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
e-mail: [email protected]
Z. Lin
The Rawls College of Business Administration, Texas Tech
University, Lubbock, TX 79409, USA
e-mail: [email protected]
123
Inf Technol Manag (2014) 15:239–254
DOI 10.1007/s10799-014-0187-z
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
123
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
242 Inf Technol Manag (2014) 15:239–254
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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.
Inf Technol Manag (2014) 15:239–254 243
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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
244 Inf Technol Manag (2014) 15:239–254
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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
Inf Technol Manag (2014) 15:239–254 245
123
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
246 Inf Technol Manag (2014) 15:239–254
123
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
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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
248 Inf Technol Manag (2014) 15:239–254
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
Inf Technol Manag (2014) 15:239–254 249
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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
250 Inf Technol Manag (2014) 15:239–254
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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)
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123
References
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signaling mechanisms: evidence from online peer-to-peer lending.
Working paper http://pages.stern.nyu.edu/*bakos/wise/papers/
wise2009-p09_paper.pdf
2. Prosper (2010) Prosper closing in on $200 million in loans and
series D. Prosper Blog
3. Krauter SG, Kaluscha EA (2008) Consumer trust in electronic
commerce: conceptualization and classification of trust building
measures. In: Kautonen T, Karjaluoto H (eds) Trust and new
technologies. Edward Elgar, Cheltenham, pp 3–22
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Paper presented at the thirtieth international conference on
information systems, Phoenix
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
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