Investor Platform Choice Herding, Platform Attributes, and Regulations

• Published in: Journal of management information systems Vol. 35; no. 1; pp. 86 - 116
• Main Authors: JIANG, YANG, HO, YI-CHUN (CHAD), YAN, XIANGBIN, TAN, YONG
• Format: Journal Article
• Language: English
• Published: Taylor & Francis, Ltd 01.01.2018
• Subjects: Special Issue: Financial Information Systems and the Fintech Revolution
• ISSN: 0742-1222; 1557-928X
• DOI: 10.1080/07421222.2018.1440770

Online peer-to-peer (P2P) lending, one of the most successful technology- enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the herding effect, suggesting that more information disclosure and stricter operation standards reduce the value of observational learning. The results from our analysis provide implications for P2P lending investors, platform designers, and policymakers.