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Prosocial lending in online crowdfunding has flourished in recent years, and it has become a new way to fundraise for philanthropy. However, there is almost a 70% user attrition rate in crowdfunding. The purpose of this study is to understand what the lender’s lending experience and social connection influence lender retention of online prosocial lending from a self-determination perspective.

Drawing on self-determination theory (SDT), this research utilizes a quantifiable method for factors of the lender's lending experience and social connection. Additionally, the research constructs economic models to explore the impacts of these factors acting as the necessary conditions for basic psychological needs on lender retention, using a large-scale sample of over 380,000 lenders from Kiva.

The results indicate that, from the lender's lending experience aspect, the loan narratives with more profit language in the last lending and the failure of past participation are negatively related to lender retention. Regarding the lender's social connection aspect, their friends or small lending teams are positively related to lender retention, while whether they are invited and lending team size show negative influence. Furthermore, results indicate the moderating effects of the disclosure of lending motivation.

This research explores the mechanism of lender retention of online prosocial lending, providing a self-determination perspective about how previous experience influences long-term lending behavior. The study offers significant implications for the literature on online philanthropy, SDT and user retention of online platforms. At the same time, the study provides an understanding of the effects of different aspects of SDT.

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.

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.

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.

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.

This work proposes a research model that explains investment intention in online peer-to-peer (P2P) lending based on the persuasion theory of elaboration likelihood model (ELM). In the proposed model, investment intention indirectly relates to source credibility and argument quality through the mediation of trust. At the same time, the study hypothetically moderates the relationships between source credibility and trust and between argument quality and trust by financial self-efficacy and risk preference.

This research presents an experiment to empirically validate its theoretical rationales. The hypotheses herein were tested using data from working professionals at a large science park in Taiwan. A total of 500 participants took part in the experiment in which the scenario of a pseudo-online P2P lending intermediary was first presented for their perusal, and then questionnaires based on the scenario were provided for the participants to fill out.

Trust cannot be improved over night without making great efforts on source credibility and argument quality in the long run. Online marketers should study market segmentations to decide what appropriate elements and promises should be provided in advertisements in order to improve their source credibility. Moreover, how online intermediaries formulate convincing messages and Polish their delivery communication skills should be improved so as to increase argument quality.

First, the theoretical conceptualization of source credibility and argument quality built upon the ELM not only broadens the boundary of virtual communities beyond the literature that considers source credibility and argument quality as important determinants, but also shows the practical status quo of trust as a critical mediator. Second, this research incorporates financial self-efficacy (based on social cognitive theory) and risk preference (based on economic theory) as important moderators in the development of trust. For that reason, customer education initiatives that influence financial self-efficacy and risk preference are discussed in greater detail.

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.

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.