Online micro-loan marketplaces cannot accurately and effectively approve loan applications due to the uncertain opportunistic behavior of loan applicants and the possibility of loan defaults by loan applicants. To address this challenge, in this study we integrate signaling theory, the social structure of competition, and the concept of homophily to develop a research model to predict a loan applicants re-loaning success by examining his/her financial status, friendship network characteristics, and friendship network centrality. Data of 683 anonymous and distinct loan applicants at a major online micro-loan marketplace in China largely support our hypotheses, highlighting the three key signals of a successful re-loan approval, a loan applicants credit card default, the number and the percentage of his/her friends with re-loan approvals in the focal micro-loan marketplace. Research and practical implications are discussed.
|Title of host publication||Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS)|
|Subtitle of host publication||Digital Disruption|
|Place of Publication||New Orleans|
|Publisher||Association for Information Systems|
|Publication status||Published - Aug 2018|
Gao, H., Liu, H., Ou, C., Pavlou, P. A., Zhu, H., & Zhan, M. (2018). Predicting re-loan success based on friendship network characteristics in the online micro-loan marketplace. In Proceedings of 24th Americas Conference on Information Systems (2018 AMCIS): Digital Disruption  Association for Information Systems. https://aisel.aisnet.org/amcis2018/SocialComputing/Presentations/14/