Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
It addresses the challenge of improving human-robo-advisor collaboration for financial decision-making, offering incremental insights into user behavior and system design.
This study tackled the problem of limited adoption of robo-advisors by investigating how people interpret their roles and integrate advice into financial decision-making, finding that reliance is influenced by performance information and framing, with a typology of user types and antecedents of acceptance.
Robo-advisors (RAs) are cost-effective, bias-resistant alternatives to human financial advisors, yet adoption remains limited. While prior research has examined user interactions with RAs, less is known about how individuals interpret RA roles and integrate their advice into decision-making. To address this gap, this study employs a multiphase mixed methods design integrating a behavioral experiment (N = 334), thematic analysis, and follow-up quantitative testing. Findings suggest that people tend to rely on RAs, with reliance shaped by information about RA performance and the framing of advice as gains or losses. Thematic analysis reveals three RA roles in decision-making and four user types, each reflecting distinct patterns of advice integration. In addition, a 2 x 2 typology categorizes antecedents of acceptance into enablers and inhibitors at both the individual and algorithmic levels. By combining behavioral, interpretive, and confirmatory evidence, this study advances understanding of human-RA collaboration and provides actionable insights for designing more trustworthy and adaptive RA systems.