A critical overview of theories in research on judgment and decision-making using algorithmic advice: The potential of cognitive continuum theory

This article discusses the impact of digitalization on judgment and decision-making processes, focusing on the use of algorithmic advice. We highlight the growing popularity and effectiveness of artificial intelligence (AI) and algorithmics in various domains. Although algorithmics has demonstrated superior predictive capabilities, decision-makers often exhibit algorithm aversion, preferring human advice over algorithmic advice. However, there is also a phenomenon known as algorithm appreciation, in which individuals trust and appreciate algorithmic advice in some situations. We conducted a comprehensive overview of theories of algorithm use, scrutinizing 145 studies examined in four recent systematic overviews. We emphasize the need for a theoretical framework that considers task-specific characteristics and individual perspectives to understand and enhance the use of algorithmic advice. After summarizing theories developed in research on algorithm use in judgment and decision-making, we focus on theories of the interplay between the decision-maker’s individual differences and the task, which have been completely neglected, as our overview shows. Thus, we suggest cognitive continuum theory (CCT) as a suitable framework for understanding the use of algorithmic advice. By using CCT, designers can develop AI systems tailored to task properties and cognitive processes, thereby increasing people’s use and understanding of algorithms. On the continuum from analytical to intuitive processes, we anticipate that there will be a higher receptivity to algorithmic guidance in tasks that fall closer to the analytical thinking end than in tasks that primarily involve intuitive thinking. We emphasize that algorithmic advice should be seen not as a replacement for human judgment but rather as a tool for augmenting and improving decision-making processes.