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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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