How transparency affects algorithmic advice utilization: The mediating roles of trusting beliefs

Abstract

Although algorithms are increasingly used to support professional tasks and routine decision-making, their opaque nature invites resistance and results in suboptimal use of their advice. Scholars argue for transparency to enhance the acceptability of algorithmic advice. However, current research is limited in understanding how improved transparency enhances the use of algorithmic advice, such as the differences among various aspects of transparency and the underlying mechanism. In this paper, we investigate whether and how different aspects of algorithmic transparency (performance, process, and purpose) enhance the use of algorithmic advice. Drawing on the knowledge-based trust perspective, we examine the mediating roles of trusting beliefs in the relationships between transparency and the use of algorithmic advice. Using the “judge-advisor system” paradigm, we conduct a 2 × 2 × 2 experiment to manipulate the three aspects of transparency and examine their effects on the use of algorithmic advice. We find that performance and process transparency promote the use of algorithmic advice. However, the effect of process transparency gets attenuated when purpose transparency is high. Purpose transparency is only useful when process transparency is low. We also find that while all three aspects of transparency facilitate different trusting beliefs, only competence belief significantly promotes the use of algorithmic advice. It also fully mediates the facilitating effects of performance and process transparency. This study contributes to the emerging research on algorithmic decision support by empirically investigating the effects of transparency on the use of algorithmic advice and identifying the underlying mechanism. The findings also provide practical guidance on how to promote the acceptance of algorithmic advice that is valuable to both individual users and practitioners.