The Twofold Role of Legal Liability Misattribution on Intention to Buy Automated Vehicles: A Survey in China

Liability attribution for crashes involving automated vehicles (AVs), if applied improperly, is a factor which can potentially hinder acceptance. The present study investigated the impact of liability attribution on intention to buy an AV. A vignette-based survey was implemented with a hypothetical crash similar to the 2018 Uber crash (which was jointly caused by driver distraction and the malfunctioning of the automated system) leading to a pedestrian's fatality. Respondents (N = 1524) chose their preferred liability attribution, ranging from human driver exclusively liable to AV manufacturer exclusively liable. Respondents were then randomly allocated to different conditions of actual liability attribution by the local authority. These conditions were then combined into, negative misattribution (the authority assigned more liability to the human driver, compared to the respondent), positive misattribution (the authority assigned less liability to the human driver), and no misattribution. Negative misattribution negatively affected intention to buy; however, positive misattribution did not have a significant impact. The results of a multiple-mediator model indicated that negative misattribution affects intention to buy through the mediating effects of trust, negative affect, and crash acceptability. Theoretical and practical implications of our results are discussed.