The peer economy, such as crowdfunding, democratizes access to tasks available only to professionals. Although the peer economy has gained great popularity in practice, how crowds infer information from their peers, especially from experts, is still under minimal study in academia. Using data from a debt-based crowdfunding platform in China, this study investigates the impact of seasoned predecessors’ bids on subsequent investors' decisions and how seasoned and unseasoned investors respond differently to herding signals. We discover that the cumulative lending amount from seasoned predecessors is positively associated with the lending amount of a successor, and such an association is greater if the successor is seasoned. In the repayment process, we find that the lending amount from seasoned investors is positively associated with loan performance, while the lending amount from unseasoned investors is not. Our results contribute to the literature on crowds of wisdom, implying that in a context that requires sophisticated knowledge, extracting hidden talents from experts rather than from crowds is more appropriate.
|Original language||English (US)|
|Journal||Electronic Commerce Research|
|State||Accepted/In press - 2022|
Bibliographical noteFunding Information:
Funding was provided by Natural Science Foundation of China (Grant No. 71771159, 72071160).
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Collective intelligence
- Debt-based crowdfunding