Trust Calibration in Augmented Reality-based Decision Support Systems

This empirical study investigates how users modify their trust in Augmented Reality-based (AR) decision support systems (DSS) in work environments, and how this trust influences work-related outcomes such as task performance, reliance on the system, and future intention to use it. Building upon existing research on trust in technology, particularly in automation, we develop a research model and hypotheses for Hu-man-AR-Interaction. To test our research model, we conducted a laboratory experiment with 115 participants using an ARbased decision support system. We manipulated the system’s performance, process, and purpose as key antecedents of trust calibration. Our findings indicate that calibrated trust has a significant positive influence on users’ reliance in the context of AR-DSS and their intention to use such systems in the future.