Psychological determinants of trust and reliance on automated decision-support systems: an integrated expectancy-based perspective

Introduction Automated decision-support systems are increasingly shaping human cognitive and decision-making processes, making it essential to understand the psychological mechanisms underlying users’ trust and reliance. While prior research has examined system characteristics and expectancy-based beliefs separately, limited empirical work has integrated these perspectives to explain how trust translates into reliance behavior. Methods This study develops and tests an integrated psychological model in which affective trust mediates the relationship between cognitive evaluations and reliance intention. Drawing on trust theory, the Technology Acceptance Model, and the Unified Theory of Acceptance and Use of Technology, survey data were collected from 345 university-educated adults with prior experience using automated decision-support systems. Structural equation modeling was employed to evaluate the measurement and structural models. Results The findings indicate that system accuracy and timeliness significantly enhance perceived reliability and responsiveness, while perceived ease of use positively influences effort expectancy and performance expectancy. Both service-related system characteristics and expectancy-based cognitive evaluations significantly contribute to affective trust formation. Affective trust evaluation, in turn, strongly predicts reliance intention, confirming its mediating role. Conclusion The results highlight a sequential psychological process in which cognitive evaluations of system performance and usability translate into reliance through affective trust. This study contributes to the psychology of human–automation interaction by providing an integrated framework that advances theoretical understanding and offers practical guidance for designing trustworthy automated systems.