What drives people to choose robo-taxis when scenario-specific factors change? An experimental investigation in Chinese cities

Abstract

This study investigates the cognitive and contextual determinants of robo-taxi adoption in Chinese cities through a factorial vignette-based experiment. Integrating the Theory of Planned Behavior with Prospect Theory, the research examines how individual-level beliefs and scenario-specific factors shape adoption intentions among both users and non-users in cities where robo-taxis operate. The design manipulates perceived economic benefits and driving task aversion (DTA) across realistic mobility scenarios to test for asymmetric behavioral responses.

Findings reveal that high economic benefits and high DTA significantly increase adoption attitudes and intentions, while low levels of these factors do not produce symmetrical negative effects, consistent with loss aversion principles. Attitude formation differs by experience: users are most influenced by economic benefits, while non-users respond more strongly to perceived system intelligence. Urban Mobility Ecosystem Integration (UMEI) emerged as a driver of favorable attitudes, particularly when platform compatibility and payment integration were emphasized. The results challenge linear adoption models by demonstrating reference-dependent, nonlinear patterns of evaluation, especially for economic and driving-related attributes.

This study contributes to adoption research by combining behavioral theories in a scenario-based design and highlights the need for experience-sensitive models in emerging mobility systems. Policy implications include the importance of privacy assurance, user segmentation, and localized integration strategies for maximizing uptake. Findings support the refinement of adoption models to better reflect asymmetric effects and boundedly rational user behavior in complex urban ecosystems.