Factors Influencing Intention to Use Online Social Lending in Lithuania

Šiuškus and Redko, 2007 1
Stockholm School of Economics in Riga
Rigas ekonomikas augstskola
“Factors influencing intention to use online
social lending in Lithuania”
By Povilas Redko and Gediminas Šiuškus

March 2007, Riga
Keywords: online social lending, online consumer behavior, innovation adoption, technology
acceptance, e-commerce, Web 2.0, loans, internet, TAM, DOI, Lithuania
Abstract: This paper is a research with a purpose to find out which factors would influence
Lithuanian consumers’ intention to adopt a new way of borrowing and lending. We present an
innovation – online social lending and try to find out what is necessary for adoption. Two most
commonly applied and empirically supported models of IT-based innovation adoption are
employed. The first model – technology adoption model (TAM) is updated by two extra variables –
risk and self-efficacy to adopt it to this certain innovation. The second model - and second model –
Diffusion of innovation (Perceived characteristics of innovation (PCI?) is modified by removal of
not applicable factors. As a research instrument, questionnaire was delivered through e-mails
(hyperlink) and hand-in paper copies. 327 responses were received and data generated for
statistical model analysis. Our findings reveal that perceived usefulness, ease of use, self-efficacy,
perceived risk, relative advantage, compatibility, and observability have a significant impact on
intention to use online social lending.
Moreover, both models do not fully explain the variance in users’ intention to adopt online social
lending. Thus, the recommendation for further studies would be to try to employ other models and
different factors.
The findings imply that any new web-based financial service (including online social lending)
introduced over the internet must firstly consider demonstrating the usefulness and benefits of their
product.
________________________________________________________________________________
Acknowledgments: We would like to acknowledge the help and encouragement of all who
have assisted in any way during this thesis period. First, the authors would like to express their
appreciation to this thesis supervisor Rokas Šalaševičius and coordinator Karlis Kreslins for
support, valuable guidelines, encouragement and flexibility in the whole thesis writing process. We
are thankful for all expertise and suggestions from our pilot think-tank group of specialist.

Secondly, we personally thank Alminas Žaldokas, Mykantas Urba, Tomas Petrauskas for their
valuable comments, deep insights, moral support, and reviews. Thirdly, we would like to thank all
participants who took part in survey for their valuable time, kind assistance and supplementary
commentaries. We will not have made it without all of you guys!

Table of contents
1. Introduction
1.1 Background
1.2 Development of information technology
1.3 Loans
1.4 Online social lending
1.4.1. How it works
1.4.2 Importance of online social lending
1.5 Research objective
2. Literature review
2.1 Internet usage in Lithuania
3. Theoretical background and framework
3.1 Introduction
3.2 Technology Adoption Model
3.3 Diffusion of Innovation model
3.4 Comparison of TAM and DOI
3.5 Research models and hypothesis
4. Methodology
4.1 Research approach
4.2 Pilot questionnaire
4.3 Target population
4.4 Sampling technique and data collection
4.4.1. Making sure people understand the issue
5. Research findings
5.1 Data analysis
5.1.1 Sample characteristics
5.2 Construct validation
5.2.1 Validity and reliability
5.2.1 Treating Likert scale as interval data
5.1.3 Correlations
5.3 Testing the hypothesis
5.4 Comparing TAM and DOI models
5.4.1 Extended TAM model
5.4.2 Extended DOI model
5.4.3 Summary of model comparison
5.5 Chapter summary
6. Conclusions
6.1 Discussion
6.1.1 Extended TAM model performance
6.1.2 Extended DOI model performance
6.1.3 Model performance
6.2 Limitations
6.3 Contribution
6.4 Conclusions
Appendixes
1. Works cited
2. Questionnaire
3. Summary of previous TAM usage in other researches

1. Introduction
In this chapter we present a short history of taking and giving loans and new opportunities in this
market. Special emphasize is placed on the development of information technology and its effect to
the loan market. By this we explain how online social lending appeared and why it is important.
1.1 Background
The concept of lending itself is very old. People have been lending resources to each other for some
interest for a very long time. The concept is important because borrowing and lending is vital for
business development and growth in general. An idea is worth nothing if there is no financing that
would make it real. Naturally people have been inventing new ways to borrow and lend money all
the time, starting with social groups such as families or small communities, then developing
banking systems and capital markets.
Rapid technological growth and increasing popularity of Internet has created new opportunities both
for business and society in general. Electronic commerce has spread from e-banking to offshore
manufacturing to e-logistics. According to the UCLA Internet Report (2001) Surveying the Digital
Future, electronic commerce has become very popular, being just a bit less widespread than
browsing web pages or chatting online. In 2004 e-commerce figure rose to 65 percent of internet
users who reported this activity (Pewinternet web-link).
All this development offers yet another, even more efficient way for people to borrow and lend –
online social lending. However the concept is so fresh, that majority of people have never heard
about it and it will be a challenge to make it as common as lending money from bank or friends.
1.2 Development of information technology
Recent progress in technology, particularly in the field of ICT , has led businesses in new directions
1
over the last few decades. New forms of trade have emerged from these advances and one is of
particular interest - electronic commerce. Internet as the distribution channel for electronic
commerce (EC) benefits both sellers and buyers. Sellers can access narrow market segments that
are widely distributed geographically in this way extending accessibility globally. Buyers gain from
the access to global market and a great number of products and services (Napier et al., 2001, 100).
1
Information communication technology

Six years after the IT bubble, things have changed in Internet and in ecommerce. Before there were
few websites containing user-generated content, most internet connections were relatively slow.
Now more than 75% of computers are connected to broadband. Second generation (Web 2.0)
websites provide users with functionality that was previously available only in standard desktop
applications. Moreover, those websites allow users to interact real time and share the results of work
they did. In other words, it is not necessary to have software on your computer - most tasks can be
performed by visiting second generation websites. Most known companies of user-generated
content are YouTube, Wikipedia, MySpace and Facebook. According to Nielsen/NetRatings Web
2.0 makes up the fastest-growing category on the Web and is likely to replace usual desktop
applications in the future.
The shift from first to second generation websites has affected electronic commerce, and naturally
online consumer behavior also changed. Trust in online shopping and payment mechanisms has
been increasing ever since. Consumers are buying online more and they are buying more complex
products as well.
1.3 Loans
Taking and giving loans probably started off as borrowing and lending food or shelter and also
taking or giving some extra as an interest for helping out. In societies with underdeveloped
economies the only possible way to borrow was to approach your family or friends. In each culture
there are significant differences, but we can observe that, as society and its economy develops, new
more efficient ways of transacting loans start to appear.
One of first steps was appearance of specific social groups. 300 A.D. in China the first rotating
savings and credit association (ROSCA) appeared (Siwan Anderson). For centuries small groups of
people all over the world have been coming together to lend each other money. Here informal
lenders use collateral substitutes to bear the risk. Third party guarantees, tied contracts, and threat of
loss of future access to credit are common devices in informal contracts (Adams and Fitchett 1992).
Later, as more and more people got involved informal guaranties did not hold anymore. Then
somebody saw it as a business opportunity and banks started to appear. Banks would pool money
that is held as savings, pay some interest and then give out loans using the same money asking for a
bigger interest. They would make profit living off the difference between interests and also by

reducing individual risks by having all the money pooled together. Even though banks still exist as
major source for getting a loan or lending money to someone, they are far from perfect.
Micro credit — small loans to poor people who are neglected by traditional banks — are big news
these days (Economist, 2006). As the prove of that, Muhammad Yunus, founder of the micro credit
Grameen Bank of Bangladesh, accepted the Nobel Peace Prize in 2006 for his work developing the
concept. Micro credit is the extension of small loans to entrepreneurs who are too poor to qualify
for traditional bank loans. According to the latest opinion presented in the media, microfinance
started as a niche business, but now it is micro no more (The Economist, 2005).
We have taken a look at some more significant sources for loans however some new ones are
appearing. According to researchers, Internet, as a distribution channel, had the greatest impact to
financial service industry (Mukherjee and Nath, 2003). Banks have begun delivering their services
online. The benefits of Internet banking are shared between bank and the customer. Banks reduce
operation costs, improve performance and customers enjoy the convenience of e-banking. However
banks face new challenges brought by evolution of Internet and Web 2.0 phenomena.
1.4 Online social lending
While the number of conventional retail banks that introduce online banking is still increasing
(Capgemini, The world Retailing bank report, 19), new types of financial institutions start to appear
because of the possibilities offered by Web 2.0 phenomena. One of them offers a service called
peer-to-peer lending. This service allows borrowers and lenders transact loans through internet
without using ordinary banks as intermediaries. This peer-to-peer lending is also more widely
known as online social lending.
1.4.1. How it works
Try to imagine the concept as an internet auction for loans. Let’s say that Jack wants to buy a small
car to start his own flower delivery business and he needs some 2000 EUR for that. He could
borrow from his friends or family, but does not want to jeopardize the relationship if something
goes wrong. Another option is to go to bank, but the terms are very standardized and the interest
rate is high. The third alternative is online social lending. Jack logs on to a special website
(Zopa.com in UK), and becomes a registered member. After submitting some of his personal
information, he posts a message on the website telling he wants to borrow a certain amount, what
the money will be used for and what interest he would like to pay. Site administrator gives his

message a risk rating. Then they wait for other users to find his message.
Now we look at Irene – lender. She logs on to the very same website and becomes a member. After
providing the administrators some information she gets access at all the messages on the site. Irene
knows she would like to invest around 2000 EUR, a bank can only promise a small interest for such
amount. Instead she starts looking through the messages. She chooses to look at more risky
messages, because loosing the money would not be a big loss for her, however if everything goes
well, she will get a high interest. For her it is almost as fun as gambling. Filtering through messages
she sees Jack’s note and clicks on it. There she gets a chance to read his personal profile and even
chat with him a bit. To her Jack seems like a guy she can trust and they agree on the rate and other
terms.
It was like a small auction, now the transaction has to be made. Website administrators create a real,
fully legal contract between lender and borrower. Once it is signed Irene transfers her money to the
account of the website and then money goes to Jack. He will have to repay through the website too.
0.5% of the loan is left to the website owners to cover the risks and for administration.
If we compare this to a bank, Jack and Irene would never get to know each other, would have to go
for rather standardized terms and both would leave more than 0.5% of the amount to the bank. That
is understandable – the bank has to pay for its branch offices and many other expenses.
1.4.2 Importance of online social lending
Organizing lending in cyberspace is different from traditional lending. It requires understanding of
consumer behavior and how new technologies confront the assumptions present in traditional
theories and models (Butler and Peppard (1998). As in physical world, a good appreciation of the
factors affecting the lending decision would contribute to understanding the cyberspace behavior.
At present there is comparatively little known about how web purchase behavior (selling and
buying loans as well) differs from traditional one and whether there are any specific web-based
factors that should be taken into consideration (Heijden et al., 2001).
1.5 Research objective
Online consumer behavior is an emerging research area with an increasing number of publications.
The articles appearing in variety of journals come from field of Information Systems, Management,

Marketing, and Psychology. Despite that, there are still significant disparities in explanations of
consumer online behavior (Llimayem, M., et al., 2003). Studies lack the essential understanding of
the factors influencing consumer’s decision to buy or sell on the Web. Therefore we want to shed
some light on matter of the online consumer behavior in Lithuania. We hope to find out which
factors affect Lithuanians decisions when adopting online social lending.
The research is the following: What are the factors influencing the Lithuanian consumer’s
intention to use online social lending? To answer the question first we take a look at previous
studies in chapter 2, then we select two models and propose hypothesis in chapter 3. In chapter 4 we
describe the methods used to collect necessary data, in chapter 5 we present the analysis and finally
in chapter 6 we discuss the results.

2. Literature review
In this chapter shortly goes over the literature related to the online consumer behavior. It tracks the
basic trends in the online banking sector as one of the key trend setters for the financial activities in
the Internet. Later we provide a basic understanding of adoption theories used in this thesis.
Online consumer behavior is an emerging research area these days as more and more of our daily
life is spend on Internet. The number of research in the field of e-commerce is growing (Cheung et
al., 2003, 3). Yet, concerning online social lending there is only one study to this date (Collette
Wrights, 2006): Internet based social lending: past, present and future. To get a better insight on
how consumer behaves online we look at the studies about online consumer buying behavior. The
current literature of consumer online purchasing decisions mainly focuses on identifying the factors
influencing the willingness of consumers to engage in Internet shopping.
Consumers' attitude towards online shopping is a major factor affecting buying behavior. In 1997
Jarvenpaa and Todd proposed a model of attitudes and shopping intention towards Internet
shopping in general. The model grouped major indicators into four chief categories: product value,
quality service of website, shopping experience, and the risk associated with Internet shopping.
Another research, conducted in 2001 (Vellido et al., 2000, 83-104), found nine factors associated
with users' perception on e-shopping: risk associated with Internet activity, convenience and control
over shopping course, customer service, ease of use and affordability of services. After tree years
Jarvenpaa together with associates. [2000] came out with other model, which analyzed consumer’s
attitude towards specific online stores. The base of this model was that perceptions of the store's
reputation and size influence consumer trust of the retailer. The findings were that the level of trust
was positively linked to the attitude toward the store, and inversely related perceived risks involved
in buying from that store. This study concluded that consumers’ attitude and risk perception
affected their intentions to buy from the e-store. Here we present the definitions and sum up
findings of factors affecting intention to shop online.
Intention to shop online - refers to the likelihood that a consumer actually buys online (Chen et al,
2002). This factor was frequently treated as dependent variable in the presented studies. This
determinant is very crucial in determining the online consumer’s behavior (Chen et al; Goerge
(2002); Goldsmith 2002, Limaeym et al., 2000)
1. Attitude – indicates how consumer evaluates the consequences of performing certain act online
(Athiyaman, 2002). This factor is consistent with the online adoption behavior studies, where this

factor is found to be considerable influencer of intention (Athiyaman, Chen et al., 2002)
2. Perceived ease of use – determines the degree to which a consumer believes that use of
particular system would be simple and will not demand of any extra effort (Davis, 1989). Perceived
ease of use has been at the centre of attention in academic and practical studies of technology
adoption.
3. Demographic variables – cover the following determinants: education, gender, income, age and
lifestyle. For instance, the opinion of the age as determinant of online consumer intention is
two-sided. Some researchers (Case et al., (2001), Goldsmith and Goldsmith (2002) and Kwak et al.,
2002)) claim that age is of no importance to online shopping behavior, whereas Teo (2001) state
that it really has great one. According to the previous researches education plays a vital role in
explaining the intention to buy online.
4. Internet usage and experience – according to researchers’ (Citrin et al., 2000 and Goldsmith
(2002)) consumers with high Internet literacy level are more likely to engage in shopping and/or
other internet activities. Experience itself considerably affects the intention to shop online (French
and O’Cas, 2001, Vijaysarathy ad Jones 2000). Thus internet usage and experience are important to
online shopping behavior.
5. Perceived behavioral control – indicates person’s perception about the ease/difficulty to
perform certain behavior (Athiyaman, 2002). Basically in certain situations a person having an
intention to complete a specific act may be unable to do so. The environment where individual
belongs can prevent him from completing the act. To be more specific, if a customer will not have
computer, internet access, and any other personal assistance the act will not take place. Therefore
this factor is important in make possible online shopping behavior.
6. Perceived consequence – each of our behavior is perceived to have a positive or negative
outcome. An individual chooses to act in certain way based on the consequences that he/she
anticipates. Limayem with other his collogues discovered that perceived consequences have an
important influence on individual’s intention to shop online (Limayem et al., 2000, Limayem and
Rowe 2002). Even if individual is favorable of online social lending, he may not adopt it due to
his/her perceived meaningful negative consequence.
7. Perceived usefulness – defines the degree how consumer believes that usage or engagement with
certain technology would enhance his/her activity (Davis 1989).

8. Personal Innovativeness – explain the degree to which an individual is relatively earlier in
adopting an innovation. Online social lending must be considered as an innovative behavior because
it is more likely to be adopted by innovators than non-innovators. Research has proved that
innovativeness is an important antecedent of intention to shop online (Limayem and Rowe 2001)
9. Risk – is uncertainty level that individual associates with certain activity and in our case
consequence of buying online (Gazioli and Jarvenpaa 2000).
10. Trust – is determinant that verifies the confidence level a costumer has in what other people
will do, based on previous interactions. Researcher Lunch et al., (2001) has found a significant link
between trust and consumer’s intention to shop online.
2.1 Internet usage in Lithuania
There are 1 million internet users as in Lithuania, 30 % of the population, according to the
International Telecommunications Union (http://www.itu.int/home/index.html) and market research
company “Gemius” (www.delfi.lt, 2006). The age structure of these one million users is the
following:
15 - 24 years: 44.2 %
25 – 34 years: 19.3 %
35 – 44 years: 20.6 %
45 – 54 years: 10.2 %
55 – 74 years: 5.7%2
As we see the biggest part of internet users are young people and they represent almost half of
Lithuania’s internet population. There are no freshmen-users, who have used internet only for one
year, as gemiusAudience states: 76% of respondents used internet more then several years. Almost
71% of internet users surf it each day. The average Lithuanian internet user can be depicted in this
manner: 23-24 years old student or early employed (with income level ranging from 800 to 1500
litas per month). She lives in Vilnius and connects to internet each day from home or work and uses
it already for several years. Besides that, internet usage differs between genders. Evaluating
audience composition of top visited web-sites we can deduct that there are female and male intersts.
Men mostly surf about sport, cars, games, and IT. In the case of females, they are attracted by
2
TNS Gallup market research: Internet usage in Lithuania, 2007-01-02, (percent from those who used internet over 6
months: 2006 autumn)

beauty, health, fashion, motherhood, lifestyle, entertainment, greetings, and financial services.
At home, 31.10% of population have a computer and 15.45% - internet . From occupation point of
3
view, Lithuania’s internet market is dominated by specialists-office workers (32.75% of audience)
and students (33.10%).
Almost 30 % of Lithuanians internet users purchased goods and services on the internet. This data
4
was revealed in the study of international e-commerce trends, which was conducted in Lithuania by
market research company Gemius Baltic and internet advertising company TradeDoubler. Out of
these 30 per cent, who have at least one time bought online, 18% do internet shopping once a month
or more. Whereas 38% of e-shops and e-auction goods and services is obtained few times a year,
rest even more rarely. Lithuanian internet users mostly buy books, CDs and movies – 43 % of
respondents. Travel tickets are ordered by 35% of buyers, and 28 % purchase tickets for
entertainment. 14 % of respondents manage their finances by paying for utilities, internet, cable TV
and mobile connection. 5% of respondents pay for their loans, leasing, life and health insurance, 3%
buy online tickets, and only small fraction of 0.3% - flight tickets. Various goods are bought 2% of
respondents .
5
As Gemius Baltic indicates, development and growth of e-commerce can be accelerated by changes
in user’s perception to shopping online. Half of respondents are aware of e-commerce safety in
Lithuania, and 1/5 thinks that the process itself is complicated. Nevertheless, the survey data
forecasts the long-term increasing popularity of e-commerce in Lithuania. Even 63% of those who
have never purchased over the internet were thinking of such possibility. 10% of them say that they
determined to buy in the future, and 36 are still thinking to do so. Only 6 % have decided not to use
e-commerce at all.
80% of Lithuania’s internet users use e-banking. When choosing bank and e-banking, the most
important thing is security. Second factors that stress the importance of e-banking – extensive
information about the service and ease of use. Peer recommendation is only vital for 5.08 %
respondents. Complexity is in the 5th
place.
5
2007-03-14, BNS news center
4
http://www.infobalt.lt/main.php?&s=42&r=638&i=7240
3
TNS Gallup, 2006 march, computer and internet penetration in households 2004 and 2005.

3. Theoretical background and framework
In this chapter we describe the framework we are going to employ in our research. We choose two
models, TAM and DOI which are most often used when analyzing online consumer behavior and
innovation adoption.
3.1 Introduction
Understanding why people accept one and reject one or another IT project is one of the most
challenging issues in information systems research (Swanson 1988). As it was showed before
online consumer behavior research findings were mixed and inconclusive. Later observers have
examined that some studies employed wide array of different belief, attitude, and satisfaction
measures, without adequate theoretical or psychometric justification. Therefore, further information
systems investigators have suggested using intention models from social psychology as a potential
theoretical foundation for research on the determinants of user behavior (Swanson 1982).
Findings showed that the Theory of Reasoned Action (TRA) and its family theories including the
Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) are the
dominating theories in the field of consumer e-commerce. TAM appears to be most often used when
analyzing consumer behavior with respect to technology. Diffusion of innovation (DOI) has also
been repeatedly tested in the study of online consumer behavior however it puts more emphasis on
the innovation.
We decide to employ both TAM and DOI since these are the most trusted, empirically tested and
often used models in similar researches. From further descriptions it will become clear however that
both models are used to explain the same outcome with different sets of factors. Comparing
empirical observations with connections implied by the two models will reveal which set of factors
is more accurate when explaining consumers’ intention to engage into social lending through
internet.
3.2 Technology Adoption Model
Davis developed technology acceptance model (TAM) was adopted from TRA. The objective of
TAM is to provide an explanation for technology users’ behavior. As recent studies suggest TAM is
applicable to e-commerce and to the adoption of Internet technology (Gefen and Straub, 2000).

TAM states that user’s intention to a new technology is determined by their perception about the
usefulness of technology and attitude towards the technology use (figure 1.). Attitude is jointly
influenced by two behavioral determinants: perceived usefulness and perceived ease of use.
External variables, such as task, user characteristics, social environment, are expected to influence
technology acceptance behavior indirectly by affecting perceived usefulness and perceived ease of
use (Szajna, 1996). A great number of research papers support that perceived usefulness influence
user intention and behavior over time (Taylor & Todd, 1995b; Venkatesh & Davis, 1996).
Figure 1. Technology Acceptance Model. Source: (Davis et al., 1989)
Although TAM is very powerful model for explaining and predicting much of the variance in new
IT acceptance but it excludes the influence of social norms and perceived behavioral control on
behavioral intention. We believe that the proper model for this research should include the social
norm and behavioral control factors. Subjective norm refers to one’s perception of social pressure to
perform or not to perform or not to perform the behavior under consideration (Athiyaman, 2002).
Furthermore, Hartwick and Barki (1994) suggested the effect of subjective norms to be more
significant in the initial stages of system implementation. Since the online social lending system has
not been developed in Lithuania we expect that subjective norm should also affect the intention to
use the online social lending.
3.3 Diffusion of Innovation model
Innovation is the process of making improvements by introducing something new or simply a
successful introduction of something new. Roger’s theory (Rogers, 2003) stated that innovation is
more likely to be adopted within these assumptions:
Relative advantage – innovation is better than existing technology
Compatibility – it is possible to compare the innovation with existing technology
Complexity – innovation is easy to understand and use
Trialability – innovation can be experimented with before adopting
Observability – the result of innovation is clearly visible (Pease & Rowe, 2004).
Researches observed that usually only a few early adopters, who are active information seekers of

new ideas, don’t rely on opinions of others, have access to resources necessary to adopt changes,
have good formal education, are able to cope with risk and uncertainty and are willing to adopt an
innovation at an early stage.
Figure 2. Diffusion of Innovation model.
DOI has been widely used to understand consumers’ attitude and adoption of various innovations .
6
Technology-based consumer related innovation such as Internet baking service or any other kind of
e-commerce (including social lending) represents an innovation where both intangible service and
an innovative medium of service delivery employing high technology are present.
3.4 Comparison of TAM and DOI
Both these models focus on individual’s perception about innovation attributes that affects
consumer intention to use the technology. Two theories differently define their perceptions. TAM
includes two perceptions that come from target users and can be different for every new innovation
(Agarwal and Prasad, 1997), while DOI presents five perceived characteristics of an innovation that
affect consumer behavior (Rogers, 2003). Plouffe et al. (2001) claimed that DOI’s determinants
explain a higher proportion of variance than TAM when they are used as predecessors to adoption
intention. TAM places relatively lower strains on respondents and researchers compared to DOI
(Plouffe et al., 2001). Nevertheless, conceptual similarity of TAM and DOI on technology intention
behavior and that the set of determinants used in TAM is in many ways similar to some of DOI’s,
this study plans to apply both TAM and DOI models to identify factors that influence social lending
intended adoption in Lithuania.
3.5 Research models and hypothesis
Previous insights and conclusions are used to develop two research models analyzing the adoptive
intention to use social lending online. The first model takes the original TAM and adds other
significant factors according to findings and extended TAM model. The second research model is
based on the Roger’s DOI model (2003) taking into considerations proposed few modifications by
Moore and Benbast (1991) and adopted the Internet social lending.
3.5.1 Research model 1 and its hypothesis
6
(Howcroft, Hamilton, & Hewer, 2002; Lee & Lee, 2000; Moore & Benbasat, 1991; Tan & Teo, 2000)

CIA KOPINTA MANAU.
Similarly as with Internet banking, we propose to add for social lending TAM an internal control
factor perceived self-efficacy, a very important factor explaining Internet banking usage within
perceived behavioral control in TPB (Luan and Lin, 2004). Further, perceived risk is added to the
model since it is a widely recognized obstacle to the adoption of Internet-related financial
applications in prior studies. The security and privacy issues are found to be significant concern for
users while lending or borrowing over the Internet.
Perceived ease of use refers to the degree to which a person believes that using a particular system
would be free of effort. In this study perceived ease of use refers to the attitude and degree to which
consumers believe that using the online social lending system would be easy and free of effort.
Thus, we propose:
H1: Perceived ease of use will have a positive effect on the attitude towards using online social
lending.
H2: Perceived ease of use will have a positive effect on perceived usefulness of online social
lending.
Perceived usefulness refers to the degree to which a person believes that using a particular system
would enhance his or her life. In this study perceived usefulness represents the degree to which
borrowers and lenders believe in positive consequences of using online social lending system. As
such we propose:
H3: Perceived usefulness will have a positive effect on the attitude towards using online social
lending.
According to Ajzen (1991) the construct of perceived behavioral control reflects beliefs regarding
the availability of resources and opportunities for performing the behavior as well as the existence
of internal/external factors that may impede the behavior. Hence, we agree with Taylor and Todd's
(1995) decomposition of perceived behavioral control into" facilitating conditions" and the internal
notion of individual "self-efficacy". Self-efficacy indicates an individual's self-confidence in his or
her ability to perform certain actions. In terms of internet purchases, if an individual is self
confident about engaging in activities related to purchasing online, he or she should feel positive his
or her behavioral control (George, 2004).
H4: Perceived self-efficacy will have a positive effect on intention to use online social lending.

Internet shopping is a new form of commercial activity, which tends to involve a higher degree of
uncertainty and risk when compared with traditional purchasing activities. Trust and perceived risk
(e.g. Jarvenpaa et al 2000, Pavlou 2001, Ruyter et al 2001) have been widely investigated in the
study of consumer online purchase intention. Risk refers to the distrust a person has in his or her
expectations of what other people will do, based, in many cases, on previous interactions. Hence,
we propose:
H5: Perceived risk will have a positive effect on the attitude towards using online social lending.
H6: Perceived risk will have a positive effect on the intention to use online social lending.
Attitude refers to one's evaluation about the consequences of performing behavior. In this research
attitude represents passengers' positive or negative feelings towards online social lending (lending
and borrowing money over the internet) that affects the intention to use online social lending As
such, we propose:
H7: Perceived attitude will have a positive effect on the intention to use online social lending.
Subjective norm refers to one's perception of social pressure to perform or not to perform the
behavior under consideration. Considering the fact that current Lithuanian culture is more
individualistic than collectivistic and that individualist are less likely to comply with others than are
collectivists, we expect that Lithuanian consumers’ intention will be conservative towards using
online social lending. Since the online social lending is not yet introduced in Lithuania and
therefore we expect that subjective norm have a strong positive influence on the intention to adopt
online ticketing. As such, we propose:
H8: Subjective norms will have a positive effect on the intention to use online social lending.
Figure 3. Research model 1 – extended TAM
3.5.2 Research model 2 and its hypothesis
Rogers (2003) describes the innovation-diffusion development as user’s “an uncertainty reduction
process” (p. 232). To decrease this uncertainty about the innovation (in our case about online social
lending) researchers suggested attributes of innovations. These five innovation attributes make our
2nd
model’s factors for assessment of how online social lending is perceived by Lithuanian users.
The DOI model includes five attribute characteristics of innovations: (1) relative advantage, (2)
compatibility, (3) complexity (contra – ease of use), (4) trialability, and (5) observability. We

customize DOI model to our needs with respect to TAM model and suggestions from Moore and
Benbasat. Researchers proposed to change complexity factor into ease of use, and added few new
factors – image and voluntariness. Image in our analysis is not included in the model due to its low
importance to online social lending, where transaction is conducted individually. Voluntariness is
also deducted from the model, as it is not relevant to analysis of innovation in non-organizational
context according to researchers. Figure below presents the finally customized model and
relationship between hypotheses.
Figure 4. Research model 2 (extended DOI)
Relative advantage (RA) is a factor that deals with level of convenience, increase in comfort and
saving users time and efforts in adopting a certain innovation. RA in social lending can be
perceived by users as easy accessibility of resources in any location and at any time. A user is in
control of his/per personal finances and flexible in managing them. Few studies on e-banking (quite
close area for online social lending) have proved that RA is one of determinants that effect
consumer’s intention and later adoption of the service (Ekin & Polatoglu, 2001; Tan & Teo, 2000;
Kolondinsky & Hogarth, 2001). Yet, another study revealed that RA had weak significance in
explaining the intention to use internet innovation (Agarwal and Prasad, 1997). This study explains
that users become most aware of innovation due to its high visibility regardless of any benefits and
conveniences it provides. To sum up, after thorough literature review we see that online social
lending is perceived more relative-advantageous than simple social lending carried only with your
peers. We see that individuals would be more likely to adopt such service over the alternative that
he/she has now. Therefore such hypothesis is tested:
H9: Perceived relative advantage will have a positive effect on the intention to use online social
lending
Compatibility is another factor that has great effect on consumers/users intention to use innovation.
Rogers (2003) himself stated that “compatibility is the degree to which an innovation is perceived
as consistent with the existing values, past experiences, and needs of potential adopters” (p. 15).
Basically if an innovation is compatible with an individual’s needs, then uncertainty will decrease
and the rate of intention to adopt the innovation will increase. We expect that people who recognize
online social lending more compatible to their needs and life-habits, they would have greater
intention to use the service (Tand & Teo, 2000). Thus we test the following hypothesis:

H10: Perceived compatibility will have a positive effect on the intention to use online social lending
Trialability is the factor that is omitted from the model as such service is not available for trials in
Lithuania. Here we were not able to offer opportunity for our respondents to experiment with online
social lending innovation on a limited basis. Therefore, the trialability factor of DOI model was not
included in the study.
Observability is the final factor characterizing innovation. It is defined as “the degree to which the
results of an innovation are visible to the user” (Rogers, 2003, p. 16). Similar to relative advantage,
compatibility, and trialability, observability IS expected to positively correlate with the intention to
adopt a social lending innovation. Therefore, the last hypothesis is the following:
H11: Perceived observability will have a positive effect on the intention to use online social lending
Ease of use is found to be significant determinant to contribute to user’s intention to adopt
innovation, as most academics sum up. Rogers (2003) defined complexity or contra-ease of use as
“the degree to which an innovation is perceived as relatively difficult to understand and use” (p.
15). We expect ease of use to correlate positively with intention to adopt the innovation. If online
social lending system is user-friendly, then they might be adopted successfully for the usage of
lending/borrowing money. However we do not test a separate hypothesis, because it is very similar
to H1.
4. Methodology
In this chapter we describe our research approach, questionnaire development and sampling
techniques. The questionnaire was developed in two stages – a pilot survey was presented to a
group of experts. After their feedback adjustments were made and then questionnaire was presented
to the main audience.
4.1 Research approach
Taking into account that our aim was to test numerous hypotheses we decided to choose a
quantitative approach over qualitative one. Our research strategy was to create two identical
questionnaires – one online and one printed on paper and then analyze responses statistically. Other

alternatives were making historical researches, experiments or case studies. However since research
topic is so fresh, all these choices would provide us with less tangible results. Also, we choose our
research to be deductive, e.g. we built a theoretical model and then check how it works.
4.2 Pilot questionnaire
The construction of our questionnaire began with conducting a pilot study on local experts in fields
very closely related to online social lending: finance, IT, and sociology. The experts and their
spheres of specialization are presented in the table below:
Specialist Company Position Core responsibility /
specialization
Evaldas Tylas Metasite Business
Solutions
Experienced analyst E-banking and
e-commerce solutions
Martynas Daugirdas Ernst & Young Baltics Experienced analyst Technologies & Solutions,
Business process
re-engineering, Enterprise
IS strategic planning, IT
project management, IS
requirements specification
Valdas Virbalas Ernst & Young Baltics Manager, Business
development
Information systems
implementation, Business
planning, financial
modeling and strategy
development
Žygintas Bernotavičius Ernst & Young Baltics Experienced analyst IS implementation, IT and
IS audit, Information
security audit, IT practice
financial modeling
Paulius Kriščiūnas SEB Director E-banking department
Aleksandras Dobryninas Vilnius University,
sociology faculty
Professor Consumer behavior, future
society trends
Table 4.1 Experts presented with our pilot quesionaire
We provided the experts with summarized findings from previous researches. A pilot study of our
analysis of online social lending framework was conducted with them to eliminate risks and
incorporate the environment knowledge into the study. They warned us about possible weak points
and suggested with ways to improve our survey.
In our in-depth specialist interview, we discovered that the biggest threat from the consumers will

be the absence of need. Second thing that must be taken into consideration - Lithuania is a small
country and not so many people are using e-commerce to buy goods or products. Thirdly,
sociologist and other experts described Lithuanian consumer as quite conservative who is not
interested in new ideas and would like to see others try it before. Therefore they suggested
explaining explicitly how the online social lending works and what its weak and strong points are.
Overall, we got the local knowledge on people’s possible perception of online social lending from
the pilot study and made the necessary changes to the survey. Here are the actual improvements
made to the study design and the research process:
The survey was delivered to the target group both electronically and in paper format;
Questions observing the person’s intention should be at the beginning of the survey before
the technicalities are asked (e.g. ease of use, compatibility);
Control questions for credible and sustainable responses were included in the survey;
Simple questions and presentation
Asked about the intention to use, but not about adoption the innovation.
4.3 Target population
If we want to know where Internet is heading today, we must look at the most innovative and
adoptive users of internet – teenagers and youth. In a study released this January, there is a finding
of how common using social networking sites has become among teenagers, especially those over
14 (Aline van Duyin, 2007). More than half (55%) of American children aged between 12 and 17
use online social networking sites such as MySpace, research by the Pew Internet & American Life
Project has found.
Due to this reason people we are most interested in are teenagers and young adults, the ones that are
most likely to adopt the innovation. However we do not shy away from answers provided by older
respondents. Both online users and people on the street were approached. All respondents are
Lithuanian and the questionnaire is in Lithuanian language. Our aim was to build up a sample of
500 hundred answers. However this was a very optimistic aim, since actual response was lower.
4.4 Sampling technique and data collection
As already mentioned before, we distributed two identical surveys, one through several internet
channels and one in the shopping malls to be filled out on paper. Distribution over internet was not

completely random, we posted a link to our survey webpage on different universities discussion
boards or mailing lists. This was done for two reasons. First is a higher response rate – students
know how hard it is to gather data, so they are more willing to answer. Second, young and educated
people are more likely to adopt innovations, so they would be able to provide much stronger
opinions about this particular innovation. Besides that, we have sent out a link on random
discussion boards, and sent emails through our own contact lists. We also posted our link as
comments of Delfi.lt articles about internet usage and other internet newspaper articles. We also
used contact lists provided by our experts mentioned before therefore some completely random
people got a chance to fill out our survey.
As for the paper version we went to major shopping malls on weekends and asked random people to
fill out the questionnaire. Each time we told a little story explaining how it works. Even though
some respondents were below legal age (younger than 18), their opinion is important too, because it
represents the overall attitude and intention of the nation. Besides, these young people would be
able to use the system as it would start off when they are already over 18.
4.4.1. Making sure people understand the issue
We have put a lot of effort to be sure that respondents understood the issue. Online version had an
interactive Flash animation showing how the concept and the actual website work. Afterwards there
was a possibility to fill out the questionnaire. As for real life responses, we tried our best to explain
the concept in an appealing and simple way, making real life examples and showing some print-out
graphs. Besides that, we had controlling questions in the surveys asking if respondent understood
the concept or not. There were just a few who filled out the survey without understanding how it
works, so we believe our effort paid off.

5. Research findings
In proceeding chapters we have described theoretical background and the framework used to
answer our research question. In this chapter we analyze the collected data and present the results.
5.1 Data analysis
This section begins with presentation of respondents’ sample and its demographic characteristics.
Afterwards analytical statistical measurements are conducted and their implications presented. First
step of the empirical analysis – validity and reliability of the whole survey constructs testing.
Secondly, hypotheses and their model interconnections are observed and evaluated using the data
from the survey. Finally model comparison is carried out to figure out which model is better at
explaining empirical observations of this research.
5.1.1 Sample characteristics
The response rate to the questionnaire was lower than expected, but still good. Out of 327 responds
that filled up the questionnaire, 242 were submitted online and 85 – prints. The respondents were
quite equally divided from the both delivery channels. The responses were more or less equally
divided between males (48.44%) and females (51.56%) with a slightly higher response rate from the
female side. From the perspective that online social lending deals with the internet, this confirms
general usage of internet between genders.
Figure 5. Demographics
We can draw the parallels between our sample and Lithuanian internet population, as it suits
the same age: 80.47% of our sample is 21-30 years old (rest, 15-21 years – 10.16%, and 31-54 years
– 9.38 %). The same trend of younger people liking innovations was pointed out during the pilot
study. This group of 21-30’ers consist of people who are most interested in innovations and new
things internet provides us.
Finally, the other demographic figures related with internet are presented in the graphs. Using the
demographical data, average respondent is described. It is she, who is of 21-30 years old, and has
been using computer and internet for 3-5 years. She is a mature e-banking consumer, using this

service for 3-5 years, and has either never or just 1 time bought online. Principally we can conclude
that our average interviewee is similar to the average Lithuanian internet user, described
beforehand.

5.2 Construct validation
We chose to test validity and reliability of the items we have included in our survey. We want to be
sure that they provide us with consistent and reliable results. Analyzing each item will let us know
exactly which ones can be included into the regressions and other tests. According to David (1989)
construct validity shows to which degree the scale being used represents the concept about which
generalization will be made. Therefore we want to know to which degree our items are good
enough to run statistical tests on them and then make more general conclusions.
5.2.1 Validity and reliability
It is possible to measure construct reliability statistically by using a measure called Cronbach alpha
coefficient and we choose to employ this method since it is widely used in other researches.
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Cronbac
h Alpha
ATT1 0.731 0.921
ATT2 0.632
ATT3 0.674
ATT4 0.690
PEU1 0.599 0.821
PEU2 0.550
PEU3 0.570
PEE1 0.746 0.766
PEE2 0.778
PEE3 -0.570
SUN1 0.503 0.664
SUN2 0.545
SUN3 0.605
SEF1 0.669 0.610
SEF2 0.800
SEF3 0.715
SEF4 0.660

PER1 0.786 0.739
PER2 0.617
PER3 0.760
PER4 0.621
PER5 0.816
REA1 0.688 0.713
REA2 0.551
REA3 0.763
COM1 0.784 0.839
COM2 0.733
OBS1 0.510 0.503
OBS2 0.701
Table 5.1 Factor analysis and Cronbach alpha coefficients
Examining the alpha values of each item group tested reliability of the construct. According to
Nunnally (1976) construct can be considered reliable as long as its alpha value is above 0.6. From
the table 5.1 we can see that all constructs’ alpha values were above or just slightly above 0.6,
except the last one – observability. This proves that that our constructs are reliable, but we also need
to check if they are valid.
Construct validity was tested by running a factor analysis on items of our survey and checking if
they converge together into constructs as they were supposed to, or split up among different factors.
Also Nunnally (1976) suggests that values for factor analysis should be above 0.5, therefore in this
table we already exclude everything below this value.
The results in table 5.1 reveal that construct perceived attitude has been loaded into two different
factors. Technically we should use them in analysis separately, but since Cronbach-alpha is so high
(0.921) we keep these items in one group.
Perceived usefulness is fit in the factor 1 with the first item falling out of the construct. We omit the
item PEU1. Perceived ease of use falls into factor 2 with the third item being misplaced,
subsequently item 3 was removed from the analysis.
Subjective norm construct was not perfect either, with third item being connected to factor 8. Due to

this misplacement the item SUN3 was taken out of the analysis. Self efficacy construct was not
flawless either - first three items were loaded into factor 6, but the last in factor two. We omit the
last one.
Perceived risk originally consisted of five items, most of which were loaded into factor 4. However,
the first item was loaded into factor 7 and the second item was assigned to factor 5 which is shared
with some items from perceived attitude, which implies that people perceived those items similarly.
Relative advantage and compatibility were both loaded on factor 1, which means people did not see
a distinct difference between the items. Observability was loaded into factor two just like perceived
ease of use, which implies respondents saw a relationship between the two items.
Below is the final result of testing data reliability and validity, in the table 5.2 we present how many
items were left in each construct for further tests. We had to make adjustments almost to each of the
groups of items, therefore our construct was far from perfect. However after employing these
statistical tools the remaining data can be treated as reliable and valid.
Construct Number of items in the survey Items used in analysis
ATT – Attitude 4 4
PEU – Perceived usefulness 3 3
PEE – Perceived ease of use 3 2
SUN – Subjective norm 3 2
SEF – Self efficacy 4 4
PER – Perceived risk 5 3
REA – Relative advantage 3 3
OBS – Observability 2 2
COM – Compatibility 2 2
Table 5.2 Construct items included and survey and used in analysis

5.2.1 Treating Likert scale as interval data
You might recall that in our survey we used a Likert scale to measure the respondents’ attitude by
giving seven possible degrees of answers ranging from 1 to 7. Some scientists believe the responses
can be directly converted into interval level data (Cooper, 2001) and analyzed statistically as any
other data. Others however argue that it is not possible, because the difference between 1 and 2
(fully disagree and strongly disagree) is not equal to difference between 3 and 4 (disagree and
indifferent).
There is a choice between using parametric and non parametric tests. The later are more flexible
and can detect smaller differences due to more precise measuring techniques. There were numerous
researches conducted analyzing usability of parametric tests, and they turned out to be equally
powerful for analyzing both ordinal and interval level data. Taking into account such findings we
decided to employ parametric tests for analyzing survey data. In some situations we run
non-parametric tests for comparison of results.
5.1.3 Correlations
Before testing the hypothesis we wanted to check for possible associations between variables.
Therefore we calculated Pearson coefficient of correlation for all the variables shown in table 5.3.
We perform a one-tailed test to predict a direction and strength of relationship. The results are
shown in table below. We expect the items to be slightly correlated, because of relationships
between the factors.
ATT PEU PEE SUN SEF PER REA COM OBS
ATT 1
PEU 0.4995 1
PEE 0.2227 0.1557 1
SUN 0.3479 0.2868 -0.1497 1
SEF 0.2625 0.2683 0.3936 -0.0945 1
PER -0.1710 -0.2138 -0.2234 0.1285 -0.0396 1

REA 0.2478 0.5327 0.07350 0.1615 0.1840 -0.1858 1
COM 0.4295 0.4985 0.10014 0.3018 0.2415 -0.179 0.6263 1
OBS 0.1972 0.1844 0.5141 0.01913 0.3088 -0.158 0.2665 0.2400 1
Bold italic means correlation is significant at 0.01 level
Bold means correlation is significant at 0.05 level
Table 5.3 correlations between items
Results show that users’ perceptions were significantly correlated. They also revealed directions as
expected and therefore provided support. Previous researches suggest that multicollinearity is a
common problem when analyzing results or surveys and field interviews, because researcher has
little control over the predicting variables. In an experimental setting there is no such problem. In
plain words, analyzing real life data breaks lots of assumptions that models are usually based on,
therefore models have little explanatory power and a lot of exogenous effects.
Multicollinearity is the degree to which one variable is explained by another variable in research or
how similar they are (near linear dependence). We would prefer to avoid it since multicollinearity
makes it harder to see the effects of single variables to other variables – everything become mixed
up and interrelated. One of most simple ways to detect multicollinearity is to measure R2
as we are
doing in this case. We can observe that some variables have correlation almost as high as 0.5, but
none of them reach 0.8 which is suggested the critical level (Thong, 1999)
Attitude was found to have significant relationships with all other variables, being highly correlated
with perceived usefulness and compatibility. This is most likely because usefulness and
compatibility are highly important when forming initial attitude towards the idea. Perceived
usefulness had high correlation with Relative advantage and Compatibility which means that
probably most respondents view the concepts as similar or implied direct connection between them.
Perceived ease of use was not very highly correlated with other variables except for Observability.
We would explain such result by saying that users perceived the product easy to use if they could
immediately observe and understand the results of their actions. The rest of variables were not
highly correlated, except for Relative advantage and Compatibility. Respondents probably saw high
compatibility as big relative advantage.
5.3 Testing the hypothesis

To test the hypothesis stated in chapter 3 we run simple linear regressions and calculate path
coefficients. Such regressions are good for testing the relationships between dependent and
independent variables. They are often used in other researches, mostly because of their simplicity.
We accepted hypothesis at 0.05 levels.
Independent variables Perceived ease of use, Perceived usefulness, Perceived risk were regressed on
a dependent variable Attitude (H1, H3, H5). Perceived ease of use was also regressed with
Perceived usefulness as a dependent variable (H2). Also, independent variables Self-efficacy,
Perceived risk, Attitude, Subjective norm, Relative advantage, Compatibility and Observability were
regressed with dependent variable Intention to use. You can see the results in the table below.
Coeff. t p R2
Correlati
on
H1 PEE → ATT 0.142 3.031 0.030 0.050 0.223
H2 PEE → PEU 0.125 2.092 0.038 0.024 0.156
H3 PEU → ATT 0.399 7.650 0.000 0.250 0.500
H4 SEF → INT 0.537 4.466 0.000 0.102 0.319
H5 PER → ATT 0.145 2.303 0.022 0.029 0.171
H6 PER → INT 0.418 4.051 0.000 0.085 0.292
H7 ATT → INT 0.786 6.959 0.000 0.216 0.465
H8 SUN → INT 0.358 3.798 0.000 0.076 0.275
H9 REA → INT 0.487 4.256 0.000 0.093 0.305
H10 COM → INT 0.552 7.475 0.000 0.241 0.491
H11 OBS → INT 0.276 2.638 0.009 0.038 0.195
Table 5.4 generalized results of hypothesis testing
Hypothesis H1 that “Perceived ease of use will have a positive effect on the attitude towards online
social lending” is supported (Coeff. = 0.142, p = 0.03). The findings are consistent with what TAM
model suggests. And it suggests that if people find the innovation easy to use, their attitude towards
the innovation will be higher. However the effect is only partial.
Hypothesis H2 that “Perceived ease of use will have a positive effect on the perceived usefulness of
online social lending” is supported (Coeff. = 0.125, p = 0.038). It is consistent with TAM and with

previous studies that support TAM. The result suggests that people will find the innovation useful if
it is easy to use.
Hypothesis H3 that “Perceived usefulness will have a positive effect on the attitude towards
intention to use online social lending” is supported (Coeff. = 0.399, p < 0.01). This is also
consistent with TAM. Results suggest that people’s attitude will substantially improve if they find
the innovation useful.
Hypothesis H4 that “Perceived self-efficacy will have a positive effect on intention to use online
social lending” is strongly supported (Coeff. = 0.537, p < 0.01). Result is consistent with
relationship offered by TAM. It also suggests that people have much higher intention to use the
innovation if they have the necessary equipment and skills to become users.
Hypothesis H5 that “Perceived risk will have a positive effect on the attitude towards using online
social lending” is supported (Coeff. = 0.145, p = 0.022). The result means users will have a much
more positive attitude toward the new idea if it contains little risk.
Hypothesis H6 that “Perceived risk will have a positive effect on the intention to use online social
lending” is supported (Coeff. = 0.418, p < 0.01). The result suggests that users will have a higher
intention to use the innovation if they perceived as having little risk. The findings are consistent
with relationship suggested by TAM.
Hypothesis H7 that “Perceived attitude will have a positive effect on the intention to use online
social lending” is strongly supported (Coeff. = 0.768, p < 0.01). Such finding is consistent with
TAM. In plain words it means that to high extent people will be willing to use the product if their
attitude toward the new idea is positive.
Hypothesis H8 that “Subjective norms will have a positive effect on the intention to use online
social lending” is supported (Coeff. = 0.358, p < 0.01). It means that to some extent people will be
more willing to adopt the new idea if it is also valued positively by the rest of the society. The
finding is in line with what TAM suggests.
Hypothesis H9 that “Perceived relative advantage will have a positive effect on the intention to use
online social lending” is supported (Coeff. = 0.487, p < 0.01). As DOI model suggests, users will
be more willing to use the new product if it has a relative advantage over existing solutions. In our
case people saw a positive relative advantage compared with other solutions.
Hypothesis H10 that “Perceived compatibility will have a positive effect on the intention to use

online social lending” is supported (Coeff. = 0.552, p < 0.01). The result is in line with relationship
suggested by DOI model. Users have a higher intention to adopt the innovation when it is more
compatible with their existing habits and values.
Lastly hypothesis H11 that “Perceived observability will have a positive effect on the intention to
use online social lending” is accepted as well (Coeff. = 0.276, p = 0.009). It means people find it
important that they can immediately observe the outcomes of adopting the new idea. This is exactly
what DOI suggests.
In general, all of our hypothesis were accepted, most of them at very high probabilities (p < 0.01
levels), which implies that both TAM and DOI models work and are good at explaining intention to
adopt online social lending. However we still want to see which model is better therefore we
proceed with comparison in the next part.
5.4 Comparing TAM and DOI models
We have used several linear regressions to determine the amount of variance in independent
variables explaining the dependent one. By employing such technique we imply that some sort of
linear relationship exists in our data. If data is non-normally distributed or extreme outliers exist,
models will turn out useless in explaining it. However in our case data was normally distributed,
without severely outstanding outliers, therefore we believe linear regressions will be sufficient to
test the usability of our models.
5.4.1 Extended TAM model
Model R R2
Adjusted R2
Std. Error
1 .539(a) 0.290 0.286 1.402
2 .584(b) 0.341 0.334 1.355
3 .607(c) 0.368 0.357 1.331
4 .626(d) 0.392 0.378 1.310
5 .645(e) 0.416 0.399 1.287
a. Predictors: (Constant), PEU b. PEU, ATT c. PEU, ATT, PER d. PEU, ATT, PER, SEF
e. PEU, ATT, PER, SEF, SUN
Table 5.5 Stepwise regression analysis of extended TAM model
Model Beta t Sig.

1 PEU 0.539 8.486 0.000
2 PEU 0.409 5.772 0.000
ATT 0.260 3.675 0.000
3 PEU 0.380 5.401 0.000
ATT 0.246 3.524 0.001
PER 0.169 2.727 0.007
4 PEU 0.350 4.980 0.000
ATT 0.218 3.135 0.002
PER 0.174 2.850 0.005
SEF 0.161 2.586 0.011
5 PEU 0.308 4.360 0.000
ATT 0.159 2.210 0.028
PER 0.214 3.466 0.001
SEF 0.203 3.213 0.002
SUN 0.178 2.680 0.008
a. Dependent Variable: INT
Table 5.6 Explanatory power of variables in extended TAM model
To identify the most important variables we run a stepwise regression analysis. This multiple
regression technique lets isolate those independent variables that contribute most to variance of the
dependent variable. The analysis suggests five models, first where only perceived usefulness is used
as explanatory variable – Perceived usefulness. However this model explains only 29% (R2
= 0.290)
of the variance in Intention to use. The last model uses five variables to explain Intention to use
with a total R-squared of 0.416. This means adding another four variables explains additional
19.6% of the dependent variable variance. The fifth model indeed states that there are five
independent variables: Perceived usefulness, Perceived attitude, Perceived Risk, Self efficacy and
Subjective norm. In the table 5.6 you can see how those factors contribute.

5.4.2 Extended DOI model
Model R R2 Adjusted R2 Std. Error
1 .491(a) 0.241 0.237 1.450
a. Predictors: (Constant), COM
Table 5.7 Stepwise regression analysis of extended DOI model
Model Beta t Sig.
1 COM 0.552 7.475 0.000
Table 5.8 Explanatory power of variable in extended DOI model
The results for a stepwise regression analysis on extended DOI model independent variables are
such suggest a model with only one variable – Compatibility. The single variable is good at
explaining 24.1% of the dependent variable’s Intention to use variance.
5.4.3 Summary of model comparison
Multiple regression analysis revealed that the first model has more explanatory (0.416 > 0.241)
power over intenetion to use than the extended DOI model. We found following variables not
significant in explaining the dependent variable: Perceived ease of use, Relative advantage,
Observability.
5.5 Chapter summary
This chapter presented data analyses, and described the statistical tools we have employed to
analyze the collected data. We have tested data validity and reliability and afterwards ran simple
linear regressions to check if our initially proposed hypothesis can be accepted. All of the
hypotheses were accepted, implying that models are good at explaining survey results and users
intention to adopt online social lending. Afterwards we made a comparison of TAM and DOI
models. Stepwise regression revealed that an extend TAM model is better at explaining the
intention to adopt and use online social lending in Lithuania.
6. Conclusions
The concluding section discusses the results found in 5th chapter and compares it with the findings
of the similar previous studies. Further final conclusions of research are drawn after the
comparison. In the next step practical implications of research implications of online social lending
are presented. The closing part ends up with recommendations for the further studies.

6.1 Discussion
The key objectives of this research are:
• Identify factors influencing the intention to adopt online social lending in Lithuania;
• Examine which of the theories TAM or DOI explain more variance in intention to adopt online
social lending.
6.1.1 Extended TAM model performance
6.1.2 Extended DOI model performance
6.1.3 Model performance
6.2 Limitations
One of the major limitations of our work is
6.3 Contribution
Pasakojame kuo musu darbas prisidejo prie bendros kruvos ir kokie yra practical implications
6.4 Conclusions
Nu cia jau pilnai pilnai sumuojame viska. pabaiga

Appendixes
1. Works cited
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2.
Questionnaire
Internet social lending is where people access their borrowing/lending profiles (accounts) and
lend or borrow money using the internet. Internet social lending is an alternative to banks or any
other credit institutions. This model is similar to www.ebay.com where people buy and sell their
products. Here instead of products and services, people lend money.
This is an Stockholm School of Economics in Riga (SSER) survey designed to collect
information on attitudes towards new financial innovation - Internet social lending. The
information gathered will be used to build a better understanding of what influences people’s
intentions to use Internet social lending.
The average time to answer all 41 questions is 5- 8 minutes.
The survey questionnaire is posted: http://www.hollybaltija.lt/test/
When you enter the site, you will see the picture depicting the scheme how online social lending
works. After you click on the picture, you would be guided through flash-presentation
step-by-step to experience how to borrow money in social lending practically.
All the statements in the questionnaire are ranked from 1 to 7 having these meanings:
1. Strongly disagree
2. Disagree
3. Rather disagree than agree
4. Do not know
5. Can agree

6. Agree
7. Completely agree
Your response is anonymous. You also have the opportunity to request a copy of the survey
results. Thank you for participation in this survey
Queries regarding this survey can be directed to survey conductors:
Gediminas Šiuškus and Povilas Redko: gsiuskus@sseriga.edu.lv and predko@sseriga.edu.lv.
Stockholm School of Economics in Riga Degree committee has approved this research.
ALL RESPONSES ARE ANONYMOUS AND WILL BE TREATED IN CONFIDENCE
Statement Strongly disagree Strongly agree
ATTITUDE
I feel
comfortabl
e
borrowing/l
ending
money to
other
people
1 2 3 4 5 6 7
I feel
comfortabl
e
borrowing/l
ending
money to
people I
trust
1 2 3 4 5 6 7
I could
borrow/len
d to people
1 2 3 4 5 6 7

via internet
if it was
guarantee
d by a
bank
I could
borrow/len
d to people
via internet
1 2 3 4 5 6 7
INTENTIO
N TO
BORROW
I feel
comfortabl
e
borrowing
money
from
people
1 2 3 4 5 6 7
If such
possibility
existed, I
would feel
comfortabl
e
borrowing
money via
internet
1 2 3 4 5 6 7
If such
possibility
existed, I
would feel
comfortabl
e
borrowing
money via
internet
1 2 3 4 5 6 7
If there will
be 3ed
party
managem
ent
1 2 3 4 5 6 7

infrastructu
re then I
would be
very
interested
in lending
money
INTENTIO
N TO
LEND
I feel
comfortabl
e lending
money to
people
1 2 3 4 5 6 7
I feel
comfortabl
e lending
money to
people I
trust
1 2 3 4 5 6 7
I feel
comfortabl
e lending
money to
anybody if
the loan is
guarantee
d by a
Lithuanian
bank
1 2 3 4 5 6 7
PERCEIV
ED
USEFULN
ESS
Direct
online
lending
would let
me find
1 2 3 4 5 6 7

better
credit rates
than
typical
bank
Direct
online
borrowing
would give
me better
return than
bank
deposit
1 2 3 4 5 6 7
I find direct
online
lending
useful for
my needs
in the
future
1 2 3 4 5 6 7
PERCEIV
ED EASE
OF USE
I
understan
d how
online
lending/bor
rowing
works
1 2 3 4 5 6 7
I could be
confused
how to
lend/borro
w online
1 2 3 4 5 6 7
If this
service
would offer
a familiar
technology
(a familiar
internet
1 2 3 4 5 6 7

browser, a
familiar
internet
banking
service)
learning to
use it
would be
easy for
me
SUBJECTI
VE NORM
When it
comes to
lending/bor
rowing
people‘s
opinion
about me
is very
important
1 2 3 4 5 6 7
I would
trust online
lending/bor
rowing if
people I
trust use it
1 2 3 4 5 6 7
Owing
money to
the bank
and
person, I
would give
back first
to person
1 2 3 4 5 6 7
FACILITA
TING
CONDITIO
NS
1 2 3 4 5 6 7
I have 1 2 3 4 5 6 7

access to
internet
I know
how to use
internet
banking
service
(online
shopping)
1 2 3 4 5 6 7
I have
spare cash
to
invest/lend
to other
people
with return
1 2 3 4 5 6 7
SELF-EFF
ICACY
1 2 3 4 5 6 7
I feel good
when I
make
successful
investment
1 2 3 4 5 6 7
I feel good
when I can
save on
some
purchase
1 2 3 4 5 6 7
I feel good
when I can
use
technology
to my
advantage
1 2 3 4 5 6 7
I feel good
when I can
help
people in
need
1 2 3 4 5 6 7
I feel good 1 2 3 4 5 6 7

when I can
do
something
better than
my peers
I could use
direct
online
lending
with only
the online
help
function or
instruction
s for
assistance
1 2 3 4 5 6 7
Perceived
RISK
1 2 3 4 5 6 7
Direct
online
lending
lacks the
benefits of
personal
interaction
with
person
1 2 3 4 5 6 7
I can rely
on direct
online
lending to
work as
expected
1 2 3 4 5 6 7
Using
direct
online
lending
may
expose me
to fraud or
monetary
loss
1 2 3 4 5 6 7

Using
direct
online
lending
may
jeopardise
my privacy
1 2 3 4 5 6 7
To my
mind direct
online
lending
would be
insecure
1 2 3 4 5 6 7
1 2 3 4 5 6 7
RELATIVE
ADVANTA
GE
1 2 3 4 5 6 7
Direct
online
lending
looks more
convenient
than
arrange
the loan
directly
1 2 3 4 5 6 7
Direct
online
lending is
more
accessible
than
borrowing
from
people
1 2 3 4 5 6 7
Direct
online
lending is
less
time-consu
ming than
arranging
loans
1 2 3 4 5 6 7

directly
with
people
Direct
online
lending
gives me
greater
control
over my
finances
than
(visiting a
bank or
borrowing/l
ending
from other
people)
1 2 3 4 5 6 7
1 2 3 4 5 6 7
COMPATI
BILITY
1 2 3 4 5 6 7
Direct
online
lending is
compatible
with my
lifestyle
1 2 3 4 5 6 7
Using
direct
online
lending fits
well the
way I like
to mange
my
finances
1 2 3 4 5 6 7
OBSERVA
BILITY
1 2 3 4 5 6 7
The
advantage
s and
1 2 3 4 5 6 7

disadvanta
ges of
using
direct
online
lending are
obvious
I would
have
difficulty
explaining
why using
direct
online
lending
may or
may not be
beneficial
1 2 3 4 5 6 7
DEMOGR
APHICS
What is
your
gender?
What is
your age?
For how
long have
you used a
computer?
For how
long have
you used
the
Internet?
For how
long have
you used
Internet
banking?
How many
times have
you pay for

the
product or
service
over the
internet?

3. Summary of previous TAM usage in other researches
Reference Sample Field Factors and influence
(+)
Ahn, Tony, Seewon Ryu,
and Ingoo Han (2004)
932 internet users in
Korea (internet, 2003)
E-service
intention
Attitude toward using
E-service (+)
Perceived usefulness (+)
Perceived ease of use (+,
indirect)
System quality (+, indirect)
Information quality (+,
indirect)
Service quality (+, indirect)
Product quality and
delivery service (+,
indirect)
Chen and Tan
(2004)
253 email users in the
US (internet)
E-service
intention
Attitude toward using
E-service (+)
Perceived usefulness (+)
Perceived trust (+, indirect)
Compatibility (+, indirect)
indirect)
Perceived service quality
(+, indirect)
Product offerings (+,
indirect)
Usability of storefront (+,
indirect)
Childers et al.
(2001)
Study 1: 274 students
in a large US
Midwestern university,
(paper)
Study 2: 266 computer
users in the US (paper)
Attitude toward
E-service
Perceived usefulness (+,
+)
+)
Enjoyment (+, +)
Navigation (+ indirect, +
indirect)
Convenience (+ indirect, +
indirect)
Substitutability of personal
examination (+ indirect, +
indirect)
Gefen and
Straub (2000)
202 students in the
mid-Atlantic region,
USA
Intended purchase
and intended
inquiry
+)
Perceived ease of use (+
indirect, +)
Henderson
and Divett
247 individuals in
Auckland, New Zealand
The number of
log-ons, the number of
grocery
deliveries, and
purchase amount
Perceived usefulness (+, +,
+)
Perceived ease of use ((0,
+, +)
O’Cass and
Fenech (2003)
392 email users in
Australia (internet)
E-service
adoption
Attitude toward web retail
(+)
Opinion leadership and
impulsiveness (+, indirect)
Web experience (+,
indirect)

indirect)
indirect)
Shih (2004) 212 employees in
Taiwan (paper)
Acceptance of
online physical
products, online
digital products,
and online services;
E-service intentions
Attitudes (+, +, +)
User satisfaction (0, +, +)
Perceived information
quality (+, +, 0)
Perceived system quality
(0, +, 0)
Perceived service quality
(-, -, 0) Intention to use
web for information search
(+)
Attitudes (+)
Internet purchase
experience (+)
Perceived behavioral
control (+, indirect)
Van der
Heijden et al.
(2003)
228 students in a Dutch
academic institution
E-service
intention
Attitude toward online
purchasing (+)
Trust in online store (+,
indirect)
Perceived risk (-, indirect)
indirect)
Actual use
Intention to use
30.7%
Attitude towards use
Perceived ease of use
Trialability
29.5%
Perceived usefulness
Adoption
External variables
15 - 20 10.16%
31 - 54 9.38%
21 - 30 80.47%
Age, years

Gender
Male
48.44 %
Female 51.56%
Paper
85
Online 242
Observability
Relative advantage
Complexity
Compatibility
H11
H10
H9
Intention to use
Observability
Relative advantage
Ease of use
Compatibility
H8
H7
H6
H5
H4
H3

H2
H1
Perceived risk
Subjective norms
Perceived self-efficacy
Intention to use
Attitude towards use
Perceived ease of use
Perceived usefulness
Response
35.8%
4%
36.3%
48.2%
15.5%
Computer user, years
Internet user, years
10.6%
26.7%
20.5%
35%
24.3%
21.4%
12.2%
Items purchased online
E-banking user, years
7.2%

8.5%
33.6%

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