Trust Transfer in Autonomous Vehicles: The Role of Warm Brand Image Amid Automation Failure

As automated driving technology becomes an integral part of daily life, the significance of consumer trust in autonomous systems grows. While existing research has explored human-machine trust, the role of brand image has been overlooked. Trust transfer theory posits that initial trust in new products often originates from existing brand trust. Given the limited awareness of self-driving products, this study investigates how attitudes toward current brands may transfer into trust in autonomous systems. This paper utilized CARLA for simulating autonomous driving scenarios and conducted three studies to explore the impact of brand image (warmth/competence) on human-machine trust and its influence on automation failure. Study 1 analyzed the brand images of 39 Chinese autonomous driving companies, revealing a predominant focus on competence. In Study 2, a one-way, three-level online experiment demonstrated that both competence-based and warmth-based brand images significantly enhance initial human-machine trust. Study 3 explored the impact of automation failure on trust and the moderating effect of brand image, using a 2 (automation failure: high vs. low) × 2 (brand image: competence vs. warmth) × 2 (measurement time: pre-test vs. post-test) experimental design. The findings expand current understanding of human-machine trust and provide empirical support for the integrated model of human trust in AI. The research also offers strategic insights for self-driving brands, suggesting that adopting a warmth-based brand image could help maintain human-machine trust despite potential automation failures.