Inf Technol Manag (2014) 15:239–254 DOI 10.1007/s10799-014-0187-z

A trust model for online peer-to-peer lending: a lender’s perspective

Dongyu Chen • Fujun Lai • Zhangxi Lin

Published online: 31 May 2014 Ó Springer Science+Business Media New York 2014

Abstract Online peer-to-peer (P2P) lending is a new but Keywords Online peer-to-peer (P2P) lending Á Trust Á essential financing method for small and micro enterprises China that is conducted on the Internet and excludes the involve- ment of collateral and financial institutions. To tackle the inherent risk of this new financing method, trust must be 1 Introduction cultivated. Based on trust theories, the present study devel- ops an integrated trust model specifically for the online P2P The question of financing small and micro enterprises lending context, to better understand the critical factors that (SMEs) in an effective and efficient way has attracted drive lenders’ trust. The model is empirically tested using much attention from both academics and practitioners. The surveyed data from 785 online lenders of PPDai, the first and financing problem is especially critical in developing largest online P2P platform in China. The results show that countries like China. According to a report from the Chi- both trust in borrowers and trust in intermediaries are sig- nese Government Research Center, approximately 50 % of nificant factors influencing lenders’ lending intention. SMEs in China face financial constraints. With advances in However, trust in borrowers is more critical, and not only information technologies, a new type of financing method, directly nurtures lenders’ lending intention more efficiently online peer-to-peer (P2P) lending has, since 2005, become than trust in intermediaries, but also carries the impact of an important supplement to traditional financing. Online trust in intermediaries on lenders’ lending intention. To P2P lending allows people to lend and borrow funds develop lenders’ trust, borrowers should provide high- directly through an online intermediary without the medi- quality information for their loan requests and intermediaries ation of financial institutes. should provide high-quality services and sufficient security P2P lending has experienced rapid growth in recent protection. The findings provide valuable insights for both years around the world, including the UK., the US, Japan, borrowers and intermediaries. Sweden, Canada, and China [1]. Prosper.com, one of the largest online lending intermediaries in the world, has attracted over 1 million members and facilitated over D. Chen Á F. Lai 32,000 loans, totaling over $193 million [2]. As a leading Dongwu Business School, Soochow University, Suzhou 215000, China platform in China, PPDai (www.PPDai.com) has attracted e-mail: [email protected] 500,000 members and facilitated about 100 million RMB in loans in 2011. & F. Lai ( ) Online P2P lending has several unique characteristics College of Business, University of Southern Mississippi, Long Beach, MS 39560, USA that differ from traditional e-commerce business models. e-mail: [email protected] First, the ‘‘goods’’ exchanged on online P2P platforms are neither tangible products nor services, but rather the rights Z. Lin to claim principle and interests in the future. Second, The Rawls College of Business Administration, Texas Tech University, Lubbock, TX 79409, USA lenders make lending decisions mainly based on the risks e-mail: [email protected] and benefits of a lending transaction rather than on the 123 240 Inf Technol Manag (2014) 15:239–254 quality of the goods, services, logistics, or anything else. Table 1). These platforms employ similar lending proce- Third, the escrow systems that are used by traditional dures. The potential user who intends to borrow or lend e-commerce for product and service exchange are not must create an account, providing personal information, readily applied to online P2P lending settings. In traditional such as name, address, phone numbers, and social security consumer-to-consumer (C2C) e-commerce (e.g. Taobao in number. Some online P2P lending platforms (e.g., Prosper) China and eBay in the US), the intermediaries hold the also require users to provide bank account information. The funds from buyers and transfer them to sellers only after information is then verified and a credit number is assigned the buyer confirms they have received the product or ser- accordingly. For members of Prosper, a credit score is vice. Such an escrow system cannot be applied in online extracted directly from Fair, Isaac Credit Organization P2P lending because the funds themselves are the exchange (FICO). However, there is no such agency to provide credit object. Therefore, the transactional behaviors of online P2P scores in China, so borrowers’ credit scores are calculated lending may not be the same as those in traditional based on the information they provide, such as ID number, e-commerce business settings. In addition, previous studies bank account, income, age, and occupation. have mainly focused on developed countries, whose results Borrowers deemed creditworthy are invited to create their may not be applicable to Chinese settings. To better borrowing listings. The listings are essentially loan requests understand the lending behaviors in China’s online P2P that specify the amount they seek, the maximum interest rate lending platforms, further research on China’s online P2P they will pay, and other optional information, such as free- lending is warranted. format descriptions of loan purpose. Lenders make lending Online P2P lending is inherently high risk; it is not only decisions according to the listing information and the bor- characterized by uncertainty, but also by anonymity, lack rower’s personal information. On most P2P lending platforms, of control, and potential opportunism [3]. On online P2P such as Prosper in the US and PPDai in China, a lender lending platforms, lenders and borrowers are not able to chooses to finance only a portion of a loan, rather than the communicate face-to-face and funds trading is conducted entire loan. For instance, a lender can bid a minimum amount online. There is a high level of information asymmetry of $50 on Prosper. Borrowers can choose either a closed or between borrowers and lenders [4], which presents a sig- open auction format. In the closed format, the auction closes as nificant barrier to the further development of this market- soon as the total amount requested is reached. The loan’s place. P2P lending faces a variety of risks either from the interest rate is that specified by the borrower in the listing. In implicit uncertainty of using a sophisticated technological the open format, the auction is open for a pre-assigned period. infrastructure or from the conduct of borrowers involved in Even if the entire amount requested is funded, lenders can online transaction [3]. Prior studies have also reported that continue to bid down the interest rate. trust plays a central role in online transactions [5–8]. Once the bidding process ends, the listing is closed and Therefore, initiating trust between borrowers and lenders is submitted to the lending intermediary for further review a critical issue for online P2P lending. Previous studies [1]. Borrowers may be asked to provide additional docu- have investigated the antecedents of trust from a variety of mentation and information. If the lending is approved, perspectives in the e-commerce context, such as online funds are directly transferred from the winning bidder’s purchasing (e.g., [8–10]), the adoption of Internet banking account to the borrower’s account. In general, service fees (e.g., [11]), mobile payment (e.g., [12, 13]), and virtual are charged to both borrowers and lenders by the inter- community development (e.g., [14, 15]). However, few mediary. The borrower’s payback is also directly trans- studies consider this issue in the context of the online P2P ferred from the borrower’s account to the lender’s account. lending marketplace. If the payback is overdue beyond a pre-determined limit, The remainder of this paper is organized as follows. We such as 2 months on Prosper, the borrower’s default will be first briefly present the background of online P2P lending recorded and submitted to credit bureaus and then debt and then review the related literature, followed by devel- collection is initiated. oping a conceptual model with hypotheses. Subsequently, Although P2P lending has been growing rapidly in we present the research methodology and test the hypoth- China, it is still in the initial stages of development. The eses. Finally, we discuss the findings and implications and first online P2P lending platform, PPDai (ppdai.com), was make a conclusion. established in July 2007. Due to differences in legislation, credit systems, and network security, many unique prob- lems face China’s online P2P lending that may not exist in 2 Online P2P lending background developed countries. The most important problem is the lack of a legal basis in the supervision of online P2P There are several commercial lending platforms, such as lending intermediaries and the lack of safety guarantees for Prosper, PPDai, Lending Club, Zopa, and Easycredit (see lenders [16]. 123 Inf Technol Manag (2014) 15:239–254 241

Table 1 Online P2P lending Region Intermediary Start Region Intermediary Start intermediaries year year

US Prosper 2006 China Yixin 2006 Zopa, LendingClub, VirginMoneyus, 2007 PPDai, Qifang, 2007 Loanio, Mircroplace, Fynanz Wokai People Capital, Zimple Money 2008 My089 2009 Zidisha 2009 ChangDai 2010 Vittana 2010 France BabyLoan 2009 Multi-national Kiva 2005 UK Zopa 2005 Microplace 2007 FundingCircle 2010 Italy Zopa 2007 Canada IOUCentral 2008 Boober 2007 CommunityLend 2008 Poland Kokos 2008 Japan Zopa 2008 Monetto 2008 Denmark Fairrates 2007 Australia IGrin 2007 Holland Boober 2007 Sweden Loanland 2007 Africa MyC4 2006 Germany Smava 2007

3 Related studies on P2P lending 4 Theoretical background and conceptual model

Several studies have been conducted on the behaviors of High risk is inherent in P2P lending, in particular for online P2P. Based on open data from Prosper, researchers lenders. It is vital for lenders to identify credible borrowers have found that information from borrowers and loan and choose the right lending intermediary. On this basis, requests are critical to lenders’ decisions. For instance, Lin for P2P lending to succeed, trust must be established at the [17] revealed that the lower the credit level of a borrower, very beginning [10]. Therefore, it is critical to investigate the less likely his/her loan listing will be funded. Collier the key factors in lenders’ trust-building processes. and Hampshire [18] discovered that information of both loan amount and debt/income ratio of a borrower influence 4.1 Conceptual model the final interest rate of a loan. Some scholars have also found that the social relationship information of a borrower Trust is a complex behavior, which has been defined from influences loan success, interest rate, and default proba- several different perspectives in a variety of disciplines. bility. For example, Lin et al. [4] found that the relational For instance, in psychology, trust is defined as an expec- aspect of social capital is a reliable signal that indicates a tation that ‘‘an exchange partner will not engage in borrower’s trustworthiness. Greiner and Wang [19] pointed opportunistic behavior, despite short-term incentives and out that social capital plays a more important role for uncertainties about long-term rewards’’ [22]. In sociology, borrowers with lower credit levels. it is defined as ‘‘a particular level of the subjective prob- Although P2P lending has been attracting increasing ability with which an agent assesses that another agent or interests from practitioners in China, research on it is still group of agents will perform a particular action, both scarce, theoretical studies in particular. Among them, for before such action can be monitored and in a context in example, Chen et al. [20] explored the critical antecedents which it affects his own action’’ [23]. In management of lenders’ trust in borrowers in China and found that areas, trust is defined as the willingness of a party to be structural social capital, relational social capital, and dis- vulnerable to the actions of another party based on the position to trust are important in initiating trust in the expectation that the other will perform a particular action lending process. Xu et al. [21] made a comparison of the important to the trustor, irrespective of their ability to online lending marketplace between China and other monitor or control the other party [24]. countries and found that cultural factors may influence When there is uncertainty as to how others will behave, online lending business models as well as lenders’ trust is a prime determinant of what people expect from the behaviors. situation and how they behave [10]. Therefore, trust is a

123 242 Inf Technol Manag (2014) 15:239–254

Specific Trust Beliefs General Trust Beliefs Outcome Trust Belief

Knowledge - based

Familiarity

Trust in H7 Institution -based Intermediary

Service Quality

Conative Security Personality - based Protection Willingness to Lend Disposition to Trust

Cognition - based

Social Capital

Trust in H8 Borrower Information Quality Perceived Benefit

Fig. 1 Conceptual model. Solid lines hypothesized relationships; Dashed lines controls; Glow boxes specific trust beliefs; Bevel boxes general trust beliefs; 3D Rotation box outcome trust belief central aspect in many economic transactions, including The model is contextualized to the online P2P context, e-commerce. In the online P2P context, trust is critical in where specific trust beliefs are delineated as knowledge- fulfilling lending transactions because of the high risk of based, institution-based, and cognition-based, while gen- borrowers engaging in opportunistic behaviors. Although eral trust beliefs are described as trust in intermediary and there are no studies on trust building in the online P2P lending trust in borrower. Various variables are contextualized for context, there are a number of studies on trust building in other the specific trust beliefs in the online P2P context. For related contexts, such as e-commerce e.g., [25–27]. example, familiarity is a variable for a knowledge-based In the literature, trust has been examined through the specific trust belief, service quality and safety as institu- framework of ‘‘antecedents–trust–outcomes’’ [28]. In this tional-based and social capital and information quality as framework, trust is conceptualized as specific trust beliefs cognition-based specific trust beliefs. These specific and and general trust beliefs [24, 29]. Specific trust beliefs deal general trust beliefs are further deliberated as follows. primarily with the characteristics of trustees, while general trust beliefs deal primarily with the overall impressions of 4.2 Specific trust beliefs trustees [10]. Specific trust beliefs are framed as anteced- ents to general trust beliefs [24, 30] and general trust In the context of e-commerce, Gefen et al. [10] identified beliefs lead to behavioral intention [31]. Gefen et al. [10] specific trust beliefs as cognition-based, institution-based, thought that the distinction between specific and general knowledge-based, calculative-based, and personality- trust beliefs was applicable in the context of online trans- based. The first four types of trust antecedents are mainly actions. Therefore, we frame our conceptual model with relevant either to the characteristics of trustees or to the specific trust beliefs as antecedents of general trust beliefs relationships between trustees and trustors, while person- and behavioral intention as the outcome of general trust ality-based trust relates to the personalities of trustors and beliefs, as depicted in Fig. 1. is irrelevant to trustees [29, 31].

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Cognition-based trust refers to the rational assessment online P2P intermediary, it can be cultivated neither by of characteristics demonstrated by trustees. Individuals borrowers nor by intermediaries. Thus, personality-based assess trustee’s trustworthiness based on first impressions trust is included as a control in the model. through second-hand information [32], and tend to place Table 2 summarizes ten widely cited articles, which more trust in people similar to themselves [10]. This kind examined specific trust beliefs as antecedents of general of trusting belief is formed via categorization and illusions trust beliefs in e-commerce contexts. The studies listed in of control [10]. Overall, cognition-based trust is utilized to this table are selected from the leading IS journals, gain some sense of control in an uncertain situation when a including Information System Research, MIS Quarterly, trustor has no prior first-hand experience with the trustee Journal of Management Information Systems, Omega, [29]. Information and Management, International Journal of Institution-based trust involves third parties (i.e., lend- Electronic Commerce, and The Journal of Strategic ing intermediaries), and refers to the trust based on guar- Information Systems. antees and commendations from third parties [33]. Such institution-based trust is ‘‘especially suited for online marketplaces where buyers predominantly transact with 4.3 General trust beliefs new and unknown sellers under the aegis of third parties who provide an institutional context’’ [34, p. 38]. 4.3.1 Trust in borrower Knowledge-based trust antecedents suggest that trust develops as a result of the aggregation of trust-related Trust in borrower is conceptualized in this study as a belief knowledge by the parties involved [35]. Once a trustor that the borrower will act cooperatively to fulfill the len- obtains sufficient knowledge and information of the trustee, der’s expectations without exploiting his or her vulnera- he is more likely to engage in trustworthiness assessment bilities [6]. Trust in borrower is of vital importance for based on the knowledge and information obtained [36]. lending success. Although P2P lenders are able to select This is because knowledge-based trust beliefs, such as loan requests from multiple potential borrowers, they are familiarity, allow individuals to better predict the behaviors often not familiar with these borrowers and repetitive of trusted parties, and hence to reduce the possibility that transactions between lenders and borrowers are unlikely they may mistakenly feel that they are being unfairly taken [42]. Therefore, the lender’s trust in the borrower is ex ante advantage of [31]. in nature. Due to the lack of repetitive transactions, ex-ante Calculative-based trust is derived from an economic trust is primarily cognition-based. Such cognition-based analysis, interpreting trust as ‘‘it is not worthwhile for the trust relies on rapid, cognitive cues of first impressions other party to engage in opportunistic behaviors’’ and ‘‘if [43], rather than experiential personal interactions [44]. the costs of being caught outweigh the benefits of cheating, Due to the fact that lenders’ trust in borrowers is based then trust is warranted since cheating is not in the best on the former’s first impression of the latter, lenders often interest of the other party’’ [10, p. 64]. Such trust is built if act on information that is incomplete and far from perfect an individual believes that the trusted party has nothing to [10]. They are thus often exposed to a high level of gain from being untrustworthy. Calculation-based trust is uncertainty and risk in their lending decisions, especially not included in the model, because it is not appropriate for since the transactions are monetary in nature. Therefore, China’s P2P context. There is no national credit system and lenders would seek to assess borrowers on a full spectrum. law enforcement is weak in China. Defaulted borrowers There are two ways for lenders to assess borrowers. The may lose very little, if anything. Therefore, borrowers on first is direct assessment of the information quality of loan China’s P2P lending platforms do indeed have reason to requests, such as reliability and the sufficiency of the engage in opportunistic behaviors. On this basis, we request information. The information provided in the bor- believe that lenders have no, or very low if any, calcula- rower’s requests may directly reflect whether he is honest tion-based trust in borrowers on China’s P2P platforms, and behave professionally. The second is indirect assess- and so this form of trust is excluded from the model. ment. Although there are no repetitive transactions between Personality-based trust refers to the tendency to believe a particular lender and borrower, the borrower might have or not in others and so to trust them [10, 24, 29]. A person already made multiple requests on the platform and inter- with a greater disposition to trust may tend to trust others. acted with other lenders. These previous requests and Such trust belief is credit given to others before experience interactions with other lenders are the borrower’s social can provide a more rational interpretation [10]. It is related capital, which may serve as a proxy for reliability and to an individual’s personality and is especially important in honesty. On this basis, we include both direct assessment the initial stages of a relationship [29]. Although lenders’ (i.e. information quality) and indirect assessment (i.e. personalities may influence their trust in borrowers and in social capital) as cognition-based trust beliefs. 123 244 Inf Technol Manag (2014) 15:239–254

Table 2 Specific trust beliefs Study Specific trust beliefs Cognition Institution Knowledge Calculative Personality Others -based -based -based -based -based

McKnight et al. [7] 44 Gefen and Straub [37] 444 4 Gefen et al. [10] 444 4 4 4 Koufaris and 44 4 Hampton-Sosa [38] Pavlou [8] 44 Pavlou and Gefen [34] 44 Pavlou [39] 44 Teo et al. [40] 44 4 McKnight et al. [7] 4 Pavlou and Fygenson [41] 444 4

4.3.2 Trust in intermediary between the lender and the intermediary. Previous experi- ence may serve as an indirect assessment of the interme- Like C2C e-commerce, online P2P lending involves not diary. For example, lenders who have used an intermediary only a buyer (i.e., the borrower) and supplier (the lender) very often and for a long time may have greater trust in it. but also an intermediary (e.g., Prosper in the US and PPDai in China) [10]. The lending intermediary is a platform (i.e., marketplace) that uses Internet structure to facilitate lend- 5 Research hypotheses ing transactions among potential borrowers and lenders in an online marketplace by collecting, processing, and dis- 5.1 Antecedents of trust in intermediary seminating information [34, 45]. Lenders must put their trust not only in borrowers but also in intermediaries. Trust For lenders’ trust in intermediaries, lenders assess the in lending intermediary is thus defined as the subjective intermediary directly based on safety and service quality belief with which a lender believes that the intermediary and indirectly according to their previous experience with will institute and enforce fair rules, procedures, and out- the intermediary. Therefore, two trust antecedents are comes in its marketplace competently, reliably, and with incorporated into trust in intermediary—institution-based integrity, and, if necessary, will provide recourse for trust and knowledge-based trust. The safety protection and lenders to deal with borrowers’ opportunistic behaviors service quality of the intermediary serve as institution- [34]. based trust and the lender’s familiarity with intermediary Similar to lender’s trust in borrowers, lender’s trust in an serves as knowledge-based trust. intermediary is also assessed from two sources, direct and Familiarity refers to lenders’ familiarity with a lending indirect. The direct assessment is based on whether the intermediary through interaction. When lenders acquaint intermediary is safe for the transaction and whether it themselves with an intermediary, they become more provides high-quality services. Since P2P lending transac- familiar with the intermediary’s behavior patterns, and so tions are monetary in nature and the lenders bear much they can fairly predict the intermediary’s behaviors based higher risk than borrowers, lenders have great need for the on the information they obtained from previous interac- intermediary to safeguard their funds. The lender’s trust in tions [7, 46]. This predictability may result in trust in an the intermediary is in general based on transaction safety intermediary, because ‘‘familiarity leads to an under- the intermediary can provide, such as escrow services, standing of an entity’s current actions while trust deals with fraud protection, authentication, and verification. Other beliefs about an entity’s future actions’’ [46, p. 551]. than the core features (e.g., safety and protection), lenders Lenders who have had pleasant experiences with an also expect the intermediary to provide high-quality ser- intermediary would stick with that intermediary and vices to facilitate the transactions, such as a web site that become more familiar with it. This stickiness reflects a runs 24/7. lender’s trust in an intermediary. The lenders who have had In contrast to lenders’ trust in borrowers, which is ex bad experiences with an intermediary would trust it less be ante in nature and generally based on first impressions, it is less familiar with it, and leave it. Prior literature has more likely that there have been repetitive interactions examined familiarity in the e-commerce context and

123 Inf Technol Manag (2014) 15:239–254 245 revealed that familiarity positively relates to trust in 5.2 Antecedents of trust in borrower e-commerce websites [e.g., 15]. Therefore, we propose: For lenders’ trust in borrowers, lenders directly and indi- H1: A lender’s familiarity with a lending intermediary rectly assess borrowers based on their first impression. The positively affects the lender’s trust in the intermediary. direct assessment is based on the information quality of the Service quality refers to the quality of functions and lending requests. The indirect assessment is based on the supportive activities provided by the intermediary to make borrower’s social capital information. the P2P lending experience more smooth and pleasant. Social capital refers to a borrower’s resources, which There are two categories of service quality: experiential can be accessed through social networks in the lending (such as responsiveness and reliability) and structural (such intermediary [46]. The majority of lending websites offer as flexibility and assurance) [47]. Experiential service social networking services such as communities and bul- quality refers to providing a prompt response to lenders’ letin boards. Such social capital information can be easily requests and comments, as well as providing uninterrupted accessed by other users. Borrowers can communicate with support 24/7, which provides lenders a more pleasant lenders and other borrowers and seek lending opportunities lending experience. Structural service quality refers to through social networks. The social network members with providing more flexible (e.g., more fund transferring a good reputation are more respected by others and their methods, such as by mobile phones, online banking, and online behaviors are more creditable. Thus, borrowers in ATM deposit) and safe (e.g., transaction encryption, bor- general aim to build up their social networks to accumulate rower authentication, and escrow) services, which meant social capital. Borrowers with more social capitals are that lending can be conducted efficiently and effectively. deemed more trustworthy. Borrowers’ opportunistic Numerous studies have shown that both of these categories behaviors may drain their social capital and lead to sanc- are critical to creating customer value and developing tions from other social network members. Therefore, social customer satisfaction e.g., [48, 49]. Lenders get more value capital may serve as an important signal of borrowers’ from high-quality services and are more satisfied with their trustworthiness. This signal can play a vital role in a experience, and expect the same pleasant experience in marketplace, because borrowers’ social capital is difficult future, thus place more trust in an intermediary. More to develop, but readily accessible for lenders [1, 18]. On importantly, high-quality services inspire a lender’s confi- this basis, we propose: dence in an intermediary’s reliability, capability, and H4: A borrower’s social capital positively affects a len- integrity [50]. Therefore, we propose that an intermediary der’s trust in the borrower. that consistently provides high service quality to lenders will cultivate more trusting relationships with lenders. That Information quality refers to a lender’s perception of the is: accuracy and completeness of the information provided by a borrower in his borrowing listing. Due to the lack of a H2: The service quality of a lending intermediary pos- national credit system in China, the listing description is itively affects the lender’s trust in the intermediary. the first and most significant means for lenders to assess borrowers. Prior studies on P2P lending revealed that the Safety and protection refers to lenders’ perceptions that listing information has a significant impact on lending a lending intermediary will fulfill security requirements, outcomes such as loan success and interest rate [8, 52–54]. such as authentication, integrity, encryption, and non- Such an impact is especially prominent in regions with less repudiation [10, 49, 51]. The exchange object of online P2P mature legal systems such as China. In such regions, lending is monetary fund, so it shares the same inherent lenders are less likely to be capable of claiming their rights risks as other financial activities. The safety and protection through legal actions when facing loan default and fraud. provided by an intermediary reflect the intermediary’s Therefore, lenders must place more importance on infor- effort to reduce lenders’ risk. Only when lenders perceive mation provided by the lending intermediary and borrow- the intermediary to provide sufficient protection do they ers to evaluate borrowers’ trustworthiness. The majority of perceive their funds to be safe, and thus trust the inter- lending platforms provide an attachment uploading func- mediary. Prior studies revealed that safety and protection tion so borrowers can provide materials they consider are important antecedents of trust for activities involving beneficial for their creditability. high risks, such as adoption of mobile payments [46] and The information quality of loan listings serves two online purchases [13]. Therefore, we posit that: purposes. First, it facilitates the lender’s assessment of the H3: The safety and protection provided by a lending fundability of a request. The information for this purpose intermediary positively affect a lender’s trust in the includes loan amount, duration, interest rate, etc. The intermediary. information on the loan purpose is also important. If it is 123 246 Inf Technol Manag (2014) 15:239–254 convincing and verifiable, the request is more trustworthy. the spurious relationship between trust in intermediary and For example, for a funds request proposed to improve the trust in borrower, the lender’s disposition to trust is borrower’s eBay store, the address of the borrower’s front incorporated as a control for both. store on eBay, if provided, will greatly facilitate the len- der’s evaluation of the request and build lender’s trust in 5.4 Outcomes of general trust beliefs the borrower. Second, information quality serves as a proxy to assess As discussed above, lenders’ risk is from both intermediary the borrower’s creditability. The high quality of the listing and borrowers, so they assess their willingness to lend in information reflects how serious, sincere, and professional relation to both the intermediary and borrower involved. the borrower is, which influences the lender’s confidence in Trust in intermediaries and borrowers can help lenders the borrower. A high-quality request indirectly reflects the ‘‘subjectively rule out many undesirable possible behaviors borrower’s capability to understand and execute the pro- on the part of the party they trust’’ [34, p. 45]. Once trust posed plan using the loan, such that he is more trustworthy. overcomes social uncertainty, a more positive attitude Thus, we propose: towards lending will be created, which in turn leads to lending intention. Prior studies also indicated that purchase H5: The information quality of a borrower’s loan request intention is not only influenced by a customer’s trust in the positively affects lender’s trust in the borrower. vendor, but also by their trust in intermediaries (e.g., [34, 57]). Such findings have also been validated in the context 5.3 Trust in intermediary and trust in borrower of virtual communities [58]. Therefore, we propose that a lender’s willingness to lend is influenced by both trust in A lender’s trust in an intermediary comprises two aspects: the intermediary and trust in the borrower: (1) the intermediary’s technical protection, and (2) the good standing of its borrower base and rigorous transaction H7: The lender’s trust in an intermediary positively regulations. These two aspects also lead lenders to trust affects the lender’s willingness to lend. borrowers, because technical protection prevents borrower H8: The lender’s trust in a borrower positively affects the frauds and a borrower base with good standing and rigor- lender’s willingness to lend. ous regulations lower the probability of borrower defaults. Due to the high risks lenders bear, the safety and pro- In addition, perceived benefit may also be a critical tection of lenders are the intermediary’s first priorities. To determinant of willingness to lend. As this paper mainly alleviate a lender’s risk, the intermediary needs not only to aims to develop a trust model for lenders in P2P lending, utilize high technologies such as encryption and authenti- perceived benefit is used as a control for willingness to cation to protect the lender’s funds, but also to screen lend. potential borrowers and rigorously monitor loan transac- tions. In addition, the intermediary also institutes regula- tions that restrict borrowers’ potential to engage in 6 Methodology opportunistic behavior and provides guidelines of what constitutes acceptable transaction behavior [34]. The To ensure the content validity of the measures, we adapted membership registration screening and transaction regula- them from previous studies and pilot tested them prior to tions reduce the probability of borrower defaults. The low the formal data collection. The finalized instrument com- probability of borrower fraud and default help lenders trust prises two parts, as presented in ‘‘Appendix’’. The first part borrowers. Therefore, when lenders trust an intermediary, collects respondents’ demographic information, such as they perceive the association between borrowers and the gender, age, education, income and their information on intermediary and their trust in the intermediary is cascaded the intermediary. The second part is for main constructs, from intermediary to borrowers. This is called trust trans- including trust, familiarity, service quality, security pro- ference [55]. Therefore, we propose that a lender’s trust in tection, social capital, information quality, and willingness an intermediary may lead to the lender’s trust in a borrower to lend. Familiarity was measured as the monthly fre- whose behaviors are regulated and restricted by the quency and the number of years of using the intermediary. intermediary: The other constructs were anchored with a 7-point Likert scales, ranging from 1 (disagree strongly) to 7 (agree H6: The lender’s trust in an intermediary positively strongly). affects the lender’s trust in a borrower. To conduct this study, we first obtained the permission Extensive studies revealed that trust beliefs are also and collaboration of a leading P2P intermediary in China, affected by an individual’s personality [10, 56]. To rule out PPDai (www.PPDai.com). PPDai sent a message

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Table 3 Demographic information of respondents response bias. In addition, we also compared the respon- Frequency Percentage dents’ profile with the profile of PPDai’s lender population and no significant difference was found, indicating no Gender severe non-response bias. The respondent profile demo- Male 672 86 graphics are summarized in Table 3. Female 113 14 Age Below 20 55 7 7 Data analysis 21–30 484 62 31–40 203 26 Structural equation modeling with LISREL 8.70 was Above 40 43 5 applied to analyze the data. The model was estimated by Education maximum likelihood (ML). The two-step procedure [59] High school or below 279 36 was followed. First, the measurement model was examined College 465 59 to assess construct reliability and validity. Then, the Graduates 41 5 structural model was tested to evaluate the causal rela- Income tionships among the theoretical constructs. We had 32 Below 2,000 RMB 192 24 items in this model and 785 responses, an adequate sample 2,000–3,000 RMB 175 22 size for our model according to the ‘‘ten times’’ rule of 3,001–5,000 RMB 207 26 thumb, which requires the sample size to be at least ten 5,001–8,000 RMB 112 14 times the number of items in the model [60]. 8,001–15,000 RMB 70 9 Above 15,000 RMB 29 4 7.1 Measurement model Years of lending intermediary use 2 \1 year 581 74 The model fits the data well, with v = 1140.93 and df 1–2 years 122 16 = 288. The goodness-of-fit indexes are CFI = 0.98, NFI = 0.97, and NNFI = 0.98, greater than the limit of 2–3 years 36 5 0.95. The RMSEA is 0.065, lower than 0.10, the suggested More than 3 years 46 6 cut-off value for complex models. Frequency of lending intermediary use(per month) Reliability, convergent validity, and discriminant \1 times 289 37 validity of the multi-item scales were assessed by follow- 1–3 times 252 32 ing the guidelines of Fornell and Larcker [61] and Gefen 4–6 times 51 6 and Straub [62]. Except for perceived benefit (0.68), the 7–9 times 20 3 values of Cronbach’s alpha are [0.7. All composite reli- More than 9 times 173 22 ability values are[0.8 (see Table 4), suggesting acceptable reliability. explaining the research purpose to 1,500 of its lenders, who Convergent validity is assessed in terms of factor load- were randomly selected from its lender database. The ings and average variance extracted (AVE). As shown in lenders were asked to fill in our online questionnaire. To Table 4, all 32 items have loadings greater than 0.7 and are encourage their participation, we offered a nominal gift of significant at the p \ 0.01 level, suggesting convergent a 50 RMB coupon from PPDai and entry into a draw to win validity at the item level. All AVE values are[0.5, the cut- an Apple iPod touch or iPod shuffle. To reduce the possi- off value, suggesting acceptable convergent validity at the bility of multiple responses from the same lender, partici- construct level [62]. pants were required to provide their mobile phone Discriminant validity was assessed by (1) examining numbers. Repeat responses from the same mobile phone whether the squared root of each construct’s AVE was numbers were filtered out. larger than any inter-correlation between this focal con- A total of 938 responses were collected. After a careful struct and all other constructs; and (2) examining whether comparison of the data (e.g., personal ID, and borrower’s each item loading was substantially higher on its principal ID on PPDai) collected from the questionnaires with those construct than on other constructs [61]. The results show of the PPDai database, invalid responses were screened out that the cross-loading differences are higher than the sug- and a total of 785 valid responses were obtained for use in gested threshold of 0.1 [62], and the square root of each the analysis. AVE is larger than the inter-correlations of the construct We compared the demographic variables of the early with the others (See Table 5). These results suggest ade- (the first month) and late responses (later months) to assess quate discriminant validity. 123 248 Inf Technol Manag (2014) 15:239–254

Table 4 Descriptive statistics Construct Item Loading T-value Composite Cronbach’s Average variance and measurement model reliability alpha extracted

Disposition to trust(DT) DT1 0.88 24.92 0.89 0.82 0.73 DT2 0.91 41.70 DT3 0.77 11.93 Information quality (IQ) IQ1 0.71 7.65 0.85 0.73 0.65 IQ2 0.85 17.31 IQ3 0.85 22.06 Perceived benefit(PB) PB1 0.82 3.66 0.82 0.68 0.61 PB2 0.70 5.85 PB3 0.76 8.82 Social capital (SC) SC1 0.78 12.04 0.85 0.74 0.65 SC2 0.82 15.70 SC3 0.83 21.96 Security protection (SP) SP1 0.81 15.52 0.84 0.72 0.64 SP2 0.74 7.61 SP3 0.84 17.96 Service quality (SQ) SQ1 0.77 12.20 0.86 0.76 0.68 SQ2 0.85 16.64 SQ3 0.85 23.77 Trust in borrower (TB) TB1 0.85 25.54 0.88 0.79 0.71 TB2 0.86 20.52 TB3 0.81 17.07 Trust in intermediary (TI) TI1 0.86 21.72 0.89 0.82 0.74 TI2 0.88 27.16 TI3 0.83 15.43 Willingness to lend (WL) WL1 0.82 18.86 0.88 0.80 0.72 WL2 0.86 25.12 WL3 0.86 22.51

Table 5 Correlations of DT IQ PB SC SP SQ TB TI WL VIF constructs Disposition to trust (DT) 0.86 2.23 Information quality (IQ) 0.65 0.81 2.82 Perceived benefit (PB) 0.62 0.66 0.78 2.26 Social capital (SC) 0.59 0.68 0.65 0.81 1.14 Security protection (SP) 0.45 0.50 0.44 0.49 0.80 2.55 Service quality (SQ) 0.49 0.51 0.47 0.49 0.50 0.82 1.67 Trust in borrower (TB) 0.60 0.66 0.57 0.60 0.45 0.53 0.84 1.93 The diagonal elements (in bold) Trust in intermediary (TI) 0.57 0.60 0.57 0.63 0.59 0.66 0.62 0.86 2.42 represent the squared roots of Willingness to lend (WL) 0.58 0.60 0.59 0.56 0.42 0.49 0.65 0.59 0.85 2.67 the AVE

Multicollinearity was also examined by assessing the by following the analytical procedure suggested by Pod- index of variance inflation factor (VIF) [62]. The VIFs for sakoff et al. [64]. CMV is present in data if one factor the constructs range from 1.14 to 2.82, less than the con- accounts for most of the covariance. The result of the factor servative threshold of 3.3 [63], suggesting that multicol- analysis showed that the first factor only accounts for linearity is at an acceptable level. 36.1 % of the total variance. Second, the correlation matrix In addition, common method variance (CMV) was reveals that the highest correlation is \0.90, indicating no assessed. First, we conducted Harmon’s single factor test severe CMV in the data [65]. 123 Inf Technol Manag (2014) 15:239–254 249

Familiarity

R2=0.78

Trust in Service Quality Intermediary

Safety R2=0.69

** Disposition to Willingness to H6: 0.21 Trust Lend

** Social Capital 0.53

Trust in Borrower Perceived Information Benefit Quality R2=0.68

Fig. 2 Structural model. v2=1195.74, df.=299, CFI=0.98, NFI=0.97, NNFI=0.98, RMSEA=0.07 **p\0.05; ns. not significant

7.2 Structural model has significant influence on trust in borrower (b = 0.21, p \ 0.05) while trust in borrower has significant influence The structural model was also analyzed using SEM. The on willingness to lend (b = 0.31, p \ 0.05). Therefore, a results are shown in Fig. 2. The model has v2 = 1195.74 mediation analysis was conducted to test whether trust in and df = 299. The goodness-of-fit indexes are CFI = 0.98, borrower carries the influence of trust in intermediary on NFI = 0.97, NNFI = 0.98, and RMSEA = 0.07, suggest- the lender’s willingness to lend. The indirect effect is ing an acceptable fit. The model explains 78, 68 and 69 % 0.21 9 0.31 = 0.07 with t = 2.98. It appears that although of the variances of trust in intermediary, trust in borrower, the direct effect of trust in intermediary on lender’s will- and lender’s willingness to lend, respectively. ingness to lend is not significant (b = 0.06), the indirect As shown in Fig. 2, among three antecedents of trust in effect through trust in borrower is significant (b = 0.07, intermediary, the influence of familiarity is not significant p \ 0.05). The total effect of trust in intermediary on (b = 0.02), while service quality (b = 0.49, p \ 0.05) and lender’s willingness to lend is 0.06 ? 0.07 = 0.13, which safety and protection (b = 0.30, p \ 0.05) significantly is significant (p \ 0.05). These analyses indicate that the influence trust in intermediary, suggesting support for H2 and overall influence of trust in intermediary on willingness to H3, but not for H1. Similarly, for trust in borrower, social lend is significant, while this influence is primarily present capital has no significant influence (b =-0.07), while in an indirect form through its nourishing of trust in bor- information quality has a large magnitude (b = 0.66, rower, suggesting support for H7. In addition, the influence p \ 0.05), suggesting support for H5 and not for H4. The of trust in borrower on willingness to lend (b = 0.31) is borrower’s disposition to trust has a significant impact on trust significantly greater than the influence of trust in interme- in borrower (b = 0.22, p \ 0.05) but its influence on trust in diary (total effect = 0.13), indicating that trust in borrower intermediary (b = 0.08) is not significant. After controlling plays a more critical role in influencing lender’s lending borrower’s disposition to trust, trust in intermediary has a willingness. significant influence on trust in borrower (b = 0.21, p \ 0.05), suggesting support for H6. After controlling the influence of perceived benefit (b = 0.53, p \ 0.05), trust in 8 Discussion borrower significantly influences lender’s willingness to lend (b = 0.31, p \ 0.05), suggesting support for H8. 8.1 Major research findings Although the direct influence of trust in intermediary on lender’s willingness to lend is not significant (b = 0.06), it This study proposed an integrated trust model to examine may exert influence through trust in borrower, because it lenders’ trust in China’s online P2P lending context.

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Lenders’ trust was examined from both intermediary and driving factors for general trust beliefs from both direct and borrower perspectives. Both antecedents and outcomes of indirect aspects. For trust in borrower, information quality lenders’ trust were incorporated into our model. For both was included as the lender’s direct assessment and social trust in borrower and intermediary, direct and indirect capital as indirect assessment. For trust in intermediary, antecedents were included. The model was tested using service quality, and safety and protection represent the responses from 785 lenders on China’s first and largest lender’s direct assessment while familiarity is the indirect online P2P lending platform, PPDai. The major findings are assessment. summarized below and the implications for research and Second, our study contributes to literature by examining practices follow. China’s online P2P lending. Although other e-commerce platforms have been examined extensively, online P2P lend- 1. In China’s online P2P context, trust in borrower plays ing platforms are still under researched. The monetary nature a central and essential role in influencing a lender’s and inherent high risk of online P2P also warrant further willingness to lend. First, trust in borrower is more research. In addition, China’s unique social, legal, institu- effective than trust in intermediaries in increasing a tional, and cultural environment poses challenges to online lender’s willingness to lend. Second, trust in borrower P2P lending. For example, law enforcement and contract spirit also significantly carries the influence of trust in in China are weaker than in developed countries. Without intermediary on a lender’s willingness to lend. other protection mechanisms, defaults in China will inevitably 2. It is more effective for borrowers to gain a lender’s trust be high. Therefore, the results from previous studies con- by providing high-quality information concerning their ducted on other e-commerce platforms and in developed loan requests than by building up social capital when countries may not be applicable to China’s online P2P context. there is a lack of stringent screening procedures for In fact, the findings of the present study are quite different social network membership in the lending intermediary. from previous studies, which are discussed as follows. 3. Service quality and safety protection help develop trust Third, in an environment with less mature legal systems in an intermediary, but such trust cannot be cultivated such as China, we found that trust in borrower is more from familiarity with the lending intermediary. critical than trust in intermediary in determining online lending intentions. Such a finding runs counter to previous 8.2 Research implications studies conducted in developed countries, which reported that trust in intermediary plays a more critical role than This study contributes to the online P2P literature in sev- trust in borrower [e.g., 34]. In addition, our study found eral ways. First, it proposes an integrated and compre- that trust in borrower plays two roles—it not only directly hensive trust model developed specifically for the online improves lender’s willingness to lend, but also carries the P2P context. Although the literature suggests knowledge- effect of trust in intermediary to influence the lender’s based, personality-based, institution-based, cognition- willingness to lend. based, and calculation-based specific trust beliefs as the Fourth, our findings revealed that the impact of social antecedents of general trust beliefs [c.f. 10], certain specific capital on trust might be subject to the lending environment. type of trust beliefs are not appropriate for the online P2P Our results indicate that in China’s online P2P context, the lending context. For example, due to the high risks lenders borrower’s social capital does not effectively influence trust bear and the little or no risk borrowers bear, calculation- in borrower. In contrast, in the context of Proper.com, an based trust appears inappropriate for the online P2P lend- online P2P platform in the US, social capital influences ing contexts. In addition, both trust in borrower at the willingness to lend to a great extent [42]. One possible individual level and trust in intermediary at the firm level reason for this discrepancy is the lending platform’s insti- were simultaneously incorporated into our model. Previous tutional arrangement. In the context of China’s PPDai, the studies examined the inter-personal trust or individual-to- requirements for group membership are loose. All registered firm trust separately. Furthermore, we developed our trust users on PPDai can add anyone as a friend and create a new model by further materializing specific trust beliefs. We group at any time. Such loose requirements may have ero- incorporated familiarity as knowledge-based trust, and ded the value of social capital, because such social capital is included service quality and safety and protection as insufficient for lenders to distinguish trustworthy borrowers institution-based trust, and social capital and information from untrustworthy ones. In contrast, on Prosper.com group quality as cognition-based trust belief, which further membership follows a stringent screening and verification materializes and operationalizes the concepts of specific procedure, which ensures members with high social capitals trust beliefs. More importantly, the incorporation of are more trustworthy. materializing variables (e.g., service quality, information Finally, our results revealed that knowledge-based trust quality, and familiarity) comprehensively delineates beliefs are not significant for developing general trust in 123 Inf Technol Manag (2014) 15:239–254 251 intermediary. This finding is not consistent with previous prove that they have been successfully funded multiple studies either. We conducted in-depth interviews with times and paid back their loans on time to join the elite several lenders, which revealed that they were not very level. The social capital of those members may improve satisfied with the services provided by any online P2P lenders’ trust in borrowers and willingness to lend. platform in China, but they had no better investment alternatives and thus had to reluctantly stick with P2P 8.4 Limitations and directions for future research lending platforms. It is especially true that in China many investors are unable to find reliable investment products. In While this study contributes to both the literature and recent years, almost all investment varieties, such as practice, it has several limitations that open up avenues for securities (e.g., stock, futures, options, and bonds), gold, future research. First, we only sampled from one Chinese mutual funds, and real estates, have been very unstable and online P2P intermediary. This may have caused sampling highly risky. Many investment varieties, such as savings bias, so future research may need to obtain responses from and bonds, have de facto negative returns because of high multiple intermediaries. Second, lenders’ willingness to inflations. Under these circumstances, familiarity cannot lend is a dynamic behavior and may evolve over time along develop lender’s trust in an intermediary even among with the development of the online P2P lending market. lenders who use online P2P lending very frequently and Longitudinal studies on P2P lending would be interesting. have done so for a long time. Third, present study was conducted in China, which has very particular social, economic, and cultural characteris- 8.3 Managerial implications tics. Future research may perform cross-cultural compari- sons between China and other developed countries to This study provides several valuable insights for practitio- unveil differences in lenders’ behaviors. ners. For borrowers it is important to improve the informa- tion quality of loan requests, such as a convincing loan purpose, project description, verifiable previous loans, and 9 Conclusions payback records. However, involvement of social networks on intermediary platforms may not help borrowers in seeking This study developed an integrated model to examine trust loans, especially on the platforms lacking stringent screening in the online P2P lending context. The model integrates and verifications procedures for their social networks. cognition-based, institution-based, knowledge-based, and For intermediaries there are three implications. First, it personality-based trust beliefs to investigate how trust in an is extremely important for intermediaries to improve their online P2P intermediary and trust in borrowers are culti- service quality and to ensure the safety of funds and the vated and how these two trust beliefs influence lenders’ security and protection of transaction, which can signifi- willingness to lend. The model was tested using data from cantly improve lenders’ trust in intermediaries. Our inter- 785 lenders on PPDai, the first and largest online P2P views revealed that many lenders continue to use online lending platform in China. The results revealed that trust in P2P lending frequently, not because they are satisfied with borrower plays two important roles. It drives lenders’ the P2P lending platforms in China, but because they lack willingness to lend more efficiently than trust in interme- better investment alternatives. When the economic envi- diary and it also carries the significant impact of trust in ronment, such as the stock market and real estate market, intermediary on lenders’ willingness to lend. The infor- becomes more attractive, lenders may switch to other mation quality of borrowers’ loan requests is the most varieties of investment. Therefore, to retain lenders online important factor influencing lenders’ trust in borrowers, P2P lending intermediaries should establish themselves and the intermediary’s service quality and protection are more solidly by improving service quality and providing two essential factors to determine lenders’ trust in an more protection for lenders. Second, intermediaries should intermediary. These findings provide valuable insights for provide functions for borrowers to give high-quality both borrowers and intermediaries. information for their loan requests and encourage and/or require borrowers to do so. For example, intermediaries Acknowledgments We gratefully acknowledge the financial sup- may ask borrowers to provide information of their loan port of National Natural Science Foundation of China (No. 71302008) history and to detail their projects for which they are and National Social Science Foundation of China (No. 11AZD077). seeking loans. Third, intermediaries may need to set more stringent entrance requirements for their social networks. These social networks should reflect members’ credit to a Appendix certain degree. For example, intermediaries may classify their borrowers into several levels, and require members to (See Table 6) 123 252 Inf Technol Manag (2014) 15:239–254

Table 6 The instrument Constructs Measurement items Adapted from Mean SD

Familiarity (FAM) FAM1: How long have you been using PPDai’s peer-to-peer Kim et al. [46] 1.42 0.83 lending services? FAM2: How often do you use PPDai in each month? Kim et al. [46] 2.41 1.53 Service quality(SQ) SQ1: PPDai can guarantee borrowers’ quality Yin [66] 4.52 1.37 SQ2: PPDai can provide reliable services Watson et al. [47] 4.61 1.34 SQ3: PPDai provides good services and supports Watson et al. [47] 4.70 1.35 during my payback process Safety and protection (SP) SP1: PPDai implements sufficient security Watson et al. [47] 4.42 1.33 measures to protect its users SP2: PPDai usually ensures that transactional Kim et al. [46] 4.56 1.33 information is protected from being altered or destroyed during a transmission on the Internet SP3: I feel safe making transactions on PPDai Kim et al. [46] 4.40 1.24 Social capital (SC) SC1: The borrower is active in interacting with others on Kim et al. [46] 4.57 1.28 PPDai SC2: The borrower and I have good interaction and Lin et al. [1] 4.30 1.26 communication SC3: The borrower has a good image and is respected by others Lin et al. [1] 4.71 1.20 Information quality (IQ) IQ1: I think the borrower provides reliable information Pavlou et al. [67] 4.13 1.28 IQ2: The borrower provides sufficient information Kim et al. [46] 4.56 1.25 when I try to make a transaction IQ3: I am satisfied with the information Kim et al. [46] 4.68 1.24 provided by the borrower Trust in intermediary (TI) TP1: PPDai is able to protect the interests of lenders Kim et al. [46] 4.61 1.40 TP2: The systems and policies implemented Pavlou et al. [67] 4.52 1.26 by PPDai protect lenders TP3: PPDai tries its best to satisfy the requests Pavlou et al. [67] 4.56 1.27 and needs of its users Trust in borrower (TB) TB1: The borrower on PPDai is trustworthy Pavlou et al. [67] 4.19 1.33 TB2: The borrower on PPDai gives me the impression Lu et al. [15] 4.60 1.31 that she/he would keep promises TB3: I expect that the intention of the borrower is benevolent Lu et al. [15] 4.64 1.31 Willingness to lend (WL) WL1: It is very likely that I will lend to the borrower Lu et al. [15] 4.36 1.22 WL2: The borrower is reliable, and I will bid Gefen [31] 4.42 1.22 for his/her loan request WL3: The borrower’s listing is worth bidding for Jarvenpaa et al. [68] 4.52 1.19 Perceived benefit (PB) PB1: I can earn a good return if I lend to the borrower Jarvenpaa et al. [68] 4.51 1.45 PB2: The turnover time of my investment is Kim et al. [46] 4.70 1.28 short if I lend to this borrower PB3: It is a good chance to lend to the borrower Kim et al. [46] 4.72 1.39 Disposition to trust (DT) DT1: I feel that people are generally reliable Kim et al. [46] 4.49 1.38 DT2: I feel that people are generally dependable Kim et al. [46] 4.63 1.29 DT3: I feel that people are generally trustworthy Kim et al. [46] 4.61 1.18

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