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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.

This study aims to develop a theoretical model that uses the decision-making theory in a financial intermediation setting to provide insights into the differences between the outcomes of the decision-making process for a bank and for a peer-to-peer (p2p) lending platform to explain the role of p2p lending versus bank lending in the credit market.

This study develops a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. In particular, it analyzes the likelihood of a risky borrower being able to obtain a loan from a p2p lending platform versus the likelihood of being able to obtain a loan from a bank. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market, with implications for recovery from an economic crisis.

p2p lending platforms have the potential for contributing to economic recovery when they are subject to less regulations and are able to offer a faster and less costly lending process than do banks and when they are used by a large number of lenders. However, the potential role of p2p lending platforms in recovery might be reduced when banks have access to anticyclical measures that reduce banks’ capital requirements or provide them with low-cost funds.

This study provides a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market.

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 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.

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.

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.

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.

This study aims to examine the adoption of peer-to-peer (P2P) lending platforms to determine the factors that encourage SMEs to use P2P lending platforms in obtaining loans.

A sample of ten SMEs from a variety of backgrounds was taken in Bandung, Indonesia. Bandung has been awarded the title of “creative city” by UNESCO, as the city allows for the development of the creative economy. This research used a semi-structured interview. Coding method was then used for content analysis to establish which factors emerging from the interview were leading respondents to obtain a loan through the P2P lending platform.

The findings imply that loan processes, interest rates, loan costs, loan amounts and loan flexibility affect SMEs in obtaining a loan through P2P lending. Moreover, alternative payment schemes in the form of Sharia-based lending and profit-sharing schemes were found. These findings constituted the original findings of this study.

The study offers findings on factors affecting SMEs in using the P2P lending platform as a form of alternative financing. Moreover, the theoretical framework provided can be used as literature in future research. As this study was conducted in Bandung, Indonesia, the findings may not be generalisable to other regions.

This study is one of the few studies that discusses P2P lending in Indonesia as the concept has been in practice only since 2015.

China is a country with the most number of operating peer-to-peer (P2P) lending platforms (approximately 2,000) worldwide. This study aims to provide an overview on FinTechs in China. It was examined why payment services and P2P lending are so popular in China and what are the determinants for the emergence of P2P lending platforms in different provinces in China.

This study conducted a descriptive analysis of P2P lending in China and an empirical analysis of determinants of P2P lending in China.

This descriptive analysis shows that the surge in the number of the P2P platforms in China follows an inverted U-shaped phenomenon. However, the outstanding balances of P2P lenders is still increasing, while average yields of P2P lenders have sharply plunged. The empirical findings indicate that P2P lending is more extensive in the region with more mobile phone subscriptions; outstanding balance of P2P lenders in region is negatively associated with the size of traditional banking sector; and the number of the P2P platforms in negatively related to the fixed assets investments in region, whereas average yield is positively associated with the fixed assets investments.

Currently, almost no research papers with empirical analysis of FinTechs, especially P2P lenders, exist. This study estimates a simple model to find determinants of P2P lending.

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.