Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study

2025-10-02

AI Summary

Despite their cost-efficiency and reduced bias, robot advisors (RAs) suffer from low user adoption, primarily due to ambiguous role perception and unclear mechanisms for integrating algorithmic advice. Method: This study employs a multi-stage mixed-methods approach (N=334 behavioral experiments, thematic analysis, and quantitative validation) to systematically examine how information presentation formats and performance framing influence user reliance on RAs. Contribution/Results: We propose a novel tripartite RA role taxonomy—tool, collaborator, and authority—and identify four distinct user archetypes, culminating in a dual-dimensional 2×2 “individual–algorithm” acceptance model. Results show that while users generally rely on RAs, reliance is significantly moderated by performance information salience and gain/loss framing of recommendations. Key antecedents of adoption—both individual (e.g., cognitive style, trust disposition) and algorithmic (e.g., transparency, explainability of performance)—are empirically identified, offering theoretical grounding and actionable design principles for human–algorithm collaborative decision-making.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Intelligent Robots: Human-Robot Interaction
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Examining how people interpret robo-advisor roles in financial decision-making
• Investigating how individuals integrate robo-advisor advice into their decisions
• Identifying antecedents of robo-advisor acceptance at individual and algorithmic levels

Innovation

• Multiphase mixed methods design integrating experiments
• Thematic analysis revealing user types and roles
• Typology categorizing acceptance antecedents at levels