THE IMPACT OF DEVELOPER REPUTATION AND EXPERTISE ON DYNAMIC DEVELOPMENT OF TRUST

Abstract

The present study investigated dynamic development of trust in algorithmic trading system under information consistency about the developer reputation and system errors. Across two experimental studies, trust trajectories were studied among novice and domain-knowledge users within financial decision-making context. Study 1 investigated the effects of developer reputation (consistent vs inconsistent information) and system errors on trust development and recovery among novice users. Study 2 extended the investigation to domain-knowledge users under persistent system errors to check the role of expertise in trust calibration. Trust was measured repeatedly across four time points using Situational Trust/Distrust Scale and behavioral concordance with system recommendations was assessed through chi-square analyses. The results showed that consistent developer reputation information produced higher trust and system errors led to reduced trust across studies. Novice users showed strongest trust fluctuations and recovery following system update and improved performance, whereas domain-knowledge users showed comparatively more stable and calibrated patterns of trust. Findings from behavioral concordance showed greater agreement with system recommendations despite decrease in trust under persistent errors. Overall, the study highlighted how trust is dynamic, performance-sensitive and moderated by user expertise.