Theoretical Mechanisms of How AI-Enabled Driver Assistance Systems Influence Consumers’Automobile Choice Behavior

Abstract

This study develops a theoretical mechanism-based framework to explain how artificial intelligence (AI)-enabled driver assistance systems influence consumers’ automobile choice behavior. While research on AI-enabled driver assistance has largely focused on engineering feasibility, system reliability, and safety performance, consumer research has not sufficiently theorized how AI systems’ autonomy-related properties reshape consumers’ cognitive evaluations and decision processes. Distinct from conventional vehicle attributes, AI-enabled driver assistance systems constitute intelligent socio-technical configurations that participate in driving decision-making, thereby introducing unique psychological concerns such as perceived autonomy, controllability, transparency, and responsibility ambiguity. Building on prior work in consumer choice, technology acceptance, and trust–risk theories, this study conceptualizes four key perceived attributes—perceived intelligence, perceived autonomy, perceived controllability, and perceived transparency—and argues that AI-enabled driver assistance affects automobile choice behavior primarily through mediated pathways of trust formation and risk/uncertainty appraisal. The framework further clarifies that the choice effects of AI-enabled driver assistance are not uniformly positive; rather, they depend on consumers’ perceived trade-offs between anticipated technological benefits and psychological uncertainty. By theorizing AI-enabled driver assistance as an autonomy-bearing technology element in consumption contexts, the study extends consumer choice theorizing into AI settings, refines conceptual distinctions between objective technical capability and subjective perceived attributes, and offers an integrative mechanism model that can guide future empirical work and managerial practice in intelligent vehicle design and communication.