Modeling the Trust-to-Psychological Dependence Transition in ADAS: A Socio-Technical Systems Perspective

With the widespread adoption ofADAS in everyday driving, issues such as users’ psychological dependence on ADAS have attracted increasing attention. Although previous studies have explored adoption behaviors and trust mechanisms toward ADAS, there remains a lack of systematic investigation into the dynamic process and boundary conditions through which ‘‘trust transforms into psychological dependence.’’ Based on the socio-technical systems (STS) theoretical framework, this study examines how social factors, user traits, and technological attributes jointly influence the formation of psychological dependence through the mediating pathway of ‘‘trust → continuance intention.’’ A mixed-methods approach was employed, combining grounded theory interviews ( n = 48) and a questionnaire survey ( n = 532), with quantitative validation conducted through structural equation modeling (PLS-SEM), artificial neural networks (ANN), and necessary condition analysis (NCA). The results indicate that perceived reliability, ease of use, and bandwagon effect significantly enhance users’ trust in ADAS, which in turn drives continuance intention and the formation of psychological dependence. Findings from the ANN and NCA further reveal that perceived ease of use is a necessary condition for establishing trust. Theoretically, this study proposes a mechanism model describing the ‘‘transformation from trust to dependence,’’ clarifying both the necessary and boundary conditions for the formation of psychological dependence. Methodologically, it demonstrates the complementary value of PLS-SEM, ANN, and NCA in modeling linear and nonlinear behavioral mechanisms. Practically, the study suggests balancing system automation with drivers’ sense of control in ADAS interface design, and guiding policy efforts to mitigate drivers’ overreliance on automated systems.