The diffusion mechanisms of dynamic ridesharing services

Abstract

Dynamic ridesharing services and platforms need to reach an initial critical mass of users in order to provide desirable levels of matching between drivers and passengers. The objective of this work is to investigate some of the levers that could boost the diffusion of a new ridesharing service, by the means of a system dynamics (SD) model that integrates existing diffusion models with the main outcomes of a project to which the authors contributed. Findings show that a dynamic ridesharing platform should operate mostly in urban areas because higher contact rates turn into increased matching opportunities and therefore support the diffusion process. Moreover, it should focus on the partnering and advertising action, especially toward the population of drivers. Finally, the combination of both high desired matching and short patience expressed by users in waiting for a more effective service provide the most significant potential barrier to the diffusion.