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

Ruyi Ge (), Zhiqiang (Eric) Zheng (), Xuan Tian () and Li Liao ()
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Ruyi Ge: Department of Electronic Commerce, Shanghai Business School, Shanghai 200235, China
Zhiqiang (Eric) Zheng: Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080
Xuan Tian: PBC School of Finance, Tsinghua University, Beijing 100083, China
Li Liao: PBC School of Finance, Tsinghua University, Beijing 100083, China

Information Systems Research, 2021, vol. 32, issue 3, 774-785

Abstract: 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.

Keywords: robo-advisor; human-in-the-loop; peer-to-peer lending; augmented intelligence (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:32:y:2021:i:3:p:774-785

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