Human-Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending

• 成果类型: Article
• 署名作者: Ge, Ruyi; Zheng, Zhiqiang (Eric); Tian, Xuan; Liao, Li
• 署名单位: Shanghai Business School; University of Texas System; University of Texas Dallas; Tsinghua University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.1009
• 发表日期: 2021
• 页码: 774-785
• 关键词: distribution channels; INFORMATION; algorithms; JUDGMENT; adoption; systems; trust

摘要

We study the human-robot interaction of financial-advising services in peer-to peer lending (P2P). Many crowdfunding platforms have started using robo-advisors to help lenders augment their intelligence in P2P loan investments. Collaborating with one of the leading P2P companies, we examine how investors use robo-advisors and how the human adjustment of robo-advisor usage affects investment performance. Our analyses show that, somewhat surprisingly, investors who need more help from robo-advisors-that is, those encountered more defaults in their manual investing-are less likely to adopt such services. Investors tend to adjust their usage of the service in reaction to recent robo-advisor performance. However, interestingly, these human-in-the-loop interferences often lead to inferior performance.