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

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

Dongyu Chen • Fujun Lai • Zhangxi Lin

Published online: 31 May 2014

� Springer Science+Business Media New York 2014

Abstract Online peer-to-peer (P2P) lending is a new but

essential financing method for small and micro enterprises

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

cultivated. Based on trust theories, the present study devel-

ops an integrated trust model specifically for the online P2P

lending context, to better understand the critical factors that

drive lenders’ trust. The model is empirically tested using

surveyed data from 785 online lenders of PPDai, the first and

largest online P2P platform in China. The results show that

both trust in borrowers and trust in intermediaries are sig-

nificant factors influencing lenders’ lending intention.

However, trust in borrowers is more critical, and not only

directly nurtures lenders’ lending intention more efficiently

than trust in intermediaries, but also carries the impact of

trust in intermediaries on lenders’ lending intention. To

develop lenders’ trust, borrowers should provide high-

quality information for their loan requests and intermediaries

should provide high-quality services and sufficient security

protection. The findings provide valuable insights for both

borrowers and intermediaries.

Keywords Online peer-to-peer (P2P) lending � Trust �China

1 Introduction

The question of financing small and micro enterprises

(SMEs) in an effective and efficient way has attracted

much attention from both academics and practitioners. The

financing problem is especially critical in developing

countries like China. According to a report from the Chi-

nese Government Research Center, approximately 50 % of

SMEs in China face financial constraints. With advances in

information technologies, a new type of financing method,

online peer-to-peer (P2P) lending has, since 2005, become

an important supplement to traditional financing. Online

P2P lending allows people to lend and borrow funds

directly through an online intermediary without the medi-

ation of financial institutes.

P2P lending has experienced rapid growth in recent

years around the world, including the UK., the US, Japan,

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

32,000 loans, totaling over $193 million [2]. As a leading

platform in China, PPDai (www.PPDai.com) has attracted

500,000 members and facilitated about 100 million RMB

in loans in 2011.

Online P2P lending has several unique characteristics

that differ from traditional e-commerce business models.

First, the ‘‘goods’’ exchanged on online P2P platforms are

neither tangible products nor services, but rather the rights

to claim principle and interests in the future. Second,

lenders make lending decisions mainly based on the risks

and benefits of a lending transaction rather than on the

D. Chen � F. Lai

Dongwu Business School, Soochow University,

Suzhou 215000, China

e-mail: [email protected]

F. Lai (&)

College of Business, University of Southern Mississippi,

Long Beach, MS 39560, USA


Z. Lin

The Rawls College of Business Administration, Texas Tech

University, Lubbock, TX 79409, USA


123

Inf Technol Manag (2014) 15:239–254

DOI 10.1007/s10799-014-0187-z

http://www.PPDai.com

quality of the goods, services, logistics, or anything else.

Third, the escrow systems that are used by traditional

e-commerce for product and service exchange are not

readily applied to online P2P lending settings. In traditional

consumer-to-consumer (C2C) e-commerce (e.g. Taobao in

China and eBay in the US), the intermediaries hold the

funds from buyers and transfer them to sellers only after

the buyer confirms they have received the product or ser-

vice. Such an escrow system cannot be applied in online

P2P lending because the funds themselves are the exchange

object. Therefore, the transactional behaviors of online P2P

lending may not be the same as those in traditional

e-commerce business settings. In addition, previous studies

have mainly focused on developed countries, whose results

may not be applicable to Chinese settings. To better

understand the lending behaviors in China’s online P2P

lending platforms, further research on China’s online P2P

lending is warranted.

Online P2P lending is inherently high risk; it is not only

characterized by uncertainty, but also by anonymity, lack

of control, and potential opportunism [3]. On online P2P

lending platforms, lenders and borrowers are not able to

communicate face-to-face and funds trading is conducted

online. There is a high level of information asymmetry

between borrowers and lenders [4], which presents a sig-

nificant barrier to the further development of this market-

place. P2P lending faces a variety of risks either from the

implicit uncertainty of using a sophisticated technological

infrastructure or from the conduct of borrowers involved in

online transaction [3]. Prior studies have also reported that

trust plays a central role in online transactions [5–8].

Therefore, initiating trust between borrowers and lenders is

a critical issue for online P2P lending. Previous studies

have investigated the antecedents of trust from a variety of

perspectives in the e-commerce context, such as online

purchasing (e.g., [8–10]), the adoption of Internet banking

(e.g., [11]), mobile payment (e.g., [12, 13]), and virtual

community development (e.g., [14, 15]). However, few

studies consider this issue in the context of the online P2P

lending marketplace.

The remainder of this paper is organized as follows. We

first briefly present the background of online P2P lending

and then review the related literature, followed by devel-

oping a conceptual model with hypotheses. Subsequently,

we present the research methodology and test the hypoth-

eses. Finally, we discuss the findings and implications and

make a conclusion.

2 Online P2P lending background

There are several commercial lending platforms, such as

Prosper, PPDai, Lending Club, Zopa, and Easycredit (see

Table 1). These platforms employ similar lending proce-

dures. The potential user who intends to borrow or lend

must create an account, providing personal information,

such as name, address, phone numbers, and social security

number. Some online P2P lending platforms (e.g., Prosper)

also require users to provide bank account information. The

information is then verified and a credit number is assigned

accordingly. For members of Prosper, a credit score is

extracted directly from Fair, Isaac Credit Organization

(FICO). However, there is no such agency to provide credit

scores in China, so borrowers’ credit scores are calculated

based on the information they provide, such as ID number,

bank account, income, age, and occupation.

Borrowers deemed creditworthy are invited to create their

borrowing listings. The listings are essentially loan requests

that specify the amount they seek, the maximum interest rate

they will pay, and other optional information, such as free-

format descriptions of loan purpose. Lenders make lending

decisions according to the listing information and the bor-

rower’s personal information. On most P2P lending platforms,

such as Prosper in the US and PPDai in China, a lender

chooses to finance only a portion of a loan, rather than the

entire loan. For instance, a lender can bid a minimum amount

of $50 on Prosper. Borrowers can choose either a closed or

open auction format. In the closed format, the auction closes as

soon as the total amount requested is reached. The loan’s

interest rate is that specified by the borrower in the listing. In

the open format, the auction is open for a pre-assigned period.

Even if the entire amount requested is funded, lenders can

continue to bid down the interest rate.

Once the bidding process ends, the listing is closed and

submitted to the lending intermediary for further review

[1]. Borrowers may be asked to provide additional docu-

mentation and information. If the lending is approved,

funds are directly transferred from the winning bidder’s

account to the borrower’s account. In general, service fees

are charged to both borrowers and lenders by the inter-

mediary. The borrower’s payback is also directly trans-

ferred from the borrower’s account to the lender’s account.

If the payback is overdue beyond a pre-determined limit,

such as 2 months on Prosper, the borrower’s default will be

recorded and submitted to credit bureaus and then debt

collection is initiated.

Although P2P lending has been growing rapidly in

China, it is still in the initial stages of development. The

first online P2P lending platform, PPDai (ppdai.com), was

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

developed countries. The most important problem is the

lack of a legal basis in the supervision of online P2P

lending intermediaries and the lack of safety guarantees for

lenders [16].

240 Inf Technol Manag (2014) 15:239–254

123

3 Related studies on P2P lending

Several studies have been conducted on the behaviors of

online P2P. Based on open data from Prosper, researchers

have found that information from borrowers and loan

requests are critical to lenders’ decisions. For instance, Lin

[17] revealed that the lower the credit level of a borrower,

the less likely his/her loan listing will be funded. Collier

and Hampshire [18] discovered that information of both

loan amount and debt/income ratio of a borrower influence

the final interest rate of a loan. Some scholars have also

found that the social relationship information of a borrower

influences loan success, interest rate, and default proba-

bility. For example, Lin et al. [4] found that the relational

aspect of social capital is a reliable signal that indicates a

borrower’s trustworthiness. Greiner and Wang [19] pointed

out that social capital plays a more important role for

borrowers with lower credit levels.

Although P2P lending has been attracting increasing

interests from practitioners in China, research on it is still

scarce, theoretical studies in particular. Among them, for

example, Chen et al. [20] explored the critical antecedents

of lenders’ trust in borrowers in China and found that

structural social capital, relational social capital, and dis-

position to trust are important in initiating trust in the

lending process. Xu et al. [21] made a comparison of the

online lending marketplace between China and other

countries and found that cultural factors may influence

online lending business models as well as lenders’

behaviors.

4 Theoretical background and conceptual model

High risk is inherent in P2P lending, in particular for

lenders. It is vital for lenders to identify credible borrowers

and choose the right lending intermediary. On this basis,

for P2P lending to succeed, trust must be established at the

very beginning [10]. Therefore, it is critical to investigate

the key factors in lenders’ trust-building processes.

4.1 Conceptual model

Trust is a complex behavior, which has been defined from

several different perspectives in a variety of disciplines.

For instance, in psychology, trust is defined as an expec-

tation that ‘‘an exchange partner will not engage in

opportunistic behavior, despite short-term incentives and

uncertainties about long-term rewards’’ [22]. In sociology,

it is defined as ‘‘a particular level of the subjective prob-

ability with which an agent assesses that another agent or

group of agents will perform a particular action, both

before such action can be monitored and in a context in

which it affects his own action’’ [23]. In management

areas, trust is defined as the willingness of a party to be

vulnerable to the actions of another party based on the

expectation that the other will perform a particular action

important to the trustor, irrespective of their ability to

monitor or control the other party [24].

When there is uncertainty as to how others will behave,

trust is a prime determinant of what people expect from the

situation and how they behave [10]. Therefore, trust is a

Table 1 Online P2P lending

intermediariesRegion Intermediary Start

year

Region Intermediary Start

year

US Prosper 2006 China Yixin 2006

Zopa, LendingClub, VirginMoneyus,

Loanio, Mircroplace, Fynanz

2007 PPDai, Qifang,

Wokai

2007

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

Inf Technol Manag (2014) 15:239–254 241

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central aspect in many economic transactions, including

e-commerce. In the online P2P context, trust is critical in

fulfilling lending transactions because of the high risk of

borrowers engaging in opportunistic behaviors. Although

there are no studies on trust building in the online P2P lending

context, there are a number of studies on trust building in other

related contexts, such as e-commerce e.g., [25–27].

In the literature, trust has been examined through the

framework of ‘‘antecedents–trust–outcomes’’ [28]. In this

framework, trust is conceptualized as specific trust beliefs

and general trust beliefs [24, 29]. Specific trust beliefs deal

primarily with the characteristics of trustees, while general

trust beliefs deal primarily with the overall impressions of

trustees [10]. Specific trust beliefs are framed as anteced-

ents to general trust beliefs [24, 30] and general trust

beliefs lead to behavioral intention [31]. Gefen et al. [10]

thought that the distinction between specific and general

trust beliefs was applicable in the context of online trans-

actions. Therefore, we frame our conceptual model with

specific trust beliefs as antecedents of general trust beliefs

and behavioral intention as the outcome of general trust

beliefs, as depicted in Fig. 1.

The model is contextualized to the online P2P context,

where specific trust beliefs are delineated as knowledge-

based, institution-based, and cognition-based, while gen-

eral trust beliefs are described as trust in intermediary and

trust in borrower. Various variables are contextualized for

the specific trust beliefs in the online P2P context. For

example, familiarity is a variable for a knowledge-based

specific trust belief, service quality and safety as institu-

tional-based and social capital and information quality as

cognition-based specific trust beliefs. These specific and

general trust beliefs are further deliberated as follows.

4.2 Specific trust beliefs

In the context of e-commerce, Gefen et al. [10] identified

specific trust beliefs as cognition-based, institution-based,

knowledge-based, calculative-based, and personality-

based. The first four types of trust antecedents are mainly

relevant either to the characteristics of trustees or to the

relationships between trustees and trustors, while person-

ality-based trust relates to the personalities of trustors and

is irrelevant to trustees [29, 31].

Conative

Personality - based

Cognition - based

Institution -based

Knowledge - based

Willingness to Lend

Familiarity

Service Quality

Security Protection

Social Capital

Information Quality

Trust in Borrower

Trust in Intermediary

Disposition to Trust

Perceived Benefit

H7

H8

Specific Trust Beliefs General Trust Beliefs Outcome Trust Belief

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


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Cognition-based trust refers to the rational assessment

of characteristics demonstrated by trustees. Individuals

assess trustee’s trustworthiness based on first impressions

through second-hand information [32], and tend to place

more trust in people similar to themselves [10]. This kind

of trusting belief is formed via categorization and illusions

of control [10]. Overall, cognition-based trust is utilized to

gain some sense of control in an uncertain situation when a

trustor has no prior first-hand experience with the trustee

[29].

Institution-based trust involves third parties (i.e., lend-

ing intermediaries), and refers to the trust based on guar-

antees and commendations from third parties [33]. Such

institution-based trust is ‘‘especially suited for online

marketplaces where buyers predominantly transact with

new and unknown sellers under the aegis of third parties

who provide an institutional context’’ [34, p. 38].

Knowledge-based trust antecedents suggest that trust

develops as a result of the aggregation of trust-related

knowledge by the parties involved [35]. Once a trustor

obtains sufficient knowledge and information of the trustee,

he is more likely to engage in trustworthiness assessment

based on the knowledge and information obtained [36].

This is because knowledge-based trust beliefs, such as

familiarity, allow individuals to better predict the behaviors

of trusted parties, and hence to reduce the possibility that

they may mistakenly feel that they are being unfairly taken

advantage of [31].

Calculative-based trust is derived from an economic

analysis, interpreting trust as ‘‘it is not worthwhile for the

other party to engage in opportunistic behaviors’’ and ‘‘if

the costs of being caught outweigh the benefits of cheating,

then trust is warranted since cheating is not in the best

interest of the other party’’ [10, p. 64]. Such trust is built if

an individual believes that the trusted party has nothing to

gain from being untrustworthy. Calculation-based trust is

not included in the model, because it is not appropriate for

China’s P2P context. There is no national credit system and

law enforcement is weak in China. Defaulted borrowers

may lose very little, if anything. Therefore, borrowers on

China’s P2P lending platforms do indeed have reason to

engage in opportunistic behaviors. On this basis, we

believe that lenders have no, or very low if any, calcula-

tion-based trust in borrowers on China’s P2P platforms,

and so this form of trust is excluded from the model.

Personality-based trust refers to the tendency to believe

or not in others and so to trust them [10, 24, 29]. A person

with a greater disposition to trust may tend to trust others.

Such trust belief is credit given to others before experience

can provide a more rational interpretation [10]. It is related

to an individual’s personality and is especially important in

the initial stages of a relationship [29]. Although lenders’

personalities may influence their trust in borrowers and in

online P2P intermediary, it can be cultivated neither by

borrowers nor by intermediaries. Thus, personality-based

trust is included as a control in the model.

Table 2 summarizes ten widely cited articles, which

examined specific trust beliefs as antecedents of general

trust beliefs in e-commerce contexts. The studies listed in

this table are selected from the leading IS journals,

including Information System Research, MIS Quarterly,

Journal of Management Information Systems, Omega,

Information and Management, International Journal of

Electronic Commerce, and The Journal of Strategic

Information Systems.

4.3 General trust beliefs

4.3.1 Trust in borrower

Trust in borrower is conceptualized in this study as a belief

that the borrower will act cooperatively to fulfill the len-

der’s expectations without exploiting his or her vulnera-

bilities [6]. Trust in borrower is of vital importance for

lending success. Although P2P lenders are able to select

loan requests from multiple potential borrowers, they are

often not familiar with these borrowers and repetitive

transactions between lenders and borrowers are unlikely

[42]. Therefore, the lender’s trust in the borrower is ex ante

in nature. Due to the lack of repetitive transactions, ex-ante

trust is primarily cognition-based. Such cognition-based

trust relies on rapid, cognitive cues of first impressions

[43], rather than experiential personal interactions [44].

Due to the fact that lenders’ trust in borrowers is based

on the former’s first impression of the latter, lenders often

act on information that is incomplete and far from perfect

[10]. They are thus often exposed to a high level of

uncertainty and risk in their lending decisions, especially

since the transactions are monetary in nature. Therefore,

lenders would seek to assess borrowers on a full spectrum.

There are two ways for lenders to assess borrowers. The

first is direct assessment of the information quality of loan

requests, such as reliability and the sufficiency of the

request information. The information provided in the bor-

rower’s requests may directly reflect whether he is honest

and behave professionally. The second is indirect assess-

ment. Although there are no repetitive transactions between

a particular lender and borrower, the borrower might have

already made multiple requests on the platform and inter-

acted with other lenders. These previous requests and

interactions with other lenders are the borrower’s social

capital, which may serve as a proxy for reliability and

honesty. On this basis, we include both direct assessment

(i.e. information quality) and indirect assessment (i.e.

social capital) as cognition-based trust beliefs.


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4.3.2 Trust in intermediary

Like C2C e-commerce, online P2P lending involves not

only a buyer (i.e., the borrower) and supplier (the lender)

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-

ing transactions among potential borrowers and lenders in

an online marketplace by collecting, processing, and dis-

seminating information [34, 45]. Lenders must put their

trust not only in borrowers but also in intermediaries. Trust

in lending intermediary is thus defined as the subjective

belief with which a lender believes that the intermediary

will institute and enforce fair rules, procedures, and out-

comes in its marketplace competently, reliably, and with

integrity, and, if necessary, will provide recourse for

lenders to deal with borrowers’ opportunistic behaviors

[34].

Similar to lender’s trust in borrowers, lender’s trust in an

intermediary is also assessed from two sources, direct and

indirect. The direct assessment is based on whether the

intermediary is safe for the transaction and whether it

provides high-quality services. Since P2P lending transac-

tions are monetary in nature and the lenders bear much

higher risk than borrowers, lenders have great need for the

intermediary to safeguard their funds. The lender’s trust in

the intermediary is in general based on transaction safety

the intermediary can provide, such as escrow services,

fraud protection, authentication, and verification. Other

than the core features (e.g., safety and protection), lenders

also expect the intermediary to provide high-quality ser-

vices to facilitate the transactions, such as a web site that

runs 24/7.

In contrast to lenders’ trust in borrowers, which is ex

ante in nature and generally based on first impressions, it is

more likely that there have been repetitive interactions

between the lender and the intermediary. Previous experi-

ence may serve as an indirect assessment of the interme-

diary. For example, lenders who have used an intermediary

very often and for a long time may have greater trust in it.

5 Research hypotheses

5.1 Antecedents of trust in intermediary

For lenders’ trust in intermediaries, lenders assess the

intermediary directly based on safety and service quality

and indirectly according to their previous experience with

the intermediary. Therefore, two trust antecedents are

incorporated into trust in intermediary—institution-based

trust and knowledge-based trust. The safety protection and

service quality of the intermediary serve as institution-

based trust and the lender’s familiarity with intermediary

serves as knowledge-based trust.

Familiarity refers to lenders’ familiarity with a lending

intermediary through interaction. When lenders acquaint

themselves with an intermediary, they become more

familiar with the intermediary’s behavior patterns, and so

they can fairly predict the intermediary’s behaviors based

on the information they obtained from previous interac-

tions [7, 46]. This predictability may result in trust in an

intermediary, because ‘‘familiarity leads to an under-

standing of an entity’s current actions while trust deals with

beliefs about an entity’s future actions’’ [46, p. 551].

Lenders who have had pleasant experiences with an

intermediary would stick with that intermediary and

become more familiar with it. This stickiness reflects a

lender’s trust in an intermediary. The lenders who have had

bad experiences with an intermediary would trust it less be

less familiar with it, and leave it. Prior literature has

examined familiarity in the e-commerce context and

Table 2 Specific trust beliefs Study Specific trust beliefs

Cognition

-based

Institution

-based

Knowledge

-based

Calculative

-based

Personality

-based

Others

McKnight et al. [7] 4 4

Gefen and Straub [37] 4 4 4 4

Gefen et al. [10] 4 4 4 4 4 4

Koufaris and

Hampton-Sosa [38]

4 4 4

Pavlou [8] 4 4

Pavlou and Gefen [34] 4 4

Pavlou [39] 4 4

Teo et al. [40] 4 4 4

McKnight et al. [7] 4

Pavlou and Fygenson [41] 4 4 4 4


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revealed that familiarity positively relates to trust in

e-commerce websites [e.g., 15]. Therefore, we propose:

H1: A lender’s familiarity with a lending intermediary

positively affects the lender’s trust in the intermediary.

Service quality refers to the quality of functions and

supportive activities provided by the intermediary to make

the P2P lending experience more smooth and pleasant.

There are two categories of service quality: experiential

(such as responsiveness and reliability) and structural (such

as flexibility and assurance) [47]. Experiential service

quality refers to providing a prompt response to lenders’

requests and comments, as well as providing uninterrupted

support 24/7, which provides lenders a more pleasant

lending experience. Structural service quality refers to

providing more flexible (e.g., more fund transferring

methods, such as by mobile phones, online banking, and

ATM deposit) and safe (e.g., transaction encryption, bor-

rower authentication, and escrow) services, which meant

that lending can be conducted efficiently and effectively.

Numerous studies have shown that both of these categories

are critical to creating customer value and developing

customer satisfaction e.g., [48, 49]. Lenders get more value

from high-quality services and are more satisfied with their

experience, and expect the same pleasant experience in

future, thus place more trust in an intermediary. More

importantly, high-quality services inspire a lender’s confi-

dence in an intermediary’s reliability, capability, and

integrity [50]. Therefore, we propose that an intermediary

that consistently provides high service quality to lenders

will cultivate more trusting relationships with lenders. That

is:

H2: The service quality of a lending intermediary pos-

itively affects the lender’s trust in the intermediary.

Safety and protection refers to lenders’ perceptions that

a lending intermediary will fulfill security requirements,

such as authentication, integrity, encryption, and non-

repudiation [10, 49, 51]. The exchange object of online P2P

lending is monetary fund, so it shares the same inherent

risks as other financial activities. The safety and protection

provided by an intermediary reflect the intermediary’s

effort to reduce lenders’ risk. Only when lenders perceive

the intermediary to provide sufficient protection do they

perceive their funds to be safe, and thus trust the inter-

mediary. Prior studies revealed that safety and protection

are important antecedents of trust for activities involving

high risks, such as adoption of mobile payments [46] and

online purchases [13]. Therefore, we posit that:

H3: The safety and protection provided by a lending

intermediary positively affect a lender’s trust in the

intermediary.

5.2 Antecedents of trust in borrower

For lenders’ trust in borrowers, lenders directly and indi-

rectly assess borrowers based on their first impression. The

direct assessment is based on the information quality of the

lending requests. The indirect assessment is based on the

borrower’s social capital information.

Social capital refers to a borrower’s resources, which

can be accessed through social networks in the lending

intermediary [46]. The majority of lending websites offer

social networking services such as communities and bul-

letin boards. Such social capital information can be easily

accessed by other users. Borrowers can communicate with

lenders and other borrowers and seek lending opportunities

through social networks. The social network members with

a good reputation are more respected by others and their

online behaviors are more creditable. Thus, borrowers in

general aim to build up their social networks to accumulate

social capital. Borrowers with more social capitals are

deemed more trustworthy. Borrowers’ opportunistic

behaviors may drain their social capital and lead to sanc-

tions from other social network members. Therefore, social

capital may serve as an important signal of borrowers’

trustworthiness. This signal can play a vital role in a

marketplace, because borrowers’ social capital is difficult

to develop, but readily accessible for lenders [1, 18]. On

this basis, we propose:

H4: A borrower’s social capital positively affects a len-

der’s trust in the borrower.

Information quality refers to a lender’s perception of the

accuracy and completeness of the information provided by

a borrower in his borrowing listing. Due to the lack of a

national credit system in China, the listing description is

the first and most significant means for lenders to assess

borrowers. Prior studies on P2P lending revealed that the

listing information has a significant impact on lending

outcomes such as loan success and interest rate [8, 52–54].

Such an impact is especially prominent in regions with less

mature legal systems such as China. In such regions,

lenders are less likely to be capable of claiming their rights

through legal actions when facing loan default and fraud.

Therefore, lenders must place more importance on infor-

mation provided by the lending intermediary and borrow-

ers to evaluate borrowers’ trustworthiness. The majority of

lending platforms provide an attachment uploading func-

tion so borrowers can provide materials they consider

beneficial for their creditability.

The information quality of loan listings serves two

purposes. First, it facilitates the lender’s assessment of the

fundability of a request. The information for this purpose

includes loan amount, duration, interest rate, etc. The

information on the loan purpose is also important. If it is


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convincing and verifiable, the request is more trustworthy.

For example, for a funds request proposed to improve the

borrower’s eBay store, the address of the borrower’s front

store on eBay, if provided, will greatly facilitate the len-

der’s evaluation of the request and build lender’s trust in

the borrower.

Second, information quality serves as a proxy to assess

the borrower’s creditability. The high quality of the listing

information reflects how serious, sincere, and professional

the borrower is, which influences the lender’s confidence in

the borrower. A high-quality request indirectly reflects the

borrower’s capability to understand and execute the pro-

posed plan using the loan, such that he is more trustworthy.

Thus, we propose:

H5: The information quality of a borrower’s loan request

positively affects lender’s trust in the borrower.

5.3 Trust in intermediary and trust in borrower

A lender’s trust in an intermediary comprises two aspects:

(1) the intermediary’s technical protection, and (2) the

good standing of its borrower base and rigorous transaction

regulations. These two aspects also lead lenders to trust

borrowers, because technical protection prevents borrower

frauds and a borrower base with good standing and rigor-

ous regulations lower the probability of borrower defaults.

Due to the high risks lenders bear, the safety and pro-

tection of lenders are the intermediary’s first priorities. To

alleviate a lender’s risk, the intermediary needs not only to

utilize high technologies such as encryption and authenti-

cation to protect the lender’s funds, but also to screen

potential borrowers and rigorously monitor loan transac-

tions. In addition, the intermediary also institutes regula-

tions that restrict borrowers’ potential to engage in

opportunistic behavior and provides guidelines of what

constitutes acceptable transaction behavior [34]. The

membership registration screening and transaction regula-

tions reduce the probability of borrower defaults. The low

probability of borrower fraud and default help lenders trust

borrowers. Therefore, when lenders trust an intermediary,

they perceive the association between borrowers and the

intermediary and their trust in the intermediary is cascaded

from intermediary to borrowers. This is called trust trans-

ference [55]. Therefore, we propose that a lender’s trust in

an intermediary may lead to the lender’s trust in a borrower

whose behaviors are regulated and restricted by the

intermediary:

H6: The lender’s trust in an intermediary positively

affects the lender’s trust in a borrower.

Extensive studies revealed that trust beliefs are also

affected by an individual’s personality [10, 56]. To rule out

the spurious relationship between trust in intermediary and

trust in borrower, the lender’s disposition to trust is

incorporated as a control for both.

5.4 Outcomes of general trust beliefs

As discussed above, lenders’ risk is from both intermediary

and borrowers, so they assess their willingness to lend in

relation to both the intermediary and borrower involved.

Trust in intermediaries and borrowers can help lenders

‘‘subjectively rule out many undesirable possible behaviors

on the part of the party they trust’’ [34, p. 45]. Once trust

overcomes social uncertainty, a more positive attitude

towards lending will be created, which in turn leads to

lending intention. Prior studies also indicated that purchase

intention is not only influenced by a customer’s trust in the

vendor, but also by their trust in intermediaries (e.g., [34,

57]). Such findings have also been validated in the context

of virtual communities [58]. Therefore, we propose that a

lender’s willingness to lend is influenced by both trust in

the intermediary and trust in the borrower:

H7: The lender’s trust in an intermediary positively

affects the lender’s willingness to lend.

H8: The lender’s trust in a borrower positively affects the

lender’s willingness to lend.

In addition, perceived benefit may also be a critical

determinant of willingness to lend. As this paper mainly

aims to develop a trust model for lenders in P2P lending,

perceived benefit is used as a control for willingness to

lend.

6 Methodology

To ensure the content validity of the measures, we adapted

them from previous studies and pilot tested them prior to

the formal data collection. The finalized instrument com-

prises two parts, as presented in ‘‘Appendix’’. The first part

collects respondents’ demographic information, such as

gender, age, education, income and their information on

the intermediary. The second part is for main constructs,

including trust, familiarity, service quality, security pro-

tection, social capital, information quality, and willingness

to lend. Familiarity was measured as the monthly fre-

quency and the number of years of using the intermediary.

The other constructs were anchored with a 7-point Likert

scales, ranging from 1 (disagree strongly) to 7 (agree

strongly).

To conduct this study, we first obtained the permission

and collaboration of a leading P2P intermediary in China,

PPDai (www.PPDai.com). PPDai sent a message


123

http://www.PPDai.com

explaining the research purpose to 1,500 of its lenders, who

were randomly selected from its lender database. The

lenders were asked to fill in our online questionnaire. To

encourage their participation, we offered a nominal gift of

a 50 RMB coupon from PPDai and entry into a draw to win

an Apple iPod touch or iPod shuffle. To reduce the possi-

bility of multiple responses from the same lender, partici-

pants were required to provide their mobile phone

numbers. Repeat responses from the same mobile phone

numbers were filtered out.

A total of 938 responses were collected. After a careful

comparison of the data (e.g., personal ID, and borrower’s

ID on PPDai) collected from the questionnaires with those

of the PPDai database, invalid responses were screened out

and a total of 785 valid responses were obtained for use in

the analysis.

We compared the demographic variables of the early

(the first month) and late responses (later months) to assess

response bias. In addition, we also compared the respon-

dents’ profile with the profile of PPDai’s lender population

and no significant difference was found, indicating no

severe non-response bias. The respondent profile demo-

graphics are summarized in Table 3.

7 Data analysis

Structural equation modeling with LISREL 8.70 was

applied to analyze the data. The model was estimated by

maximum likelihood (ML). The two-step procedure [59]

was followed. First, the measurement model was examined

to assess construct reliability and validity. Then, the

structural model was tested to evaluate the causal rela-

tionships among the theoretical constructs. We had 32

items in this model and 785 responses, an adequate sample

size for our model according to the ‘‘ten times’’ rule of

thumb, which requires the sample size to be at least ten

times the number of items in the model [60].

7.1 Measurement model

The model fits the data well, with v2 = 1140.93 and

df = 288. The goodness-of-fit indexes are CFI = 0.98,

NFI = 0.97, and NNFI = 0.98, greater than the limit of

0.95. The RMSEA is 0.065, lower than 0.10, the suggested

cut-off value for complex models.

Reliability, convergent validity, and discriminant

validity of the multi-item scales were assessed by follow-

ing the guidelines of Fornell and Larcker [61] and Gefen

and Straub [62]. Except for perceived benefit (0.68), the

values of Cronbach’s alpha are [0.7. All composite reli-

ability values are[0.8 (see Table 4), suggesting acceptable

reliability.

Convergent validity is assessed in terms of factor load-

ings and average variance extracted (AVE). As shown in

Table 4, all 32 items have loadings greater than 0.7 and are

significant at the p \ 0.01 level, suggesting convergent

validity at the item level. All AVE values are[0.5, the cut-

off value, suggesting acceptable convergent validity at the

construct level [62].

Discriminant validity was assessed by (1) examining

whether the squared root of each construct’s AVE was

larger than any inter-correlation between this focal con-

struct and all other constructs; and (2) examining whether

each item loading was substantially higher on its principal

construct than on other constructs [61]. The results show

that the cross-loading differences are higher than the sug-

gested threshold of 0.1 [62], and the square root of each

AVE is larger than the inter-correlations of the construct

with the others (See Table 5). These results suggest ade-

quate discriminant validity.

Table 3 Demographic information of respondents

Frequency Percentage

Gender

Male 672 86

Female 113 14

Age

Below 20 55 7

21–30 484 62

31–40 203 26

Above 40 43 5

Education

High school or below 279 36

College 465 59

Graduates 41 5

Income

Below 2,000 RMB 192 24

2,000–3,000 RMB 175 22

3,001–5,000 RMB 207 26

5,001–8,000 RMB 112 14

8,001–15,000 RMB 70 9

Above 15,000 RMB 29 4

Years of lending intermediary use

\1 year 581 74

1–2 years 122 16

2–3 years 36 5

More than 3 years 46 6

Frequency of lending intermediary use(per month)

\1 times 289 37

1–3 times 252 32

4–6 times 51 6

7–9 times 20 3

More than 9 times 173 22


123

Multicollinearity was also examined by assessing the

index of variance inflation factor (VIF) [62]. The VIFs for

the constructs range from 1.14 to 2.82, less than the con-

servative threshold of 3.3 [63], suggesting that multicol-

linearity is at an acceptable level.

In addition, common method variance (CMV) was

assessed. First, we conducted Harmon’s single factor test

by following the analytical procedure suggested by Pod-

sakoff et al. [64]. CMV is present in data if one factor

accounts for most of the covariance. The result of the factor

analysis showed that the first factor only accounts for

36.1 % of the total variance. Second, the correlation matrix

reveals that the highest correlation is \0.90, indicating no

severe CMV in the data [65].

Table 4 Descriptive statistics

and measurement modelConstruct Item Loading T-value Composite

reliability

Cronbach’s

alpha

Average variance

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

constructs

The diagonal elements (in bold)

represent the squared roots of

the AVE

DT IQ PB SC SP SQ TB TI WL VIF

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

Trust in intermediary (TI) 0.57 0.60 0.57 0.63 0.59 0.66 0.62 0.86 2.42

Willingness to lend (WL) 0.58 0.60 0.59 0.56 0.42 0.49 0.65 0.59 0.85 2.67


123

7.2 Structural model

The structural model was also analyzed using SEM. The

results are shown in Fig. 2. The model has v2 = 1195.74

and df = 299. The goodness-of-fit indexes are CFI = 0.98,

NFI = 0.97, NNFI = 0.98, and RMSEA = 0.07, suggest-

ing an acceptable fit. The model explains 78, 68 and 69 %

of the variances of trust in intermediary, trust in borrower,

and lender’s willingness to lend, respectively.

As shown in Fig. 2, among three antecedents of trust in

intermediary, the influence of familiarity is not significant

(b = 0.02), while service quality (b = 0.49, p \ 0.05) and

safety and protection (b = 0.30, p \ 0.05) significantly

influence trust in intermediary, suggesting support for H2 and

H3, but not for H1. Similarly, for trust in borrower, social

capital has no significant influence (b = -0.07), while

information quality has a large magnitude (b = 0.66,

p \ 0.05), suggesting support for H5 and not for H4. The

borrower’s disposition to trust has a significant impact on trust

in borrower (b = 0.22, p \ 0.05) but its influence on trust in

intermediary (b = 0.08) is not significant. After controlling

borrower’s disposition to trust, trust in intermediary has a

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

borrower significantly influences lender’s willingness to lend

(b = 0.31, p \ 0.05), suggesting support for H8.

Although the direct influence of trust in intermediary on

lender’s willingness to lend is not significant (b = 0.06), it

may exert influence through trust in borrower, because it

has significant influence on trust in borrower (b = 0.21,

p \ 0.05) while trust in borrower has significant influence

on willingness to lend (b = 0.31, p \ 0.05). Therefore, a

mediation analysis was conducted to test whether trust in

borrower carries the influence of trust in intermediary on

the lender’s willingness to lend. The indirect effect is

0.21 9 0.31 = 0.07 with t = 2.98. It appears that although

the direct effect of trust in intermediary on lender’s will-

ingness to lend is not significant (b = 0.06), the indirect

effect through trust in borrower is significant (b = 0.07,

p \ 0.05). The total effect of trust in intermediary on

lender’s willingness to lend is 0.06 ? 0.07 = 0.13, which

is significant (p \ 0.05). These analyses indicate that the

overall influence of trust in intermediary on willingness to

lend is significant, while this influence is primarily present

in an indirect form through its nourishing of trust in bor-

rower, suggesting support for H7. In addition, the influence

of trust in borrower on willingness to lend (b = 0.31) is

significantly greater than the influence of trust in interme-

diary (total effect = 0.13), indicating that trust in borrower

plays a more critical role in influencing lender’s lending

willingness.

8 Discussion

8.1 Major research findings

This study proposed an integrated trust model to examine

lenders’ trust in China’s online P2P lending context.

Willingness to Lend

Familiarity

Service Quality

Safety

Social Capital

Information Quality

Trust in Borrower

Trust in Intermediary

H6: 0.21** Disposition to Trust

Perceived Benefit

R2=0.78

R2=0.68

R2=0.69

0.53**

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


123

Lenders’ trust was examined from both intermediary and

borrower perspectives. Both antecedents and outcomes of

lenders’ trust were incorporated into our model. For both

trust in borrower and intermediary, direct and indirect

antecedents were included. The model was tested using

responses from 785 lenders on China’s first and largest

online P2P lending platform, PPDai. The major findings are

summarized below and the implications for research and

practices follow.

1. In China’s online P2P context, trust in borrower plays

a central and essential role in influencing a lender’s

willingness to lend. First, trust in borrower is more

effective than trust in intermediaries in increasing a

lender’s willingness to lend. Second, trust in borrower

also significantly carries the influence of trust in

intermediary on a lender’s willingness to lend.

2. It is more effective for borrowers to gain a lender’s trust

by providing high-quality information concerning their

loan requests than by building up social capital when

there is a lack of stringent screening procedures for

social network membership in the lending intermediary.

3. Service quality and safety protection help develop trust

in an intermediary, but such trust cannot be cultivated

from familiarity with the lending intermediary.

8.2 Research implications

This study contributes to the online P2P literature in sev-

eral ways. First, it proposes an integrated and compre-

hensive trust model developed specifically for the online

P2P context. Although the literature suggests knowledge-

based, personality-based, institution-based, cognition-

based, and calculation-based specific trust beliefs as the

antecedents of general trust beliefs [c.f. 10], certain specific

type of trust beliefs are not appropriate for the online P2P

lending context. For example, due to the high risks lenders

bear and the little or no risk borrowers bear, calculation-

based trust appears inappropriate for the online P2P lend-

ing contexts. In addition, both trust in borrower at the

individual level and trust in intermediary at the firm level

were simultaneously incorporated into our model. Previous

studies examined the inter-personal trust or individual-to-

firm trust separately. Furthermore, we developed our trust

model by further materializing specific trust beliefs. We

incorporated familiarity as knowledge-based trust, and

included service quality and safety and protection as

institution-based trust, and social capital and information

quality as cognition-based trust belief, which further

materializes and operationalizes the concepts of specific

trust beliefs. More importantly, the incorporation of

materializing variables (e.g., service quality, information

quality, and familiarity) comprehensively delineates

driving factors for general trust beliefs from both direct and

indirect aspects. For trust in borrower, information quality

was included as the lender’s direct assessment and social

capital as indirect assessment. For trust in intermediary,

service quality, and safety and protection represent the

lender’s direct assessment while familiarity is the indirect

assessment.

Second, our study contributes to literature by examining

China’s online P2P lending. Although other e-commerce

platforms have been examined extensively, online P2P lend-

ing platforms are still under researched. The monetary nature

and inherent high risk of online P2P also warrant further

research. In addition, China’s unique social, legal, institu-

tional, and cultural environment poses challenges to online

P2P lending. For example, law enforcement and contract spirit

in China are weaker than in developed countries. Without

other protection mechanisms, defaults in China will inevitably

be high. Therefore, the results from previous studies con-

ducted on other e-commerce platforms and in developed

countries may not be applicable to China’s online P2P context.

In fact, the findings of the present study are quite different

from previous studies, which are discussed as follows.

Third, in an environment with less mature legal systems

such as China, we found that trust in borrower is more

critical than trust in intermediary in determining online

lending intentions. Such a finding runs counter to previous

studies conducted in developed countries, which reported

that trust in intermediary plays a more critical role than

trust in borrower [e.g., 34]. In addition, our study found

that trust in borrower plays two roles—it not only directly

improves lender’s willingness to lend, but also carries the

effect of trust in intermediary to influence the lender’s

willingness to lend.

Fourth, our findings revealed that the impact of social

capital on trust might be subject to the lending environment.

Our results indicate that in China’s online P2P context, the

borrower’s social capital does not effectively influence trust

in borrower. In contrast, in the context of Proper.com, an

online P2P platform in the US, social capital influences

willingness to lend to a great extent [42]. One possible

reason for this discrepancy is the lending platform’s insti-

tutional arrangement. In the context of China’s PPDai, the

requirements for group membership are loose. All registered

users on PPDai can add anyone as a friend and create a new

group at any time. Such loose requirements may have ero-

ded the value of social capital, because such social capital is

insufficient for lenders to distinguish trustworthy borrowers

from untrustworthy ones. In contrast, on Prosper.com group

membership follows a stringent screening and verification

procedure, which ensures members with high social capitals

are more trustworthy.

Finally, our results revealed that knowledge-based trust

beliefs are not significant for developing general trust in


123

intermediary. This finding is not consistent with previous

studies either. We conducted in-depth interviews with

several lenders, which revealed that they were not very

satisfied with the services provided by any online P2P

platform in China, but they had no better investment

alternatives and thus had to reluctantly stick with P2P

lending platforms. It is especially true that in China many

investors are unable to find reliable investment products. In

recent years, almost all investment varieties, such as

securities (e.g., stock, futures, options, and bonds), gold,

mutual funds, and real estates, have been very unstable and

highly risky. Many investment varieties, such as savings

and bonds, have de facto negative returns because of high

inflations. Under these circumstances, familiarity cannot

develop lender’s trust in an intermediary even among

lenders who use online P2P lending very frequently and

have done so for a long time.

8.3 Managerial implications

This study provides several valuable insights for practitio-

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

payback records. However, involvement of social networks

on intermediary platforms may not help borrowers in seeking

loans, especially on the platforms lacking stringent screening

and verifications procedures for their social networks.

For intermediaries there are three implications. First, it

is extremely important for intermediaries to improve their

service quality and to ensure the safety of funds and the

security and protection of transaction, which can signifi-

cantly improve lenders’ trust in intermediaries. Our inter-

views revealed that many lenders continue to use online

P2P lending frequently, not because they are satisfied with

the P2P lending platforms in China, but because they lack

better investment alternatives. When the economic envi-

ronment, such as the stock market and real estate market,

becomes more attractive, lenders may switch to other

varieties of investment. Therefore, to retain lenders online

P2P lending intermediaries should establish themselves

more solidly by improving service quality and providing

more protection for lenders. Second, intermediaries should

provide functions for borrowers to give high-quality

information for their loan requests and encourage and/or

require borrowers to do so. For example, intermediaries

may ask borrowers to provide information of their loan

history and to detail their projects for which they are

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

certain degree. For example, intermediaries may classify

their borrowers into several levels, and require members to

prove that they have been successfully funded multiple

times and paid back their loans on time to join the elite

level. The social capital of those members may improve

lenders’ trust in borrowers and willingness to lend.

8.4 Limitations and directions for future research

While this study contributes to both the literature and

practice, it has several limitations that open up avenues for

future research. First, we only sampled from one Chinese

online P2P intermediary. This may have caused sampling

bias, so future research may need to obtain responses from

multiple intermediaries. Second, lenders’ willingness to

lend is a dynamic behavior and may evolve over time along

with the development of the online P2P lending market.

Longitudinal studies on P2P lending would be interesting.

Third, present study was conducted in China, which has

very particular social, economic, and cultural characteris-

tics. Future research may perform cross-cultural compari-

sons between China and other developed countries to

unveil differences in lenders’ behaviors.

9 Conclusions

This study developed an integrated model to examine trust

in the online P2P lending context. The model integrates

cognition-based, institution-based, knowledge-based, and

personality-based trust beliefs to investigate how trust in an

online P2P intermediary and trust in borrowers are culti-

vated and how these two trust beliefs influence lenders’

willingness to lend. The model was tested using data from

785 lenders on PPDai, the first and largest online P2P

lending platform in China. The results revealed that trust in

borrower plays two important roles. It drives lenders’

willingness to lend more efficiently than trust in interme-

diary and it also carries the significant impact of trust in

intermediary on lenders’ willingness to lend. The infor-

mation quality of borrowers’ loan requests is the most

important factor influencing lenders’ trust in borrowers,

and the intermediary’s service quality and protection are

two essential factors to determine lenders’ trust in an

intermediary. These findings provide valuable insights for

both borrowers and intermediaries.

Acknowledgments We gratefully acknowledge the financial sup-

port of National Natural Science Foundation of China (No. 71302008)

and National Social Science Foundation of China (No. 11AZD077).

Appendix

(See Table 6)


123

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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

lending services?

Kim et al. [46] 1.42 0.83

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

during my payback process

Watson et al. [47] 4.70 1.35

Safety and protection (SP) SP1: PPDai implements sufficient security

measures to protect its users

Watson et al. [47] 4.42 1.33

SP2: PPDai usually ensures that transactional

information is protected from being altered

or destroyed during a transmission on the Internet

Kim et al. [46] 4.56 1.33

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

PPDai

Kim et al. [46] 4.57 1.28

SC2: The borrower and I have good interaction and

communication

Lin et al. [1] 4.30 1.26

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

when I try to make a transaction

Kim et al. [46] 4.56 1.25

IQ3: I am satisfied with the information

provided by the borrower

Kim et al. [46] 4.68 1.24

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

by PPDai protect lenders

Pavlou et al. [67] 4.52 1.26

TP3: PPDai tries its best to satisfy the requests

and needs of its users

Pavlou et al. [67] 4.56 1.27

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

that she/he would keep promises

Lu et al. [15] 4.60 1.31

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

for his/her loan request

Gefen [31] 4.42 1.22

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

short if I lend to this borrower

Kim et al. [46] 4.70 1.28

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


123

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