Psychological Distance and Algorithm Aversion: Congruency and Advisor Confidence.

The article investigates how psychological distance influences individuals’ preferences between human and algorithmic advisors, applying construal level theory (CLT) to explain algorithm aversion and appreciation. It finds that people perceive algorithms as psychologically more distant and abstract than human advisors, despite viewing algorithmic outputs at a lower construal level. Across multiple studies, greater temporal, spatial, or social distance within tasks decreases confidence in human advisors but not in algorithms, leading to increased preference for algorithmic advice when tasks or advisors are perceived as psychologically distant. Confidence mediates this relationship, and these effects hold across different contexts such as financial forecasting and hiring decisions. The research integrates social and technical factors to provide a theoretical framework for understanding mixed attitudes toward algorithmic advice and offers practical implications for framing algorithm use in organizations.