Small and micro enterprises and personal business firms face the problem of a shortage of funds. With the development of e-commerce—a particular type of lending (online lending)—provides a new solution. Online lending, also known as personal lending or peer-to-peer (P2P) lending, enables borrowers and lenders to trade directly via the Internet without using banks or other financial intermediaries. These are unsecured loans (Lin et al. 2013). This network financing model is based on Web 2.0 technology. Its core is a lending web site, which is a platform between borrowers and lenders. The P2P online lending market has developed rapidly since 2005; lending sites with slightly different modes of operation have appeared all over the world, including in America, Britain, Germany, Italy, Canada, Japan, and China.
There is some recent research on the P2P online lending market abroad, mostly focused on the data analysis of the Prosper open platform in the United States. But there is little related research in China. A deep understanding of the behavior of traders and the internal mechanisms of online lending is needed to help formulate relevant policies. Compared with the traditional e-commerce model, online lending involves higher risks, and establishing trust is also more difficult. This paper takes lenders as the research objects to study the key variables influencing lending intention in the process of online lending from the perspectives of trust and information asymmetry.
The remainder of this paper is organized as follows. First, it reviews related literature and basic theories, putting forward concept models and hypotheses. Then it introduces the research design and results, including data collection process and related hypothesis validation. Finally, it discusses and analyzes the results and presents conclusions.
Theoretical basis and research hypothesis
There are currently many P2P lending platforms in the world; all these platforms have adopted similar lending mechanisms. Users can become lenders and borrowers after registering. Borrowers release loan information, loan amounts, allowable highest interest rate, borrowing reasons, and other personal information on the platform. After verification by the platform, this information is presented to lenders. According to the list of borrowing information, lenders decide whether to loan, the loan amount and what interest rate to apply (i.e., the bid amount and interest rates), etc. Websites will usually require a minimum bid amount (e.g., 50 yuan), and a borrower accepts many bidders. Within the borrowing deadline, when the total bid exceeds the borrowing amount, lower interest rates triumph. After achieving the loan, lenders’ capital is transferred to the borrower’s account, and the borrower repays the loan over an agreed period of time.
This lending process involves high risks because the borrower is not always willing or capable of paying on time. Hence, choosing the credible borrowers to reduce investment risk is a key point for lenders to consider.
Some scholars have studied the influence of personal information on funding. Because lenders can only judge the risk of default according to limited information, personal information and borrowing information become important signals for evaluating borrowers’ credit. Personal information has an important influence on loan performance, borrowing, and lending decisions (Bachmann et al. 2011; Chen & Han 2012). For example, Lin et al. found that when the borrower’s credit rating is low, the borrower has a lesser possibility of success. When the interest rate is high, the default rates are also high (Lin et al. 2013). Herzenstein et al. found that the main factors influencing the success rate of loans were borrowers’ personal information and credit rating (Herzenstein et al. 2011). Duarte et al. found that borrowers’ appearance also has a significant impact on the success rate of loans (Duarte et al. 2012). If the borrower has an honest appearance, the success rate is higher; on the other hand, these borrowers have a high credit rating and a low default rate, which is believable.
Scholars’ research also emphasizes on the lenders’ social network and other decision-making information that influences loan behaviors. Online lending platforms not only provide borrowers’ personal information but also provide social networks for lenders to evaluate a borrower’s reputation. For example, Lin et al. found that borrowers’ social network information can effectively reduce information asymmetry in trade, improve the success rate of borrowing, and reduce the interest rate and loan default time (Lin et al. 2013). Research by Yum et al. shows that by observing others’ decisions, lenders are able to predict the private information of other lenders, pool wisdom, and improve their decision-making skills (Yum et al. 2012). Lee et al. found that others’ decisions significantly influence lenders’ behavior, leading to a phenomenon called herd behavior (Lee & Lee 2012). This phenomenon also exists in the markets of the United States. The following behavior is good for boosting lending performance (Zhang & Liu 2012).
Lending behavior involves risk, because there is information asymmetry between borrowers and lenders. To a large extent, perceived information asymmetry affects individual behavior trends. Kim et al. built a trust model about online trading. They argued that trust, risk, and profit are the core factors to decide trade trends (Kim et al. 2008). Greiner and Wang’s research on Prosper has shown that reputation mechanism has a significant impact on lending behavior, and is an important means to reduce uncertain transactions (Greiner & Wang 2010). Domestic scholars’ research also suggests that contracts and credit are the basis of contact between enterprises and consumers, who are influenced by multiple factors such as information authenticity and information transparency (Qiaopei & Song 2012). Research on domestic customer to customer (C2C) online shopping shows that perceived credit score and perceived customer reviews are the key factors that influence customer trust (Ma et al. 2012).
According to the research above, we found that the core issues of P2P lending are information asymmetry in the process of trading and trading trust. Currently, research about P2P lending is mainly concentrated on the online lending market in the United States; studies of the Chinese market are still very limited, and rigorous empirical analysis is more scarce. On the other hand, although some scholars have realized that risk and trust have a comprehensive influence on the trading process, there are no unified trust models to depict online P2P lending behavior. Compared with existing literature, the main innovations of this paper are considering risk and trust in the lending decision and putting forward a theoretical lending behavior model suitable for China’s online market.
In this paper, perceived information asymmetry and trading trust are taken as the key variables that influence lending intention. At the same time, we consider lenders’ personal information and reputation, which play an important role in lending behavior. This information is used to build the online lending behavior theory model, which is shown in Fig. 1.
Perceived reputation
Reputation is embedded in social network (Nahapiet & Ghoshal 1998). Traditional microfinance theory shows that reputation can reduce the risk to lenders, because borrowers with higher reputations are more likely to keep their promises.
Freedman’s study confirms that reputation can relieve information asymmetry and adverse selection problems (Freedman & Jin 2008). Studies such as Lin’s also obtained a similar result; they think that reputation in the social network can effectively reduce information asymmetry in the process of trading (Lin et al. 2013). Greiner and Wang’s research (Greiner & Wang 2010) affirmed Lin’s conclusion further: they think that the biggest role of the borrower’s reputation is to help improve the borrower’s perceived integrity. They also found that the greater the borrower’s reputation, the greater the borrowing rate and the lower the loan interest rate. When the borrower’s credit rating is lower, the role of reputation is more obvious. This analysis shows that the borrower’s reputation is the basis of perceived investment risk for lenders. It is an important signal for lenders to measure borrowers’ degree of credibility, and it has a significant impact on lending decisions. The resulting basic assumptions are as follows:
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H1:
The lender’s perception of the borrower’s reputation has a negative effect on the perceived information asymmetry.
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H2:
The lender’s perception of the borrower’s reputation has a positive effect on the lender’s trust in the borrower.
Information integrity
Perceived information integrity refers to the accuracy of a lender’s perception and the completeness of borrower information (Kim et al. 2008). Because users are remote from each other in both time and space, the information that consumers get from network transactions is incomplete and continuously changing, thus information quality is not guaranteed. One way to reduce the perceived risk is to search for related information before buying a product. While searching for this information, consumers face the problem of information sources’ reliability. To reduce risk, they need to get high quality information. In online lending markets, borrowing list information is an important basis for borrowers to measure lenders’ degree of believability. Kumar’s research suggests that the borrower’s information integrity is an important factor of credibility. The information integrity of the borrower will have a significant impact on credit behavior (borrowing rates, borrowing rate of full scale) and quality of repayment (default). To a great extent, the authenticity and integrity of the information in the P2P network platform of the borrowing list influences the lender’s degree of perceived information asymmetry and trust. Based on the above analysis, we have made the following basic assumptions:
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H3:
The lender’s perception of information integrity regarding the borrower has a negative effect on perceived information asymmetry.
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H4:
The lender’s perception of information integrity regarding the borrower has a positive effect on the trust in the borrower.
Perceived information asymmetry
Perceived information asymmetry refers to a lender’s perception that borrowers may have more information than lenders and may take advantage of this by harming the lender’s interest (Pavlou et al. 2007). Perceived information asymmetry is a problem that concerns consumers. Researches on e-commerce and information systems show that trust is the cornerstone of all social activities. All types of uncertain factors in trading will hinder the generation of trust (Chen et al. 2008). Transaction risk raised by information asymmetry is the key factor in the formation of trust. To ensure the safety of investments with the same return, lenders are more willing to put money into investments where they think the borrowers are credible. Based on the above analysis, we think that when the borrower’s perceived information asymmetry is low, it will be easier for him or her to gain the trust of the lender. Thus, we have made the following basic assumptions:
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H5:
The lender’s perception of information asymmetry has a negative effect on the lender’s trust of the borrower.
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H6:
The lender’s perception of information asymmetry has a negative effect on the lending intention.
Trading trust
Trust is based on a belief that the trusted party will behave in a responsible manner in order to achieve the expectations of another party (Pavlou & Gefen 2004; Pavlou 2003). Many studies have shown that trust will significantly impact individual behavior (Chen et al. 2008). Pavlou and Gefen’s research shows that trust can have an effect on decision-making behavior and attitudes (Pavlou & Gefen 2004). Pavlou and Gefen surveyed 127 respondents who had used the Amazon.com shopping site and researched the relationship between trust and consumer behavior. Their results show that a high degree of trust is associated with a high proportion of actual consumption. Online lending involves money transactions, and consideration regarding the safety of investment prompts lenders to require a stronger sense of trust to make a deal. The result is the following basic assumption:
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H7:
The lender’s trust of the borrower will positively influence the lending intention.
Study design
Variable measurement and questionnaire design
To guarantee the validity of the scale, we used various methods to design our questionnaire. First, all scales refer to the results of authoritative literature. Second, we invited industry experts to participate in a discussion to modify the scales to ensure their readability and effectiveness. Finally, we undertook a preliminary investigation in a small range and a preliminary analysis of the measuring scale; according to the results analysis and respondents’ feedback, we modified the semantics and wording of the scale, determining the final questionnaire. The questionnaire uses a multiple projects Likert scale method and a total of seven points (1 = “completely disagree” and 7 = “completely agree”).
Data collection process
We used an online survey to collect data. All respondents were registered as PPDai.com users. PPDai.com advised users of the survey and posted links on users’ main personal interface. Users who completed the questionnaire will get paid and have an opportunity to participate in a draw. To exclude the interference of invalid questionnaires, respondents were asked to supply their platform-registered account identification and information from the borrowing list, interest rates, and borrower’s credit rating. Questionnaire information that did not accord with the actual information was invalid. We collected 205 valid questionnaires in total.
Descriptive statistics of data sample
Respondents in the data sample are mainly male (84.88 %) and relatively young (respondents aged 21–25, 26–30, and 31–40 accounted for 29.76 %, 32.2 %, and 29.76 % of the sample, respectively). Their education level is relatively high (those with college graduate or undergraduate degrees accounted for 55.61 %), and those with a monthly income of 2000 yuan or above accounted for 77.08 %. Most respondents had used the lending platform network within one year. However, there was a large distribution of usage rates: those who had used it three times or less and 10 times or more accounted for 65.86 % and 22.93 %, respectively. Demographic characteristics of the sample and demographic characteristics of all registered users of the site are roughly the same, which means that the sample is representative.