Dynamic Consumer Heterogeneity in Electric Vehicle Adoption

Combining psychographic and stated choice data to analyze individual choices and identify sources of heterogeneity has long been applied in demand modelling. In this paper, the authors develop a dynamic innovation diffusion framework to model adoption of alternative fuel vehicles (AFV). The authors combine this approach with a stated preference (SP) study to elicit individual preferences for conventional, hybrid and full electric vehicles and to explore preference heterogeneity across the consumer segments. In contrast to ex-post division of actual adopters in adopter segments proposed in Diffusion of Innovations Theory (DoI) (Rogers, 2000), the authors apply an ex-ante approach and identify 5 consumer segments consisting of prospective adopters and non-adopters. These are: Innovators, Early Adopters, Early Majority, Late Majority, and Traditionalists. The authors find that respondents in various consumer segments differ in their demographic and psychological profiles in conformity with the DoI theory. Discrete choice modeling based on SP data collected in the Netherlands in 2012, and using panel latent class approach within each segment allows pinpointing potential electric vehicle (EV) adopters among the representative sample of Dutch drivers at each stage of adoption. The findings show that potential AFV adopters from the mass market mind peer opinion about the alternative vehicles and are conscious about their pro-environmental behavior. High valuations of time associated with fast charging imply the necessity in improving charging infrastructure in facilitating large-scale AFV adoption. Besides, towing potential appears to be important to many potential EV adopters on the Dutch market.