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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.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

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.

Despite a large stake of investment by retail investors and a growing number of peer-to-peer (P2P) lending platforms coupled with the initiation of secondary market and strong regulatory framework, less is known what leads investors to trust in P2P (TP2P) lending platforms in a multi-ethnic country, Malaysia. This study aims to investigate the effects of individual characteristics (gender, age, ethnicity, education and income), social influence of P2P (SIP2P) lending and privacy of P2P (PP2P) lending on the trust in emerging P2P platforms.

A cross-sectional survey was conducted to collect the data from retail investors in Malaysia. A variance-based partial least squares-structural equation modeling (PLS-SEM) model was applied to examine the significant predictors of TP2P lending platforms.

The results show that while investors' income is positively related to TP2P lending platforms, younger investors are less likely to have trust on P2P lending platforms. PP2P lending platforms increases retail investors' trust toward P2P platforms in Malaysia.

P2P service providers are suggested to give especial attention to investors' specific characteristics to develop trust and attract investors to the platforms. Service providers need to ensure the privacy of potential investors' personal and confidential data to build investors' trust.

This is the first study to assess retail investors' trust toward online P2P lending platforms in Malaysia, where this alternative financing platform gradually gaining popularity.

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.

In this chapter, the authors focus on the development of the peer-to-peer (P2P) lending industry in China. As a modern borrowing platform, P2P lending allows clients to obtain funding from peer lenders for a multitude of loan purposes, including credit consolidation, personal purchases, and the development of business ventures. However, the speed at which this industry has grown has created numerous problems for regulatory agencies, particularly in China, the largest P2P lending market in the world. This chapter examines how lenders in the Chinese context continue to function as formal institutions regulating this sector continue to grow following a series of highly publicized illegal lending activities in recent years. Additionally, the authors determine whether implemented regulatory measures are providing an overall benefit or detriment to the Chinese P2P lending industry. Finally, the authors highlight the potential for positive social change and social entrepreneurship arising from P2P lending, particularly in terms of the empowerment of traditionally disadvantaged groups by providing access to capital. The authors use the P2P lending industry in the United States, currently the second largest in the world and one operating in a highly regulated financial industry, as a comparison for the Chinese case.

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.