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User acceptance of Internet banking in an uncertain
and risky environment
Gang Liu1, Su-Ping Huang
1, Xin-Kai Zhu
2
School of Business1, School of Agricultural Economic and Rural Development
2
Renmin University of China, Beijing, P.R. China
E-mail: [email protected], [email protected], [email protected]
Abstract: Internet is expected to be especially beneficial to the banking industry. As more and more financial institutionsare finding ways to utilize Internet technologies to launch online banking services, an important issue is to understand whatfactors will impact the decisions of customers in adopting such services. Based on the UTAUT model, the D&M IS successmodel, and the concept of trust, this paper presents an integrated model to investigate user acceptance of Internet banking inan uncertain and risky environment. Empirically, we analyze an extensive dataset, a sample of 413 respondents; and theresults strongly support our model. A momentous conclusion is that user acceptance of Internet banking is highly influenced
by perceived risk and perceived uncertainty.
Keywords: Internet banking; user acceptance; perceived risk; perceived uncertainty, trust perception
1 INTRODUCTION
During the past two decades, the striking growth of the
Internet in China has attracted increasing attention. The total
number of Internet users in China had gotten to 210 million
with an annual growth rate of 53.3% by December 2007
(CNNIC, 2008). The potential to realize the concernment of
developing market through Internet has been well
recognized. In new economy, the Internet has become a
prevalent and powerful communication mechanism to
facilitate the consummation and processing of business
transactions. There is no doubt that Internet has turned intoone of the most important channels in commerce in recent
years. Consequently, companies dont only have to compete
fiercely in reality, but also on the Internet. Compared with
traditional channels, Internet is nearly benefited to every
entity involved in trade.
One of the most notable trends has been the growth in the
popularity of the application of Internet banking. The
banking industry is a very important part of modern
economy. With the increasing use of Internet, banks extend
and occupy market shares through online banking, which
also happened in China (Laforet and Li, 2005). Internet
banking refers to the use of Internet as a delivery channel
for banking services, which allows the customers to reach
them on a banks website without going to the physicalbranch. Turban et al. (2000) indicate that Internet banking is
extremely salutary to customers because of the savings in
costs, time and space it offers, its quick response to
complaints, and its delivery of improved services. However,
it is reported that only 19.2% of Internet users in China had
manipulated online banking by the end of 2007(CNNIC,
2008). Its such a helpful technology, but why there are so
few users? Some scholars figure that the ultimate question
about user acceptance may be more a function of users
belief and perception of the net value of the benefits and
costs.
Our research question is what factors will affect user
acceptance of Internet banking? This study proposes an
integrated model, based on technology acceptance literature,
to give a framework for analyzing the determinants of user
acceptance. In the following sections we outline a set of
hypotheses regarding the question. Data collected from the
business executives who were pursuing advanced degrees,
such as MBA and EMBA at universities are used to test
these hypotheses.
The structure of the paper is as follows. Section 2describes the background literature of this study. Section 3
sets out the theory and the empirical test design. Section 4
describes the method and data. Section 5 presents the
empirical analysis results, and section 6 concludes.
2 LITERATURE REVIEW
Much of the debate on user acceptance of new technology is
of a normative nature. As for the information technology
literature, individual acceptance of technology has given
birth to eight prominent modelsthe theory of reasoned
action, the technology acceptance model which believes that
usefulness and easy to use are the two determinants for anindividual to accept a new technology (Davis et al. 1989),
the motivational model, the theory of planned behavior, a
model combining TAM and TPB (Taylor and Todd 1995),
the model of PC utilization, the innovation diffusion theory,
and the social cognitive theory (Compeau et al. 1999),
which have been proposed and tested routinely in individual
intention to accept and adopt technology. Venkatesh et al.
(2003) developed a unified theory of acceptance and use of
technology (UTAUT) that can explain as much as 70
The 2008 International Conference on Risk Management & Engineering Management
978-0-7695-3402-2/08 $25.00 2008 IEEE
DOI 10.1109/ICRMEM.2008.82
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percent of the variance in intention study. The UTAUT
model postulates performance expectancy, effort
expectancy, and social influence as three significant direct
determinants of behavioral intention. These models propose
an aggregation of different determinants of technology
acceptance, even though some of them are related, others
are not. A mass of research about technology acceptance or
Information technology were built upon these models, andthe large body of empirical analyses is regard to investigate
the cause and effect relationships between the constructs,
such as between performance expectancy and behavioral
intention.
However, just using these models to study the effect
factors of user acceptance of internet banking is insufficient.
Basically, compared with conventional IT and the typical
business environment, the users of Internet banking take a
lot of risks (Chau and Lai 2003). Risks lead to low degree of
trust. While trust has been studied in depth in traditional
channels (Flavin et al. 2006), some scholars have
commenced to analyze the role of trust on B2C e-commerce
(Keen 1999; Gefen 2000). To some extent, whether users
are willing to adopt online banking depends on the degreesof their perceived risk and trust perception of the new
technology. At the same time, recruitment models suggest
that there are other attributes. Because of the particularity of
Internet banking, we suggest the addition of risk-related
constructs to our research model.
3 THEORY AND HYPOTHESES
This section describes the theory and hypotheses for the
proposed research model in detail. In our research, we
investigate the effect factors of user acceptance of Internet
banking in depth. Integrating some of the eight models,
especially TAM and D&M IS success model, we offer a
comprehensive understanding of how Internet banking
features essentially impact users attitude and conduct to
online banking. To illustrate the research model graphically,
we provide a plot of the figure, which present the
relationships between the variables needed to be examined.
Fig. 1 expounds our research model explored to observe and
study the antecedents of trust perception, the relationship
between trust perception and user satisfaction, and then
relate Internet banking site quality to user satisfaction and
ultimate adoption intention.
3.1 Self-efficacy
Previous research have done a great deal of studies on self-
efficacy (Johnson and Marakas, 2000; Hong et al., 2001),
which has been confirmed to be critical in understanding
individual responses to information technology. Self-
efficacy refers to an individuals belief that they have the
skills and abilities to accomplish a specific task successfully
(Bandura 1986).
Self-efficacy was found to have a direct influence on
system usage (Compeau et al., 1999). Bandura (1986)
indicates that self-efficacy have a direct impact on
performance expectancy. In recent IS studies, the
relationship between self-efficacy and effort expectancy is
proposed (Davis 1989). Igbaria and Iivari (1995) argue that
self-efficacy affects an individuals technology anxiety, and
then influences their performance expectancy. Therefore,
we assume:
H1: Higher self-efficacy leads to higher performanceexpectancy.
H2: Higher self-efficacy leads to higher effort expectancy.
H3: Higher self-efficacy leads to higher trust perception.
Note. SF = Self-efficacy; PR = perceived risk; C= Locus of
control; UC = perceived uncertainty; SQ = system quality; IQ =
information quality; VQ = service quality; PE = performance
expectancy; EE = effort expectancy; SI = social influence; TP =
trust perception; S = user satisfaction; BI = behavioral intention.
Figure 1 Research model
3.2 Trust perception
As a common research construct, trust has been widely
studied in several social science disciplines, including
sociology, social psychology, economics, consumer
behavior, organizational behavior, and most recently it
begins to emerge in research of e-commerce. Trust, as a
psychological state, is clearly distinct from behavior. Trust
perception in Internet banking is defined as an aggregation
of beliefs that allow a customer to willingly become
vulnerable to an Internet bank after having taken its
characteristics into consideration (McKnight and Chervany,
2002). All interactions require an element of trust,
especially those conducted in the uncertain environment
(Pavlou et al. 2007). Trust has a positive effect on an
individuals willingness to conduct transactions with an
online vendor.
Further, some researches showed that intention to use is
negatively affected by feelings of perceived risk. But a
users ability to use computer and internet is another
determinant, which is defined as locus of control. Kim et al.
(2003) show that perceived uncertainty and attitudes toward
PE
EE
SI
SQ
IQ
VQ
SF
PR
C
TP
UC
BI
S
H12H11
H6
H5
H4
H3
H10
H9
H14
H8
H7
H13
H1H2
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an IS are strong antecedents of a customers trust perception
in B2C e-commerce. Perceived uncertainty tends to erode
trust. User satisfaction, which is decompounded into the
categories of information satisfaction and system
satisfaction, has long been measured by assessing specific
characteristics inherent in the technology in question
(Delone 2003). We assumed that user satisfaction toward
Internet banking is related to consumer trust perception. Wehence suggest:
H4: Higher perceived risk leads to higher trust perception
to use Internet banking.
H5: Higher locus of control leads to higher trust
perception to use Internet banking.
H6: Higher perceived uncertainty leads to lower trust
perception to use Internet banking.
H7: Higher trust perception leads to higher user
satisfaction.
3.3 User satisfaction
User satisfaction is usually taken as the attitude that a user
has toward an IS. The original Delone and Malean (1992) ISsuccess model is an attempt to work over the nature of IS
success, which proposed to measure IS success from two
dimensions - system quality and information quality.
Nevertheless, certain researchers suggest adding a service
quality dimension to form the comprehensive IS
assessment framework. According to the updated D&M IS
success model (2003), system quality, information quality
and service quality singularly and jointly affect user
satisfaction. System quality scales the functionality of a web
site: usability, available and response time (Delone et al.
2003). Information quality expresses that content issues are
vital to e-commerce (Huizingh 2000). Service quality is
another important factor, ad hoc in the e-commerce
environment. Grounded on the aforementioned, we premise:H8: Higher system quality of the site leads to higher user
satisfaction of Internet banking.
H9: Higher information quality of the site leads to higher
user satisfaction of Internet banking.
H10: Higher service quality of the site leads to higher user
satisfaction of Internet banking.
H11: Higher user satisfaction leads to higher behavioral
intention of using Internet banking.
3.4 UTAUT model
In terms of the UTAUT model (Venkatesh et al., 2003), the
three constructs performance expectancy, effort
expectancy, and social influence jointly determine anindividuals intention to use an IT system. Performance
expectancy means the degree to which an individual
believes that using the system will help him or her to attain
gains in performance and productivity (Venkatesh et al.
2003), which is deemed as the strongest predictor of
intention in both voluntary and mandatory settings. Effort
expectancy is defined as the degree of ease in using the
system, which is composed of perceived ease of use,
complexity and ease of use. Social influence is defined as
the degree to which an individual perceives that important
others believe he or she should use the system, which is
considered as a direct determinant of behavioral intention.
In our model, we suppose:
H12: Higher performance expectancy leads to higher
behavioral intention to use Internet banking.
H13: Higher effort expectancy leads to higher behavioralintention to use Internet banking.
H14: Higher social influence leads to higher behavioral
intention to use Internet banking.
4 METHODOLOGY
4.1 Data collection
This research employed the questionnaire method to collect
primary data used in our study. A questionnaire was
developed to be the instrument for data collection.
Respondents were asked to identify the degree of agreementto some representations with regard to use of Internet
banking in China. Our measurement scale was made up with
items (see appendix) of several variables, including SF, PR,
C, UC, SQ, IQ, VQ, PE, EE, SI, TP, S and BI. Each item
was measured on a 5-point Likert-type scale, ranging from 1
(strongly disagree) to 5 (strongly agree).
4.2 Sample
The participants of the survey in the study were business
executives who were pursuing further education, such as
MBA and EMBA at universities. A total of 600
questionnaires were given out in the lump. After three
months, we received 413 effective responses, which got to69% of the gross. The majority of the respondents (352
respondents, 85%) of the 413 practicable questionnaires fell
into the age group of 25-45, which matches the target
consumer group of Internet banking. The distribution of
gender was quite balanced, with 219 of the participants
(53%) being male and the rest being female.
5 RESULTS
The measurement model used in our research has been
assessed for scale reliability and validity.
Reliability means dependability of scale tools while
representing the consistency and stability of scale results.
As shown in Table 1, the values of Cronbach of the
examined variables, which were introduced in the research
to test the reliability, are all higher than 0.7. Therewithal, we
assert that the questionnaire holds high reliability.
Validity is used to check the fitness of content in thequestionnaire by the numbers, meaning the subject-coveringextent of scale tools. As can be seen from Table 1, it isindicated that the model has convergent validity.
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Table 1 Related statistical values of factors and scales
FactorCronbach's
Items
Cronbach's
if Item
Deleted
Std.
Factor
loading
R2T-
value
BI 0.941
BI1 0.920 0.907 0.822
BI2 0.906 0.932 0.868 30.745
BI3 0.916 0.913 0.833 29.426
PE 0.909
PE1 0.902 0.758 0.575
PE2 0.894 0.811 0.658 17.09
PE3 0.891 0.829 0.688 17.531
PE4 0.884 0.828 0.685 17.487
PE5 0.897 0.760 0.577 15.852
PE6 0.890 0.796 0.634 16.73
EE 0.879
EE1 0.893 None None None
EE2 0.852 0.762 0.581
EE3 0.847 0.816 0.666 17.052
EE4 0.842 0.854 0.729 17.922
EE5 0.858 0.727 0.529 14.974
EE6 0.848 0.801 0.641 16.69
SI 0.815
SI1 0.753 0.844 0.713 19.864SI2 0.755 0.836 0.698 19.579
SI3 0.789 0.563 0.317 11.684
SI4 0.793 0.542 0.294 11.181
SI5 0.799 0.604 0.364 12.723
SF 0.866
SF1 0.853 0.734 0.538 16.646
SF2 0.847 0.638 0.407 13.824
SF3 0.832 0.749 0.561 17.139
SF4 0.831 0.772 0.597 17.906
SF5 0.845 0.734 0.539 16.656
SF6 0.848 0.625 0.390 13.466
TP 0.888
TP1 0.849 0.841 0.707
TP2 0.835 0.894 0.799 22.518
TP3 0.875 0.748 0.560 17.377TP4 0.861 0.775 0.600 18.27
PR 0.757
PR1 0.754 0.645 0.416 13.685
PR2 0.637 0.804 0.647 18.32
PR3 0.631 0.703 0.495 15.302
C 0.844
C1 0.799 0.763 0.582 17.324
C2 0.802 0.780 0.609 17.893
C3 0.819 0.686 0.470 14.989
C4 0.824 0.687 0.471 15.011
C5 0.818 0.716 0.512 15.87
UC 0.902
UC1 0.872 0.868 0.754 21.713
UC2 0.850 0.953 0.909 25.24
UC3 0.851 0.842 0.709 20.741
UC4 0.916 None None None
SQ 0.929
SQM1 0.925 0.629 0.396 13.887
SQM2 0.925 0.631 0.398 13.932
SQM3 0.925 0.630 0.396 13.9
SQM4 0.924 0.658 0.433 14.709
SQS1 0.926 0.673 0.453 15.137
SQS2 0.924 0.747 0.558 17.443
SQS3 0.923 0.753 0.567 17.655
SQS4 0.924 0.716 0.513 16.459
SQS5 0.924 0.714 0.509 16.376
SQS6 0.923 0.749 0.561 17.521
SQR1 0.923 0.673 0.453 15.152
SQR2 0.923 0.669 0.447 15.023
SQR3 0.923 0.681 0.463 15.374
SQR4 0.924 0.639 0.408 14.155SQR5 0.924 0.664 0.442 14.895
IQ 0.928
IQA1 0.920 0.779 0.607 18.591
IQA2 0.918 0.812 0.659 19.763
IQA3 0.919 0.812 0.659 19.756
IQA4 0.912 0.897 0.805 23.21
IQA5 0.915 0.866 0.750 21.88
IQR1 0.920 0.729 0.531 16.906
IQR2 0.925 0.662 0.438 14.872
IQR3 0.922 0.711 0.506 16.357
VQ 0.935
VQE1 0.935 None None None
VQE2 0.932 0.557 0.310 12.082
VQE3 0.931 0.575 0.331 12.558
VQE4 0.932 0.586 0.344 12.846
VQT1 0.926 0.887 0.787 22.899
VQT2 0.925 0.910 0.828 23.907
VQT3 0.926 0.898 0.806 23.357
VQT4 0.927 0.809 0.654 19.754
VQT5 0.931 0.694 0.482 15.917
VQT6 0.929 0.757 0.573 17.931
VQT7 0.928 0.765 0.585 18.189
VQT8 0.930 0.750 0.562 17.697
S 0.866
S1 0.852 0.593 0.352
S2 0.843 0.639 0.408 10.542
S3 0.833 0.797 0.635 12.251
S4 0.836 0.822 0.676 12.488
S5 0.822 0.856 0.734 12.778
Table 2 and 3 give the absolute and incremental fit indices
of the proposed model to determine model quality.
Parsimony fit indices of the proposed model are reported in
Table 4, which indicate a satisfactory model fit together
with the absolute and incremental fit indices reported.
The results of estimation the model are reported in Table 5.
It can be seen from Table 5, hypotheses H1 and H2 were
supported, indicating that self-efficacy had an obvious effect
on performance expectancy and effort expectancy of using
Internet banking. Trust perception was tested to be related to
perceived risk, locus of control and perceived uncertainty.
Thus, hypotheses H4, H5 and H6 were supported. The
results suggest a possible explanation for risks impact onuser of Internet banking. An increase in risk will lead to
lower trust perception to Internet banking. Furthermore, H7,
H8 and H10 were all tested to be supported. In addition,
H11, H12 and H14 were confirmed.
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Table 2 Absolute fit indices of the proposed model
Fit Indices Absolute
Name X2/df GFI RMSEA RMR SRMR AGFI
Value 3.316 0.546 0.0931 0.0656 0.0841 0.515
Table 3 Incremental fit indices of the proposed model
Fit Indices Incremental
Name CFI NNFI IFI
Value 0.941 0.939 0.941
Table 4 Parsimony fit indices of the proposed model
Fit Indices Parsimony
Name PGFI PNFI
Value 0.511 0.882
Table 5 Parameter estimates and hypotheses test
Hypotheses Std. loading T-value Conclusion
H1 SFPE 0.557 10.070 Supported
H2 SFEE 0.667 11.918 Supported
H3 SFTP 0.055 0.917 Not Supported
H4 PRTP 0.618 11.286 Supported
H5 CTP 0.161 2.499 Supported
H6 UCTP -0.244 -6.045 Supported
H7 TPS 0.207 3.984 Supported
H8 SQS 0.349 5.462 Supported
H9 IQS 0.029 0.466 Not Supported
H10 VQS 0.346 5.400 Supported
H11 SBI 0.137 2.698 Supported
H12 PEBI 0.521 9.574 Supported
H13 EEBI -0.029 -0.591 Not Supported
H14 SIBI 0.133 2.543 Supported
6 CONCLUSION
A primary motive of this study is to encourage future
researchers to pay more attention to deeply investigate the
risk of Internet banking and its effect on user acceptance.
We try to shape an integrated framework for analysing the
effect factors of user acceptance of Internet banking. Future
researchers attempting to identify predictors of user
acceptance of Internet banking may wish to avoid
considering it as a unitary construct. Hopefully, by
investigating risk and other determinants of user acceptance,
researchers will be able to advance knowledge and
understanding in this area.
APPENDIX
Table A.1. Measuring items used in this study
Construct Measure
Behavioral intention
BI1 I intend to use Internet banking in the next few months.
BI2 I predict that I would use Internet banking in the nextfew months.
BI3 I plan to use Internet banking in the next few months.Performance expectancy
PE1 I would find Internet banking useful in my daily life.PE2 Using Internet baking would enable me to conduct
transactions more quickly.
PE3 Using Internet banking would increase my productivity.PE4 Using Internet banking would improve my
performance.PE5 Using Internet banking would enhance my transaction
quality.
PE6 Using Internet banking would multiply my efficiency.Effort expectancy
EE1 I would find my interaction with the Internet clear andunderstandable.
EE2 Learning to use Internet banking would not take muchof my time.
EE3 I would find Internet banking easy to use.EE4 It would be easy for me to become skillful at using
Internet banking.EE5 I would find Internet banking to be flexible to interact
with.EE6 Working with the Internet is not complicated; it is easy
to understand what is going on.
Social influenceSI1 People who supervise me (e.g., leaders, teachers, etc)
think that I should use Internet banking.SI2 People who are important to me (e.g., family, friends,
etc) think that I should use Internet banking.SI3 The senior management of the bank has been helpful in
the use of Internet banking.
SI4 The bank has supported the use of Internet banking.SI5 People around me who use Internet banking have high
status and prestige.
Self-efficacySF1 I could complete a transaction using Internet banking if
there was no one around to tell me what to do as I go.SF2 I could complete a transaction using Internet banking if
I could call someone for help when I got stuck.SF3 I could complete a transaction using Internet banking ifI had enough time.
SF4 I could complete a transaction using Internet banking ifI had just the built-in help facility for assistance.
SF5 I could complete a transaction using Internet banking ifI had never used a system like it before.
SF6 I could complete a transaction using Internet banking ifsomeone showed me how to do it first.
Trust perception
TP1 It is easy for me to trust Internet banking system.TP2 My tendency to trust Internet banking is high.TP3 I tend to trust Internet banking, even though I have little
or no knowledge of it.
TP4 Trusting the Internet is not difficult for me.Perception risk
PR1 Conducting Internet banking transactions is not risky atall.
PR2 The decision to conduct Internet banking transactions isabsolutely correct.
PR3 Conducting Internet banking transactions leads to highgain.
Locus of controlC1 I dont need an experienced person nearby when I use
Internet banking.
C2 I can make the computer do what I want it to do.C3 I dont need someone to tell me the best way to use
Internet banking.C4 I feel confident about using the Internet to make my
financial t ransactions.C5 If I had a problem using the Internet, I could solve it
one way or another.
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Perceived uncertaintyUC1 I feel that using Internet banking involves a high degree
of uncertainty.UC2 I feel the uncertainty associated with Internet banking is
high.UC3 I am exposed to many transaction uncertainties if I use
Internet banking.UC4 There is a high degree of transaction uncertainty if I use
Internet banking.
System quality multimedia capabilitySQM1 Internet banking site uses audio elements properly.SQM2 Internet banking site uses video elements properly.SQM3 Internet banking site uses animations/graphics properly.SQM4 Internet banking site uses multimedia features properly.
System quality search facilitySQS1 There is a clear indication of Internet banking sites
content.
SQS2 Internet banking site has well-organized hyperlinks.SQS3 The structure of Internet banking site is logical to me.SQS4 Navigating Internet banking site is easy.SQS5 Internet banking site has explanation of how to use it.SQS6 I feel its easy to find information on Internet banking
site.
System quality responsiveness
SQR1 The response time of Internet banking site is proper.SQR2 The searching is fast on Internet banking site.SQR3 The searching time is reasonable.SQR4 The loading time is reasonable.SQR5 Internet banking site is responsive to my inquiries.
Information quality information accuracyIQA1 Internet banking site provides useful information.IQA2 Internet banking site provides accurate information.IQA3 Internet banking site provides updated information.IQA4 Internet banking site provides high quality information.IQA5 Internet banking site provides timely information.
Information quality information relevanceIQR1 The information on Internet banking site is relevant to
me.
IQR2 I can find what I need on Internet banking site.IQR3 Internet banking site provides relevant information.
Service quality empathy
VQE1 Internet banking site has interactive feedbackmechanism.
VQE2 Internet banking site has personalized information.VQE3 Internet banking site has empathy with customers
problems.VQE4 Internet banking site is very concerned about my
welfare.
Service quality trustVQT1 I feel safe when I use Internet banking site.VQT2 Internet banking site is secure.VQT3 Internet banking site is reliable.VQT4 I believe that Internet banking site will not misuse my
personal information.
VQT5 Internet banking site conveys a sense of competencies.VQT6 Internet banking site satisfies ethics standards.VQT7 Internet banking site is sure to solve my problem.VQT8 I feel very confident about Internet banking site.
Satisfaction
S1 I will return to Internet banking site.S2 I will continue to use Internet banking to conduct
transactions.S3 I feel satisfied with Internet banking site in regarding to
other web sites.
S4 Internet banking services live up to my expectations.S5 In general, I feel satisfied with Internet banking
services.
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