Are all robo-advisors the same? Out-group homogeneity bias in investors’ perceptions of robo-advisors
Publication Date
11-1-2025
Document Type
Article
Publication Title
Finance Research Letters
Volume
85
DOI
10.1016/j.frl.2025.107908
Abstract
Robo-advisors are becoming increasingly prevalent in financial markets, raising important questions about how investors perceive them. Using an experiment, we study whether and why investors generalize a single robo-advisor's performance more than that of a human financial advisor. Drawing on social categorization theory, we predict and find that investors perceive robo-advisors as more homogeneous than human financial advisors. This “all robo-advisors are the same” perception leads to greater transference of both success and failure across robo-advisors compared to human advisors. These findings highlight algorithmic transference in investor decision-making and carry important implications for investors and firms offering robo-advisory services.
Funding Sponsor
San José State University
Keywords
Algorithmic transference, Financial advisors, Out-group homogeneity bias, Robo-advisors, Social categorization theory
Department
Accounting and Finance
Recommended Citation
Yunshil Cha and Fangjun Xiao. "Are all robo-advisors the same? Out-group homogeneity bias in investors’ perceptions of robo-advisors" Finance Research Letters (2025). https://doi.org/10.1016/j.frl.2025.107908
