دانلود رایگان مقاله قضاوت آنلاین رفتار نظیر به نظیر وام دهی

Abstract

The past decade has witnessed a growing number of business models that facilitate economic exchanges between individuals with limited institutional mediation. One of the important innovative business models is online peer-to-peer lending, which has received wide attention from government, industry, and researchers. Using the signaling theory, we compare the effects of various signals on the likelihood of successful funding in three models (i.e., first-time borrowing, repeated borrowing without prior lending, and repeated borrowing with prior lending). Using data collected from PPDAI.com, we verify the three proposed models by employing logistic regression. Results and implications are analyzed and discussed.

7. Conclusions

Our results add to the nascent empirical research on P2P lending. Instead of investigating lender’s investment strategies, we addressed this issue by examining which factors are powerful signals that make a borrowing request more likely to be fulfilled. Overall, our results confirm the power of a list of signals from the borrowing request. However, this does not simply replicate prior research in a specific P2P-lending platform. More importantly, we extend the theory and research on P2P lending by comparing the effects of different signals in different scenarios. By using the signaling theory, our study shows that for first-time borrowing, repeated borrowing without prior lending experience, and repeated borrowing with prior lending experience, the power of different signals vary significantly. However, there are still some limitations to the current research. First, we collected only data from one P2P platform (PPDAI) that may have unique characteristics, limiting the generalizability of the proposed model. Second, although we obtained a relatively large data set, the number of records for repeated borrowing with prior lending experience was relatively small; therefore, the results should be viewed as only preliminary evidence with respect to the varying signals that affect successful funding in different scenarios. Finally, the R2 of the models became substantially lower from first time to repeated borrowing. This may suggest that there are other important factors for these models that have not been considered. Future research should be conducted by including more undiscovered variables in the models.