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The purpose of this paper is to study the effects of multidimensional friendship networks on economic outcomes in the domain of online people-to-people (P2P) lending markets.

The empirical analysis is based on the data set of transactions and friendship networks from PPDai.com market, the most prominent P2P lending market in China. A friendship hierarchy is proposed in this paper to conceptualize friendship network types. Furthermore, methodologies of t-test, logistic regression and ordinary least squares regression are implemented to measure the impact of multidimensional friendship network variables on the probability of successful funding, as well as the interest rates on funded loans.

The study demonstrates significant effects of structural, relational and cognitive friendship networks using PPDai.com data. The results indicate that structural friendship network measured in terms of the number of friendship ties is a significant factor of funding performance. Additionally, borrowers, who are involved in higher-quality friendship networks, are more likely to be funded and pay lower interest rates on funded loans. Also, the deeper the level of the relationship is in the friendship hierarchy, the more significant will be the effect of friendship on the final economic results. Furthermore, quality is more important than quantity in determining funding performance.

This paper is the first to study the effects of multidimensional friendship networks on economic outcome variables in the domain of online P2P lending, thus broadening the theory of multidimensional social capital, which can deepen our understanding about how social networks work and have significant implications practically and theoretically.

Despite the emergence of peer-to-peer (P2P) lending in Malaysia, there is a knowledge gap on what drives the lending decision of P2P lending in the emerging Malaysian market. This research investigates how borrower's loan tenure, funding purpose, verified documents, accumulated transaction and repayment history, age, trustworthy and geographical resemblance affect likelihood of lending decision in P2P platform.

Using snowball sampling, survey data was collected from 300 online banking users who were willing to invest in online P2P platform from different states in Malaysia (i.e. Selangor, Malacca, Johor and Negeri Sembilan). For estimation, regression analyses were estimated.

The findings suggest that borrower's loan tenure and borrower's age increase the probability of lending in online P2P platform, while funding purpose of credit card reduces the likelihood of lending in the P2P platform. The findings contribute to the signalling theory.

The findings imply that borrowers need to concentrate on loan tenure and clearly indicate their age in the listing in order to increase the funding probability. Moreover, they are suggested not to submit listing for credit card as funding purpose.

This study is first in its nature about P2P lending in Malaysia and the possible factors that influence lending decisions in this new financing platform.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

The purpose of this paper is to examine how the impact of Chinese peer-to-peer (P2P) platform reputation directly and indirectly (mediate effect) affects investors’ (lenders) investment choices.

Using data collected from 478 P2P platforms, this paper calculates platform reputation via a β function after establishing a reputation mechanism by game analysis. This is followed by testing both the direct effect of platform reputation on investors’ investment choices (proxying by transaction volume) and the indirect effect through credit-enhancing information using three regression models (median regression, OLS regression and random effect OLS regression). A robustness test by adding instrument variables is conducted to confirm the findings from the main regressions.

In China, P2P lending platform reputations have played both a direct and indirect (through credit-enhancing information) role on investors’ investment choices.

This paper expands the boundary of P2P online lending research by not only examining the direct, but also, importantly, the indirect effects of platform reputations.

Online Peer-to-Peer (P2P) lending platforms are becoming increasingly popular globally in recent years. Our knowledge of how to develop and manage the digital platforms that make P2P lending possible, however, is limited. Through an in-depth examination of the strategies deployed and actions taken across the various stages of development of Tuodao, one of the most successful online P2P lending platforms in China, the purpose of this study is to develop a process model of P2P Lending Platform Development to address this knowledge gap.

The case research method was adopted for this research, and a total of 16 informants were interviewed. The informants were composed of representatives of Tuodao’stop management, organizational IT functions as well as its various business units.

Our study reveals that the development of a P2P lending platform can unfold in a specific sequence across three stages, and the development of a particular side of the platform should be emphasized in each stage (i.e. Partners, followed by Lenders, and then Borrowers). Each stage is also distinctive in terms of their strategies and platform configuration outcomes, which are elaborated on in our paper.

Our process model contributes an in-depth view of how P2P lending platforms should be established and nurtured to complement the existing studies in this rapidly growing research area. In addition, our study also hints at the strategies that can facilitate the various stages. Our model can potentially serve as the foundation for formulating guidelines for the managers of P2P lending platforms, so that they are able to optimize the development of their platforms and extend the benefits of P2P lending to a broader base of customers.

The purpose of this paper is to investigate the influence of trust and perceived risk on investment willingness considering the bidirectional relationship between trust and perceived risk in peer-to-peer (P2P) lending.

Data were collected from a leading Chinese P2P platform, PPDAI.com. In total, 328 valid responses were received and analyzed using structural equation modeling (SEM).

The results show that the influence of trust on investment willingness is significant, whereas that of perceived risk is insignificant. The results also indicate that platform reputation has a positive effect on trust, and the quality of alternatives is positively associated with perceived risk. In addition, the bidirectional perspective should be preferred to cope with the bidirectional relationship between trust and perceived risk in P2P lending.

This study extends existing research on the influence of trust and perceived risk on investment willingness from a bidirectional perspective, which has not been addressed in the P2P lending context. In addition, this research enriches the current literature about trust and perceived risk by providing more evidence that the relationship between trust and perceived risk is bidirectional and thus the bidirectional model should be preferred. For practice, the study suggests that managers can earn trust and reduce the perceived risk of lenders by continuously providing high-quality products, services and enhancing platform reputation, ultimately improving their investment willingness.

Peer-to-peer (P2P) lending facilitates direct online lending and aims to provide financial inclusion and investment returns. Lender goals range from for-profit to pro-social and objective information is limited, which highlights the need to examine heuristics.

This study examines 1,347 lending decisions by finance students on a mock P2P site. Testimonials were used to randomly condition the financially literate lenders towards for-profit or pro-social decision-making. Each investor evaluated three loans. The three loan applications were identical except for a female or male headshot (vs an icon) and random reports of 50% funding for the female or male loan in 3 days (vs 11 days for opposite gender and 7 for icon). Previous research surveys students on a mock platform (Gonzalez, 2020) and reports similar heuristics and lifelike decisions in student and general population samples (Gonzalez and Komarova, 2014).

Lenders randomly conditioned towards pro-social lending state lower trust in borrowers. However, pro-social investors state lower risk in P2P lending and higher financial literacy. Second, pro-social investors are more confident when lending to borrowers highly trusted by other lenders, especially if the popular loan applicant is female. Third, pro-social conditioning increases lending to male applicants when the popular loan applicant is female. Fourth, pro-social investors who have experienced financial trauma have greater confidence in bad loan recovery.

This is the first study of heuristics in pro-social vs for-profit P2P lending. In addition, it shows that testimonials can effectively condition lending goals and affect trust and risk perceptions.

The paper focuses on the variables that help analyze peer-to-peer (P2P) lending platforms. It explores the characteristic factors of identifying problematic platforms, and designs a P2P platform risk early warning model.

With the help of web crawler software, this paper crawls the information of 1427 P2P platforms from the two largest third-party lending information platforms (i.e. P2Peye and WDZJ) in China. SPSS 22.0 was mainly used for basic descriptive statistical analysis, reliability and validity analysis, and regression analysis of the data. MPLUS 7.0 was used for confirmatory factor analysis and structural equation models analysis.

Based on the multi-dimensional information, this paper performs text mining to develop an investor sentiment index. This study shows that the characteristics of the platform (i.e. basic features, capital security, operations management, and social network) have a significant impact on identifying problematic platforms.

There are some limitations to this research. In the process of model construction, some external factors may be ignored, such as government policies. Future research will need to consider the impact of policy and other factors more comprehensively on P2P lending platform risk identification.

This study proposes an effective method for investors and regulators to identify the risk factors of P2P lending platforms. The research findings provide valuable insights for promoting government participation in platform management as well as a healthy development of the P2P lending industry.

The paper addresses the factors that influence platform risks to help analyze P2P lending platforms. Prior research has not explored how to identify problematic P2P lending platforms in-depth and is limited by only focusing on either soft information or hard information. It identifies the characteristic factors of identifying problematic platforms and designs a P2P platform risk early warning model.

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.

The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.

The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.

To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

The gradual implementation of blockchain technology in peer-to-peer (P2P) lending platforms facilitates safer, transparent and quick access to funds without having to deal with the more complex and costly processes of banks. Beyond that, the purpose of this paper is to examine trust-enhancing heuristics that show a need for blockchain to assist in monitoring and bad loan recovery.

This study examines 909 lending decisions by 303 finance students on a mock P2P site. Each participant was asked to make three lending decisions. The loan applications were identical with the exception of a female or male photo (vs an icon) and reports of having raised half the loan in either 2 or 11 days (vs 7).

Investors who have experienced financial trauma are more likely to herd and lend higher amounts to loan applicants that are highly trusted by other lenders. This effect is more pronounced for male investors lending to highly trusted female loan applicants.

Blockchain can compensate for behavioral biases and improve monitoring by helping track digital money transactions and assisting in bad loan recovery efforts.

This study is the first behavioral experiment to examine herding in P2P lending. The findings complement and corroborate those by Gonzalez and Komarova (2014, 2015) and emphasize the need for blockchain to assist beyond trusted records and safe transfers of funds.