Integrating technology and sustainability: Behavioral drivers for autonomous electric vehicle adoption

Autonomous Electric Vehicles (AEVs) are fundamentally software-driven systems built on robust IT architectures, offering vast potential for transformative mobility. Based on data from U.S. university students, this study enhances the literature on AEV adoption by proposing and empirically validating a comprehensive behavioral intention model that integrates functional (performance and effort expectancy), emotional (trust and perceived enjoyment), and value-based (green identity) predictors. Unlike traditional adoption models, this study positions trust as a central mediator and the strongest direct predictor of AEV adoption intention. The findings also highlight the distinct and important roles of green identity and effort expectancy, suggesting that both pro-environmental values and perceived ease of use are crucial in shaping early consumer acceptance. By illustrating how these diverse factors interact within the context of an uncommercialized technology, this research provides novel theoretical insights into the pre-adoption phase of sustainable and autonomous mobility innovations, as well as service management literature. Additionally, it provides practical guidance for policymakers and industry stakeholders seeking to build trust, align marketing with environmental values, and communicate ease of use in early-stage AEV adoption strategies.