研究人与机器人顾问如何协作决策，揭示信任形成机制。
Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
- 通过多阶段实验分析用户如何理解并采纳机器人建议。
- 发现用户依赖度受表现信息和建议表述方式影响。
- 提出四类用户类型与双层影响因素模型，助设计更可信系统。
机器人顾问（RAs）是成本低、抗偏见的金融顾问替代方案，但实际采纳率仍不高。尽管已有研究关注用户与RAs的交互，但对个体如何理解其角色及整合建议仍知之甚少。本研究采用多阶段混合方法设计，结合行为实验（N=334）、主题分析与后续定量验证。结果表明，用户倾向于依赖RA，且依赖程度受其表现信息及建议被呈现为收益或损失的影响。主题分析识别出三种RA在决策中的角色与四类用户类型，反映不同的建议整合模式。此外，构建了2×2分类体系，将接受度的前因分为个体与算法层面的促进因素与抑制因素。通过行为、解释性与验证性证据融合，本研究深化了对人机协作的理解，并为设计更具可信度与自适应性的RA系统提供可操作洞见。
原文摘要 · Abstract (English)
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.
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