psychological factors could be a time and cost consuming activity, and furthermore, real-world applications must rely on theoretical paradigms which are able to easily predict choice/market fractions. The present paper aims to investigate the above-mentioned issues with respect to an innovative automotive technology based on the after-market hybridization of internal combustion engine vehicles. In particular, three main research questions are addressed: (i) whether and how users’ characteristics and attitudes may affect users’ behaviour with respect to new technological (automotive) scenarios (e.g., after-market hybridization kit); (ii) how to better “grasp” users’ attitudes/concerns/perceptions and, in particular, which is the most effective surveying approach to observe users’ attitudes; (iii) to what extent the probability of choosing a new automotive technology is sensitive to attitudes/concerns changes. The choice to install/not install the innovative technology was modelled through a hybrid choice model with latent variables (HCMs), starting from a stated preferences survey in which attitudes were investigated using different types of questioning approaches: direct questioning, indirect questioning, or both approaches. Finally, a comparison with a traditional binomial logit model and a sensitivity analysis was carried out with respect to the instrumental attributes and the attitudes. Obtained results indicate that attitudes are significant in interpreting and predicting users’ behaviour towards the investigated technology and the HCM makes it possible to easily embed psychological factors into a random utility model/framework. Moreover, the explicit simulation of the attitudes allows for a better prediction of users’ choice with respect to the Logit formulation and points out that users’ behaviour may be significantly affected by acting on users’ attitudes.
Revenue management under customer choice behaviour with cancellations and overbooking