The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles

Despite efforts from governments to increase the diffusion of more sustainable vehicles, such as alternative fuel vehicles (AFV), the market penetration of these vehicles has been difficult. Eliciting consumer preferences may provide valuable information on how to increase AFV diffusion. Since these are unfamiliar and complex products for most consumers, preferences are usually learnt during the process of elicitation. Preference learning is dependent on several factors, which include the type of elicitation task and its complexity. In this work, a stated preference survey was designed to analyze the potential impact of more complex elicitation tasks, multiattribute utility theory approach (MAUT), on the learning of preferences elicited through a traditional approach, choice-based conjoint analysis (CBC). The survey comprised two CBC sets of questions, one asked before and another asked after the MAUT. As a result three rankings of the vehicles set were obtained for each consumer, one derived from the initial set of CBC answers, a second one derived from the elicited MAUT model, and a third one derived from the second set of CBC answers. According to the results, there are significant differences from the first to the third ranking, possibly due to learning effects. Differences between the CBC-derived rankings were analyzed to assess if they were aligned with the MAUT model.