Preferences for Electric Vehicles Under Uncertain Charging Prices: An Eye-Tracking Study

Abstract

Transitioning to electric vehicles (EVs) is crucial to achieve sustainable mobility targets. However, fluctuating electricity prices raise uncertainty about EVs’ operational benefits. This study is the first to examine uncertain charging costs' impact on EV preferences using an extended decision field theory (DFT) model. Specifically, we analyze Singaporean ride-hailing drivers' long-term rental preferences under uncertain charging prices. Extended DFT outperforms traditional prospect theory in explaining decisions under uncertainty, evidenced by better fit and alignment with eye movement data. Incorporating eye movement data enhances the DFT model’s explanatory power. Three key empirical insights emerge. First, young, frequent, long-distance drivers are more inclined to adopt EVs. Second, uncertainty in operating costs (OC) significantly affects pro-EV drivers. Third, informing drivers about small, expected charging price increases proves more effective than leaving them uncertain about OC fluctuations. These insights are pertinent for government and EV rental companies for shaping policies on charging prices.

Keywords: Uncertainty, Electric Vehicles, Decision Field Theory, Eye-tracking, Charging Price