Abstract

With the continuing application of artificial intelligence (AI) technologies in decision-making, algorithmic decision-making is becoming more efficient, often even outperforming humans. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of existing literature, we systematically review 80 empirical studies identified through searching in seven academic databases and using the snowballing technique. We inductively categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little attention has been given to exploring the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Our model could guide developers in designing and developing and managers in implementing and using of algorithmic decision.