Modeling, analyzing, and fostering the adoption of new technologies: The case of electric vehicles

The adoption of new technologies, especially those related to the environmental transition, entails significant changes in individual behaviors. Therefore, it is crucial to design the related services within a human-centered framework, which can effectively model the adoption drivers and offer a consistent platform on which to test incentive schemes and supporting policies at design time. This chapter offers a general and widely usable data-driven framework to perform such analysis, for the first time testing its capability of managing the application of the designed policies in feedback, i.e., adjusting them in real time based on the evolution of the adoption process. To exemplify the principles of the framework, we consider a very relevant technological transition, which is that from Internal Combustion Engine (ICE) vehicles to Electrical Vehicles (EVs), on which the European Green Deal and its worldwide counterparts have set very ambitious goals in terms of substitution between the two. To perform the adoption analysis, we segment the population based on measured mobility habits, understanding the level of suitability of each user to switch to an EV. Based on this, we connect the users within a social network based on physical proximity and model the adoption dynamics as a cascade network evolution. Incentive schemes are designed in closed-loop , yielding superior effectiveness in terms of cost/benefit analysis with respect to open-loop policy application. These results pave the way to the use of this data-driven framework to foster many other technological transitions.