How information processing style shapes people's algorithm adoption

Abstract

Across four studies I tested why people are averse to relying on algorithmic judgments in person judgment tasks (e. g., student admissions), and examined how such aversions can be attenuated. I proposed that people tend to focus more on case-specific information (vs. general propositions) in person-judgment tasks, and that algorithms (vs. human experts) are believed to be skilled at addressing general propositions (vs. case-specific information). Thus, I posited that in person-judgment tasks, people would be less averse to relying on algorithmic judgments when they focus more on general propositions (vs. case-specific information). By varying the perceived importance of case-specific information and general propositions, the research provides support for these hypotheses. In addition, the results reveal the mechanism underlying algorithm aversion in person judgments and provide a cost-effective way to increase consumers' algorithm adoption.