Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Effect of Trust on Customer Intention to use
Electronic Banking in Vietnam
Long Nguyen1, Duc Tho Nguyen2 and Tarlok Singh3
Vietnam is a developing country with a population of approximately over 90
million (11/2013). The availability of electronic banking (e-banking) over ten years
ago in Vietnam marked a significant development for society in general and for
banking in particular. Research attention has so far focused on the development
and implementation of e-banking applications in Vietnam. However, there is at
present very little research about customer trust in Vietnamese e-banking. Most
of research about e-banking in Vietnam focuses on the adoption of e-banking.
This paper, part of an investigation of critical factors affecting customer trust in ebanking, explores the effect of trust on aspects of customer intention to use ebanking in Vietnam. The proposed research model integrates constructs from
other disciplines, such as psychology, sociology, and electronic commerce. The
basic model for this study has been adopted from the Technology Acceptance
Model (TAM) to show the characteristic of e-banking, including the addition of
another belief, trust, to increase the understanding of customer intention to use ebanking in Vietnam. A Structural Equation Modeling (SEM) approach has been
used to evaluate the research model. This study begins to fill in the gap noted in
the literature by providing a model for the effect of trust on customer intention to
use e-banking. The study’s findings offer help for Vietnamese banks, policy
makers and customers to clarify and develop the effect of trust on customer
intention in using these e-banking services.
Keywords: customers’ intention, e-banking, trust, TAM
1. Introduction
Many banks around the world have launched their e-banking to provide customers with
more convenient ways to access banking information and services. Previous research has
been carried out to evaluate the quality and quantity of the e-banking services provided, as
well as the overall adoption of e-banking. The results and findings of this research differed,
based on many factors such as the level of development of the particular country, its
national culture, the customers’ knowledge of e-banking and the infrastructure of
information technology. In Vietnam, e-banking research focuses on the adoption model,
the drivers of customer intention to use e-banking, and the use of e-payment. None of this
research studied customer trust in e-banking, even though trust plays an important role in
e-commerce adoption, especially e-banking transactions, and trust is one of the most
significant factors in customer acceptance of e-banking (Suh and Han, 2002). Most of the
existing literature about trust in e-banking assumes trust to be a factor affecting customer
acceptance of e-banking (e.g. Suh and Han, 2002; Alsajjan and Dennis, 2006; Kassim and
Abdulla, 2006; Benamati and Serva, 2007; Grabner-Kräuter and Faullant, 2008; AldásManzano et al. 2009; AbdullahAl-Somali et al. 2009; Muñoz-Leiva et al. 2010; Dixit and
Datta, 2010; Khalil Md Nor et al. 2010; and Anita Lifen Zhao et al. 2010). In addition, other
studies investigated factors affecting the adoption and usage of e-banking in general and
1
Long Nguyen, Department of Accounting, Finance and Economics, Griffith University, Australia. Email: l.nguyen@griffith.edu.au
2
Duc Tho Nguyen, Department of Accounting, Finance and Economics, Griffith University, Australia. Email: t.nguyen@griffith.edu.au
3
Tarlok Singh, Department of Accounting, Finance and Economics, Griffith University, Australia. Email: tarlok.singh@griffith.edu.au
1
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
internet banking in particular (e.g. Sathye, 1999; Tan and Teo, 2000; Wan et al. 2005;
Chiemeke et al. 2006; Ndubisi and Sinti, 2006; Wang and Pho, 2009; and Alain Yee-Loong
Chong et al. 2010). These studies concluded that many factors affect the acceptance and
usage of e-banking, including education, technology acceptance, security, risk, legal
support, trust, demographic characteristics.
The main aim of this study is to identify the effect of trust on customer intention to use ebanking in Vietnam. The basic model for this study has been adopted from the Technology
Acceptance Model (TAM) (Davis, 1989) to show the characteristic of e-banking. The TAM
model is an integrated construct from disciplines such as psychology, sociology, and
electronic commerce. Another belief, trust, is added to the TAM to increase the
understanding of customer intention to use e-banking in Vietnam. A Structural Equation
Modeling (SEM) approach is used to evaluate the research model. This study differs from
the previous studies in that it conducts a comprehensive primary survey to collect data to
be used in the model. The survey encompasses selected provinces in northern, central,
and southern of Vietnam. This paper is organized as follows. Section 2 provides the
research background and discusses the Technology Acceptance Model and trust in ebanking. Section 3 outlines the study’s model and sets the hypotheses. The research
methodology and data used in the study are discussed in Section 4. Section 5 presents
empirical results and Section 6 gives the conclusions.
2. Research background
As noted, this study adds another belief, trust, to the TAM to find out the effect of trust on
customer intention to use e-banking in Vietnam. This section outlines how TAM applies to
e-banking as the basic model in explaining the customers’ acceptance of technology. Then
the definition, the literature of trust in using e-banking and the role of customers’ trust are
discussed.
2.1 Technology Acceptance Model
The TAM is a model developed by Davis (1989) and Davis et al. (1989) to explain why
users accept or reject information technology (Figure 1). It is based on Ajzen and
Fishbein’s Theory of Reasoned Action (TRA) model, a general model that suggests an
individual’s social behaviour is motivated by his/her attitude towards the behaviour. This
theory is based on assumptions that human beings are usually quite rational and make
systematic use of the information available to them (Ajzen and Fishbein, 1980). TRA is
concerned with the determinants of intended behaviours, saying that a person’s intentions
are a function of two basic determinants, one which is personal in nature (attitudes) and
the other which reflecting social influence (social or subjective norms). The major
application of this theory is in the prediction of behavioural intention, including predictions
of attitude and predictions of behaviour. The subsequent separation of behaviour intention
from actual behaviour allows some explanation of the limiting factors on attitudinal
influence (Ajzen and Fishbein, 1980).
The TAM suggests that perceived usefulness (PU) and perceived ease of use (PEOU) are
the primary relevance for technology acceptance behaviour. PU is defined as the degree
to which a prospective user believes that using a particular system would enhance his or
her job performance. PEOU is defined as the degree to which a prospective user believes
that using a particular system would be free of effort (Davis, 1989). Adopted from the TRA
model, the TAM shows that these two beliefs (PU and PEOU) specify first the attitude
2
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
towards using information systems, then the attitude towards using determining the
behavioural intention to use; lastly, the behavioural intention to use leads to actual use
(Suh and Han, 2002). The TAM does not include social norms (SN) as a determinant of
behavioural intention. This model has been widely applied for predicting the acceptance of
information technology; its validity has been demonstrated across a wide variety of
information technology systems (Plouffe et al., 2001).
Figure 1: Technology Acceptance Model
External
variables
Perceived
Usefulness (PU)
Perceived Ease of
use (PEOU)
Attitude
towards
Using (ATU)
Behavioural
Intention to
use (BI)
Actual Use
The TAM explains the causal linkages between these two beliefs (perceived usefulness
and perceived ease of use of the information system) and users’ attitudes, intentions and
actual computer adoption behaviour (Davis et al., 1989). It suggests that perceived ease of
use (PEOU) and perceived usefulness (PU) are the two most important factors in
explaining user acceptance of using the information system.
2.2 Trust in e-banking
Trust is a very complex construct and it is multidimensional (Gefen 2000; Hoy & Tarter,
2004; Smith & Birney, 2005; Mcknight et al., 2002; Mayer et al., 1995). Various definitions
of trust depend on the different research areas. It is “believing in the honesty and reliability
of others” (World Reference, 2005). Trust is also the willingness to rely (Doney and
Cannon, 1997) and is a positive form of behaviour to others (Whitener et al., 1998).
According to Yousafzai et al. (2003), trust is “the belief that a party’s word or promise is
reliable and a party will fulfil his/her obligations in an exchange relationship”. Trust occurs
“when one party has confidence in an exchange partner’s reliability and integrity” (Morgan
and Hunt, 1994). Trust is “the willingness of a party to be vulnerable to the actions of
another party, based on the expectation that the other will perform a particular action
important to the trustor, irrespective of the ability to monitor or control that other party”
(Mayer et al., 1995). These definitions of trust are applicable to the relationship between
(at least) two parties – a trustor and a trustee; the object of trust is another person or a
group of persons (Grabner-Kr.autera and Kaluscha, 2003). Trust in an online vendor is the
“willingness to make oneself vulnerable to actions taken by the trusted party based on the
feeling of confidence and assurance” (Gefen, 2000). Trust is “the belief that the promise of
another can be relied upon and that, in unforeseen circumstances, the other will act in a
spirit of good will and in a benign fashion towards the trustor” (Suh and Han, 2002). Trust
is “the subjective assessment of one party that another party will perform a particular
transaction according to his or her confidant expectation, in an environment characterized
by uncertainty” (Ba and Pavlou, 2002). The most popular definition of trust is the following:
“Trust is a psychological state comprising the intention to accept vulnerability based upon
positive expectations of the intention or behaviour of another under conditions of risk and
interdependence” (Rousseau et al., 1998).
Customers’ trust in their online transactions is very important and has been identified as a
key to the development of e-commerce (Yousafzai et al., 2003). A key reason for focusing
3
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
on the importance of trust in e-commerce in general and in e-banking in particular is the
fact that in a virtual environment the level of risk in an economic transaction is higher than
it is in traditional settings (Grabner-Kr.autera and Kaluscha, 2003). The uncertainty that
challenges online customers is because suppliers on the Internet are inevitably
independent and unpredictable, whereas in transactions there is a need for customers to
understand the suppliers’ actions. If customer uncertainty is not reduced, transactions
between online customers and suppliers will not be performed. Trust is one of the most
effective uncertainty reduction methods (Gefen, 2000). In the Internet environment, users
from everywhere are able to access files online and information is transferred via Internet.
Therefore e-banking is risky from the viewpoint of security.
E-banking seems highly uncertain because users involved in a transaction are not in the
same place (Ratnasingham, 1998). Customers cannot tell or observe a teller’s behaviour
directly, thereby increasing the uncertainty. Due to this, customer trust is a major factor
influencing the development of e-banking. Researchers have empirically indicated that
customer trust plays a very important role in e-banking website loyalty, which can be
defined as a customer’s strong desire to keep a valued relationship with a bank (Macintosh
and Lockshin, 1997). Numerous research studies have identified lack of trust as one of the
main reasons why customers are still reluctant to conduct their financial transactions
online (e.g. Wong et al., 2009). In order to use e-banking in real life as a viable medium
financial service delivery, banks must try to fill the trust gap created due to the higher
degree of uncertainty and risk in an online banking environment compared to the
environment of traditional transactions.
Many studies conducted examining the role of trust in e-banking (e.g. Suh and Han, 2002;
Casaló et al., 2007; Lichtenstein and Williamson, 2006) found that trust plays a very
important role in the adoption and continued use of e-banking. Moreover, not only does
affect the intention of using e-banking (Suh and Han, 2002; Liu and Wu, 2007), but trust in
e-banking has also been found to be an antecedent in the e-banking environment (Kassim
and Abdulla, 2006; Vatanasombut et al., 2008), so that trust can reduce perceived risk in
an online environment. Online banking transactions require collection of very sensitive
information about customers (Gefen, 2000; Morgan and Hunt, 1994). Customers always
fear to disclose their privacy and financial information on the internet, because of security
problem and distrust of e-banking providers (Suh and Han, 2000). The role of trust is very
important when e-banking providers publicize their services (Palmer and Bejou, 1994).
Thus, trust has a significant effect on the customer intention of using e-banking (Alsajjan
and Dennis, 2006). The requirement of trust is more important in the virtual environment
than in the real environment (Ratnasingham, 1998).
3. Model and the Hypotheses setting
3.1 The model
The research model for this study investigates the effect of trust on customer intention of
using e-banking by adding this additional belief, trust, to the original TAM. This research
model is adopted from Davis (1989) and Suh and Han (2002).
E-banking has many advantages, when compared with traditional banking methods. It
provides enormous benefits to banks, to customers and to economies. E-banking also has
disadvantages that bring many challenges for banks, such as security and privacy, cost
and risk. These disadvantages raise the important question: how to develop a safe
4
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
environment for e-banking transactions-especially regarding customer trust in using ebanking. Solving this problem is even more critical for e-banking, as any payment or
deposit necessarily uses a virtual savings account (Muñoz-Leiva et al., 2010).
Figure 2: Model and the hypotheses
H7
Perceived
Usefulness (PU)
External
variable
s
H3
H5
H9
Perceived Ease
of use (PEOU)
H6
Attitude
towards
Using
(ATU)
H8
Behavioural
Intention to
use (BI)
H2
H4
Trust (TEB)
H1
3.2 Hypotheses setting
Researchers in marketing areas have empirically tested the causal relationship between
trust and behavioural intention. Alain Yee-Loong Chong et al. (2010) found that trust will
affect the intention to adopt internet banking, as without private security and privacy
protection, customers will not use online banking services. Suh and Han (2002) concluded
that trust is one of the most significant beliefs in explaining customers’ behavioural
intention to use internet banking. Doney and Cannon (1997) found that customer trust
related to their intention to use the vendor in the future. Gefen (2000) showed that trust
issues play an important role in increasing customers’ intention to use the e-vendor’s
website. This leads to hypothesis 1 as follows:
Hypothesis 1 (H1): Trust has a positive effect on the behavioural intention to use ebanking.
In the marketing areas, many studies have found that trust has an effect on attitude.
Macintosh and Lockshin (1997) concluded that customers’ store trust is positively related
to store attitude. Store attitude was considered as one of the components of store loyalty.
Suh and Han (2003) suggested that trust has a positive impact on customer attitude
towards using e-commerce for trade transactions. Wang (2010) also showed that
customers’ perceived trust towards mobile phone advertisement enhanced their attitudes
towards advertisement in a mobile phone company. Limbu et al. (2012) suggested that
customer trust in online retailer websites positively influence the customers’ attitudes
towards the online retailers’ websites. This leads to hypothesis 2 as follows:
Hypothesis 2 (H2): Trust has a positive impact on the attitude towards using e-banking.
According to Davis (1989), the TAM suggested that perceived usefulness is one of two
beliefs (perceived usefulness and perceived ease of use) that are most relevant to
5
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
information systems acceptance behaviours. In this study, another belief, trust, was added
into the TAM to verify its effect on customer intention to use e-banking. Prior studies using
the TAM variables such as Suh and Han (2002), Koufaris and Hampton-Sosa (2004) have
shown that perceived usefulness had a significant impact on trust, so we expect that
perceived usefulness will have strong positive effect on trust in e-banking. This leads to
hypothesis 3 as follows:
Hypothesis 3 (H3): Perceived usefulness has a positive impact on trust in e-banking.
We also test the following TAM related hypotheses in the e-banking environment because
this research model is based on the TAM.
Hypothesis 4 (H4): Perceived usefulness has a positive impact on the attitude towards
using e-banking.
Hypothesis 5 (H5): Perceived usefulness has a positive impact on the behavioural
intention to use e-banking.
Hypothesis 6 (H6): Perceived ease of use has a positive impact on trust in e-banking.
Hypothesis 7 (H7): Perceived ease of use has a positive impact on the attitude towards
using e-banking.
Hypothesis 8 (H8): Perceived ease of use has a positive impact on perceived usefulness.
Hypothesis 9 (H9): Attitude towards using e-banking has a positive impact on the
behavioural intention to use this form of banking.
4. Research methodology and the Data
4.1. Research methodology
The research model is estimated using Structural Equation Modeling (SEM), a family
technique that is used to analyze and empirically explain the relationships among
constructs (Hair, 2009; Kline, 2010). According to Hair (2009), the focus of SEM as an
analysis technique is the covariance and correlation parameters between the constructs, a
distinguishing characteristic of SEM analysis techniques (Byrne, 2013). This approach is
chosen because of its ability to test causal relationships between constructs with multiple
measurement items (Jöreskog and Sörbom, 1993), and because it has the capability of
testing the measurement characteristics of constructs (Hair, 2009). The SEM approach
offers several advantages over the conventional regression approach in this context,
basically providing greater facility in handling multicollinearity, inherent errors in measuring
independent variables, and estimation of parameters across a system of simultaneous
equations (Titman and Wessels, 1988; Hoyle, 1995; Hair, 2009; Chang et al., 2009). The
models are then evaluated by the maximum likelihood method using AMOS software
distributed by SPSS software version 20.
4.2. Field Survey Design and the Data collection
This study uses primary data collected from the questionnaire survey in selected provinces
in northern, central, and in southern of Vietnam to test the hypotheses. The questionnaire
was presented to participants in two ways: either as hard-copy questions directly in bank
customer
meetings
or
as
a
link
to
the
Web
survey
site
(https://prodsurvey.rcs.griffith.edu.au/prodls190/index.php?sid=63492&lang=en) sent by
email to bank customers.
In total, there were 557 responses; 93 out of these samples were not used because there
were missing values. The rest, 464 samples, were gathered and eligible for data analysis
(178 samples supported via the Web survey; 286 samples collected via bank customer
6
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
meetings). Direct delivery of the survey questionnaire and sending via email addresses to
participants were preferred rather than using postal surveys because the postal service in
Vietnam is not reliable and the use of online surveys would exclude non-adopters of ebanking services. There are no missing data in the sample because online participants
could not submit their online responses with missing values via the Web survey and all
hard copies of the questionnaire survey with missing values were deleted before importing
data into the computer. Table 1 summarizes the demographic characteristics of the
respondents.
Table 1: Descriptive statistics of respondent’s characteristic
Respondents
characteristics
Gender
Age
Education
Occupation
Income (in
million VND)
Frequency use
the Internet
First visit ebanking website
Value
Male
Female
Under 20
21-30
31-40
41-50
51-60
Above 60
High school
College Diploma
Bachelor
Master
Doctorate
Other
Government employee
Private employee
Student
Other
Less than 5
6-10
11-20
21-30
31-50
More than 50
Once a month
Once a week
Between 2 and 5 times a week
Daily
Other
Recently (within the last 6 months)
More than six month but less than a year
More than one year but less than three years
More than three years ago
Other
Number of
respondents (n=464)
Percentage
(%)
202
262
0
177
201
56
28
2
9
69
267
88
2
29
137
146
5
176
144
202
73
25
10
10
4
10
27
422
1
210
34
76
140
4
43.53
56.47
0
38.15
43.32
12.07
6.03
0.43
1.94
14.87
57.54
18.97
0.43
6.25
29.53
31.47
1.08
37.93
31.03
43.53
15.73
5.39
2.16
2.16
0.86
2.16
5.82
90.95
0.22
45.26
7.33
16.38
30.17
0.86
4.3. Field Survey and the Measurement Scale
Measurement items used in this study were either adapted from previously validated
measures or developed based on the literature review. A seven-point likert scale ranging
from (1) “strongly disagree” to (7) “strongly agree” was used to assess responses. Items
from previous studies were modified for adaptation to the e-banking context. The final
questionnaire items used to measure each construct are presented in Table 2.
7
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 2: Summary of measurement scales used in the field survey
Items Constructs
1
2
PU2
3
4
PU3
Perceived
usefulness
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
PU1
PU4
PU5
PU6
PU7
PU8
PEOU1
PEOU2
Perceived
PEOU3
ease of use PEOU4
PEOU5
PEOU6
TEB1
TEB2
Trust
TEB3
TEB4
TEB5
ATU2
Attitude
ATU3
towards
ATU4
using
ATU5
BI1
Behavioural
BI2
intention to
BI3
use
BI4
Measures
Using e-banking improves my performance of utilizing
banking service.
E-banking can enhance the effectiveness of customers’
transactions with bank
Using e-banking services enables me to utilize banking
service more quickly.
Using e-banking for my banking service increases my
productivity.
I find e-banking useful for my banking activities
Using e-banking makes it easier to do my banking
activities
Using e-banking can reduce queuing time
Using e-banking can cut travelling expenses
It is easy for me to become skillful at using the e-banking
I find e-banking easy to do what I want to do
It is easy for me to learn how to use e-banking
I find e-banking to be flexible to interact with
My interaction with e-banking is clear and understandable
Overall, I find e-banking easy to use
E-banking is trustworthy
I trust in the benefits of the decisions of using e-banking
E-banking keeps its promises and commitments
E-banking would do the job right even if not monitored
I trust e-banking
Using e-banking is a wise idea
Using e-banking is a pleasant idea
Using e-banking is a positive idea
Using e-banking is an appealing idea
I intend to continue using e-banking in the future
I expect my use of e-banking to continue in the future
I will frequently use e-banking in the future
I will strongly recommend others to use e-banking
Notes: PU: Perceived Usefulness, PEOU: Perceived ease of use, TEB: Trust, ATU: Attitude
Towards Using and BI: Behavioural Intention to use.
5. Empirical results
5.1 Exploratory factor analysis
Exploratory factor analysis (EFA) is a tool for assessing the factors that underlie a set of
variables. It is frequently used to assess which items should be grouped together to form a
scale and to reduce a large number of variables (items or indicators) into a smaller,
manageable set of factors (Gerbing and Anderson, 1988; Hair, 2009). EFA is useful to
detect the presence of meaningful patterns among the original variables and for extracting
the main service factors (Lu et al., 2007). All items of the questionnaire were subjected to
principal component analysis (PCA), an extraction method used to determine factors
needed to represent the structure of the variables, using SPSS software version 20. The
suitability of data for factor analysis was checked before the PCA was performed. Firstly,
almost of each of all items was correlated (ρ≥0.3) with at least one other item, indicating
reasonable factor analysis. Secondly, result of the Kaiser-Meyer-Olkin Measure of
Sampling Adequacy (KMO) and the Barlett’s Test of Sphericity are shown in Table 3.
8
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
According to Tabachnick and Fidell (2007), KMO value is 0.6 or above and the Barlett’s
Test of Sphericity value is significant, which should be 0.05 or smaller.
It can be clearly seen that the KMO value is 0.945, and the Bartlett’s test is significant (p =
0.000) (Table 3). Finally, the communalities were all greater than 0.5 (see Table 4),
providing additional evidence that each item shared some common variance with other
items. Therefore, factor analysis is appropriate.
Table 3: Kaiser-Meyer-Olkin and Bartlett's Test
Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy.
0.945
Approx. Chi-Square
Bartlett's Test of Sphericity
df
Sig.
10650.83
351
0.000
The principal component analysis revealed the presence of five components with
eigenvalues exceeding 1.0, explaining 48.89%, 9.51%, 7.10%, 4.80%, and 3.82% of the
variance respectively (Table 4). In conclusion, the five–component solution explained a
total of 74.13% of the variance.
Table 4: Total Variance Explained and Communalities
Extraction Sums of Squared
Loadings
Commu
% of
Cumulative
% of
Cumulative nalities
Total
Total
Variance
%
Variance
%
1
13.2
48.891
48.891
13.2
48.891
48.891
0.711
2 2.567
9.507
58.398
2.567
9.507
58.398
0.816
3 1.918
7.105
65.503
1.918
7.105
65.503
0.778
4 1.298
4.806
70.309
1.298
4.806
70.309
0.682
5 1.031
3.817
74.126
1.031
3.817
74.126
0.775
6 0.658
2.438
76.564
0.800
7 0.633
2.344
78.908
0.551
8 0.585
2.165
81.072
0.683
9 0.563
2.085
83.158
0.759
10 0.412
1.526
84.684
0.743
11
0.4
1.483
86.168
0.767
12 0.382
1.416
87.584
0.776
13 0.339
1.255
88.839
0.701
14 0.327
1.212
90.051
0.656
15 0.308
1.14
91.191
0.813
16 0.292
1.083
92.274
0.689
17 0.259
0.959
93.233
0.730
18 0.242
0.898
94.13
0.527
19 0.234
0.866
94.996
0.799
20 0.215
0.797
95.793
0.759
21
0.21
0.777
96.57
0.833
22 0.188
0.696
97.267
0.772
23 0.179
0.663
97.93
0.856
24 0.164
0.607
98.537
0.811
25 0.144
0.535
99.072
0.830
26 0.132
0.488
99.56
0.825
27 0.119
0.44
100
0.572
Extraction Method: Principal Component Analysis.
Comp
onent
Initial Eigenvalues
9
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
The Pattern Matrix of the first factor analysis shows that all items have a factor loading
greater than 0.5 while according to Hair and Black (2010) and Pallant (2007) the minimum
suggested is 0.3 (Table 5). Thus, all items are valid to form five factors (latent variables):
Perceived Usefulness, Perceived Ease of Use, Trust, Attitude towards Using, and
Behavioural Intention to Use.
Table 5: Pattern Matrix
Component
Variable
PU5
PU6
PU4
PU3
PU7
PU8
PU2
PU1
PEOU3
PEOU2
PEOU1
PEOU6
PEOU5
PEOU4
TEB1
TEB5
TEB3
TEB2
TEB4
ATU5
ATU3
ATU2
ATU4
BI1
BI2
BI3
BI4
Perceived
Usefulness
Perceived
Ease Of
Use
0.931
0.887
0.865
0.819
0.805
0.772
0.737
0.595
-0.058
0.017
-0.055
0.073
0.136
0.119
-0.047
-0.067
-0.027
0.177
0.043
0.033
0.01
0.101
-0.115
0.059
0.032
0.01
-0.072
-0.032
-0.006
-0.041
0.083
0.066
0.035
0.007
0.068
0.953
0.903
0.857
0.813
0.725
0.715
-0.034
0.028
0.014
-0.072
0.136
-0.018
-0.029
-0.06
0.147
0.017
0.025
0.028
-0.11
Trust
0.013
0.064
0.065
0.006
-0.099
-0.139
0.071
0.001
-0.086
0.005
-0.005
0.079
0.108
0.046
0.937
0.904
0.853
0.68
0.68
0.025
0.003
0.006
-0.069
-0.049
-0.058
-0.008
0.275
Attitude Behavioural
Towards Intention To
Using
Use
0.001
0.034
0.082
0.024
-0.076
-0.085
0.016
0.066
0.079
-0.016
-0.035
-0.012
-0.041
0.003
-0.039
-0.061
0.06
-0.023
0.051
0.959
0.94
0.761
0.752
-0.078
0.031
0.119
0.083
-0.075
-0.103
-0.104
-0.042
0.127
0.203
0.057
0.093
-0.063
-0.016
0.085
-0.016
0.026
-0.004
0.049
0.083
-0.042
0.156
-0.192
-0.092
-0.037
0.109
0.219
0.927
0.887
0.813
0.627
Notes: (1) Extraction Method: Principal Component Analysis, (2)
Rotation Method: Promax with Kaiser Normalization, (3) a. Rotation
converged in 6 iterations.
5.2 Confirmatory factor analysis
Following the result of the exploratory factor analysis, five factors were extracted from five
groups of 27 items. Anderson and Gerbing (1988), Lomax and Schumacker (2012), and
Hair and Black (2010) suggested that the data analysis employed a two-phase approach in
order to evaluate the reliability and validity of the measures before using them in the
10
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
research model. The first phase analyses the measurement model; the second phase
tests the structural relationships among latent constructs. The test of the measurement
model, which indicates the strength measures used to test the research model, involves
the estimation of internal consistency reliability as well as the convergent and discriminate
validity of the research instruments (Fronell, 1982).
The structural equation models were then examined for hypotheses testing. Both the
measurement models and the structural models were evaluated by the maximum
likelihood method using AMOS 20 software, distributed by SPSS software version 20.
Internal consistency reliability is a statement about the stability of individual measurement
items across replications from the same source of information (Straub, 1989). Internal
consistency was evaluated by computing Cronbach’s alpha. The alpha coefficients for
each construct of this study are presented in Table 6. Hair (2010) suggested that the
lowest limit for Cronbach’s alpha be 0.70, while Straub (1989) suggested 0.80 as the limit.
All constructs in the research model have acceptable reliability because the construct with
the lowest alpha coefficient showed marginally satisfactory reliability.
The corrected item-total correlation values are given in Table 6. These values show the
correlation between individual items and the total score (Pallant, 2007; Tabachnick and
Field, 2007). If the Corrected item-total correlation value is below 0.3, this mean that this
item measures another characteristic other than the overall characteristics of the group of
items. In this study all corrected item-total correlation values are above 0.5. The alpha
score if each item is deleted is also provided in Table 6. If deleting an item results in an
increase in the alpha score, then the item should be removed (Pallant, 2007). Two items fit
this classification: TEB4 in Trust in e-banking (from 0.886 to 0.900) and BI4 in Behavioural
intention to use (from 0.879 to 0.912) (Table 6). As the differences in these cases are
rather small, these items were not deleted. Therefore, it can be concluded that the levels
of internal consistency among the five groups of items were different but they were valid
for confirmatory factor analysis (CFA).
11
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 6: Cronbach’s alpha for internal consistency checking
Factor
Perceived
Usefulness
Perceived
Ease of
Use
Trust in ebanking
Attitude
towards
using
Behavioural
intention to
use
Item
Mean
Standard
deviation
Corrected
item-total
correlation
PU1
PU2
PU3
PU4
PU5
PU6
PU7
PU8
PEOU1
PEOU2
PEOU3
PEOU4
PEOU5
PEOU6
TEB1
TEB2
TEB3
TEB4
TEB5
ATT2
ATT3
ATT4
ATT5
BI1
BI2
BI3
BI4
5.86
5.95
6.02
5.91
6.01
5.89
6.12
6.22
5.63
5.71
5.68
5.75
5.78
5.78
5.41
5.71
5.54
5.34
5.54
5.88
5.75
5.97
5.78
6.11
6.09
6.06
5.75
0.927
0.810
0.858
0.868
0.843
0.866
0.851
0.817
1.161
1.052
1.063
1.029
0.904
0.946
1.064
0.880
0.945
1.167
0.991
0.853
0.913
0.820
0.909
0.750
0.755
0.747
0.934
0.683
0.782
0.821
0.799
0.809
0.821
0.771
0.728
0.759
0.852
0.794
0.761
0.821
0.842
0.805
0.716
0.752
0.587
0.805
0.779
0.821
0.777
0.839
0.769
0.813
0.818
0.602
Cronbach's
Alpha
0.936
0.932
0.886
0.914
0.879
Cronbach's
Alpha if Item
Deleted
0.935
0.927
0.924
0.926
0.925
0.924
0.928
0.931
0.927
0.913
0.920
0.924
0.918
0.915
0.841
0.865
0.856
0.900
0.843
0.896
0.882
0.897
0.875
0.835
0.818
0.816
0.912
The level of internal consistency of the items in each of the five groups was verified, so
simple confirmatory factor analyses would be carried out on each group of items to extract
the corresponding latent variables.
The values of the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) are all
greater than 0.6 and the Barllett’s Test of Sphericity is significant with p = 0.000. All five
factors analyses are therefore appropriate (Pallant, 2007; Tabachnick and Fidell, 2007)
(Table 7). Furthermore, all items were loaded quite strongly. As a result, the five factors
extracted from five groups of items are appropriate for further use in data processing.
12
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 7: Simple confirmatory factor analyses for 5 latent variables
Factor
Perceived
Usefulness
Perceived
Ease of Use
Trust in ebanking
Attitude
towards using
Behavioural
intention to
use
Communialities
Item loading
KMO = 0.921***. Total of initial Eigenvalues = 5.557ª. Percentage of variance = 69.463
PU1
0.566
0.752
PU2
0.694
0.833
PU3
0.756
0.870
PU4
0.727
0.852
PU5
0.742
0.862
PU6
0.759
0.871
PU7
0.686
0.828
PU8
0.626
0.791
KMO = 0.897***. Total of initial Eigenvalues = 4.524ª. Percentage of variance = 75.401
PEOU1
0.689
0.901
PEOU2
0.812
0.898
PEOU3
0.738
0.884
PEOU4
0.698
0.859
PEOU5
0.782
0.835
PEOU6
0.806
0.830
KMO = 0.877***. Total of initial Eigenvalues = 3.502ª. Percentage of variance = 70.048
TEB1
0.797
0.893
TEB2
0.686
0.888
TEB3
0.724
0.851
TEB4
0.506
0.828
TEB5
0.788
0.712
KMO = 0.831***. Total of initial Eigenvalues = 3.18ª. Percentage of variance = 79.498
ATT2
0.767
0.914
ATT3
0.813
0.902
ATT4
0.765
0.876
ATT5
0.835
0.875
KMO = 0.801***. Total of initial Eigenvalues = 3.012ª. Percentage of variance = 75.295
BI1
0.790
0.915
BI2
0.836
0.908
BI3
0.825
0.889
BI4
0.561
0.749
Notes: ***. Significant at 0.01 level. ª. One component was extracted
5.3 Factor-of-factor analysis
In order to evaluate the five latent variables, a factor-of-factor analysis was carried out.
Before conducting a confirmatory factor analysis, the level of internal consistency of the
items was verified, as shown in Table 8.
All corrected item-total correlations are greater than 0.6, indicating relatively high
correlation among items in the groups (Pallant, 2007; Tabachnick and Fidell, 2007) (Table
8). The Alpha score 0.846 is greater than the limitation of 0.7 (Hair, 2010). As a result, the
group is considered appropriate for confirmatory factor analysis.
Table 9 indicates that the score of the Kaiser-Meyer-Olkin Measure of Sampling Adequacy
(KMO) is 0.814, which is greater than the suggested minimum score of 0.6 (Pallant, 2007;
Tabachnick and Fidell, 2007); the Barllett’s Test of Sphericity is significant with p = 0.000.
The factor loading values are mostly greater than 0.7 and are considered very high
(Pallant, 2007; Tabachnick and Fidell, 2007).
13
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 8: Cronbach’s alpha for internal consistency checking of 5 latent variables
Corrected itemtotal correlation
Factor
Perceived Usefulness
Perceived Ease Of Use
Trust in e-banking
Attitude Towards Using
Behavioural Intention To Use
Cronbach's
Alpha
0.750
0.629
0.602
0.631
0.661
Alpha if item
is deleted
0.789
0.822
0.829
0.821
0.813
0.846
Table 9: Factor analysis of Structural Model
Item
Communalities Factor loading
KMO = 0.814***. Total of initial Eigenvalues = 3.104ª.
Percentage of variance = 62.078. P-value=0.000
Perceived Ease Of Use
0.736
0.770
Perceived Usefulness
0.593
0.858
Trust in e-banking
0.554
0.745
Attitude Towards Using
0.591
0.769
Behavioural Intention To Use
0.629
0.793
Notes: ***. Significant at 0.01 level; ª. One component was extracted
As a result, the factor extracted from the five latent variables is appropriate. This factor is
called the Structural Model. CFA was used to examine the convergent validity of each
construct. A single factor model for each of the constructs was specified and presented in
Table 9. Table 9 also showed the factor loadings of the measurement items: all items
passed the recommended level for factor loading of 0.6 (Chin et al., 1997).
Table 10 shows the overall model-fit indices for each CFA model as recommended by
Kline (2011). Most indices passed the recommended level, suggesting that the items of
each construct reflect a single factor (Table 10). According to Fornell and Larcker (1981)
and Chin (1998), the requirement of discriminant validity is satisfied when the Average
Variance Extracted (AVE) of the construct is larger than all pair square correlations
between the construct and other constructs in the model.
Table 10: Overall model fit indices of CFA for convergent validity
Construct
Chisquar
e
d.f
Recommended Value
Perceived Usefulness
Perceived Ease of Use
46.05
1
16.10
2
17
6
Trust in e-banking
8.180
5
Attitude towards using
0.927
1
Behavioural intention to
use
0.143
1
𝜒 ₂ /df
< 3.0
2.70
9
2.68
4
1.63
6
0.92
7
0.14
3
GFI
AGFI
NFI
TLI
CFI
RMSEA
PCLOS
E
> 0.9
0.97
5
0.98
9
0.99
3
0.99
9
1.00
0
> 0.8
> 0.9
0.98
4
0.99
3
0.99
4
0.99
9
1.00
0
> 0.9
0.98
4
0.98
9
0.99
5
1.00
0
1.00
4
> 0.9
< 0.08
> 0.05
0.990
0.061
0.182
0.996
0.06
0.272
0.998
0.037
0.626
1.000
0.000
0.566
1.000
0.000
0.831
0.946
0.961
0.979
0.990
0.998
Note: GFI: Goodness of Fit Index, AGFI: adjusted goodness of fit index, NFI: normed fit index, TLI: Tucker-Lewis Index,
CFI: comparative fit index, RMSEA: root mean square error of approximation, PCLOSE is an alternative test of
hypothesis that RMSEA is less than or equal to 0.05.
As shown in Table 11, the diagonal elements (in bold) are the square root of the average
variance extracted (AVE), and off-diagonal elements are the correlations among
constructs. For discriminant validity, the diagonal elements should be larger than the off14
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
diagonal elements. As a result, all indicators load more highly on their own constructs than
on other constructs. All these results point to the discriminant validity of the constructs.
Table 11: Discriminant validity of constructs
Attitude Towards Using
Attitude
Towards
Using
0.825
Perceived
Usefulness
Perceived
Ease Of
Use
Trust in
EBanking
Perceived Usefulness
0.633
0.808
Perceived Ease Of Use
0.505
0.772
0.840
Trust in E-Banking
Behavioural Intention to
use
0.530
0.602
0.568
0.796
0.758
0.642
0.518
0.523
Behavioural
Intention to
use
0.827
5.4 Normality distribution
The assessment method suggested by Hair et al. (2010) proves the violation of normality.
Nevertheless, taking into consideration these acceptable values of skewness and kurtosis,
and that the sample size was significantly larger than 200 (in this study, sample = 464
participants), the influence of this assumption violation on the results of the parametric
analysis would be minimal (Curran et al., 1996; Mendenhall et al., 2012; Tabachnick and
Fidell, 2007). Furthermore, Shah and Goldstein (2006) and Jöreskog and Sörbom (1993)
suggested that non-normality can be tolerable when the SEM Maximum Likelihood (ML)
estimation technique is used. Therefore, violation of the normality assumption was
considered to be at an acceptable level for the parametric statistical method (Byrne, 2013;
Curran et al., 1996; Mendenhall et al., 2012).
5.5 The final measurement model
After CFA was conducted for each construct, the whole measurement model was
subjected to CFA to test the discriminant validity and to evaluate the overall fit (as
reported). In the overall measurement model, all constructs are free to correlate with
others.
The relationships among latent variables are depicted in the overall measurement model
shown in Figure 3. The five latent variables were measured by respective items. These
latent variables can correlate with each other. The selected fit indices of this model (chisquare/df = 2.38, GFI = 0.897, TLI = 0.954, CFI = 0.960, RMSEA = 0.055, PCLOSE =
0.073) indicate that this model has achieved a very good fit.
The correlation coefficients among constructs are provided to evaluate multi-collinearity
(see Table 12). According to Hair et al. (2010) and Kline (2011), variables highly correlated
with each other (i.e., 0.8 and above) indicate the problem of multi-collinearity. None of
these constructs in this study are too highly correlated with each other in each model, as
the highest correlation was 0.772 between Perceived Ease of Use and Perceived
Usefulness. Therefore, the level of multi-collinearity was considered to be acceptable.
15
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Figure 3: Structure of the model and inter-relationships among latent variables
5.6 The final structural model
The structural model identifies the relationship among latent variables. It then investigates
the way by which certain latent variables directly or indirectly influence other latent
variables (Byrne, 2013). In line with the hypotheses, the second step of the analysis
relates to constructing structural models.
After assessing the reliability and validity with CFA, we test the structural model fit. The
overall model fit evaluates the correspondence of the actual or observed input matrix with
that predicted from the propose model. Table 12 shows a summary of the overall fit indices
of the path model.
16
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Table 12: Structural model fit indices
Construct
Chisquare
df
𝜒 ₂ /df GFI
AGFI
NFI
TLI
CFI
RMSEA
PCLOSE
Recommended
<
> 0.8 > 0.8 > 0.9 > 0.9 > 0.9
< 0.08
> 0.05
Value
3.0
Structural Model
720.001 303 2.38 0.897 0.871 0.934 0.954
0.96
0.055
0.073
Note: GFI: Goodness of Fit Index, AGFI: adjusted goodness of fit index, NFI: normed fit index, TLI: TuckerLewis Index, CFI: comparative fit index, RMSEA: root mean square error of approximation, PCLOSE is an
alternative test of hypothesis that RMSEA is less than or equal to 0.05.
Most indices passed the recommended level. The indices of this model indicate that this
model achieved a very good fit (Table 12).
The structural model specifies the relationships among latent variables. All the path
coefficients of the structural model are standardized; the square multiple correlation
(analogous to R2 in linear regression analysis) of each dependent latent variable was also
depicted (Figure 4). The results from the structural model in Table 13 are used to test the
hypotheses (from H1 to H9).
Table 13: Standardized regression weights (SRW)
Behavioural relationship
Hypothesis SRW
Pvalue
Behavioural_Intention_to_Use
←
Trust
H1
0.093
0.051
Attitude_towards_Using
←
Trust
H2
0.242
***
Trust
←
Perceived_Usefulness
H3
0.418
***
Attitude_towards_Using
←
Perceived_Usefulness
H4
0.513
***
Behavioural_Intention_to_Use
←
Perceived_Usefulness
H5
0.186
0.008
Trust
←
Perceived_Ease_of_Use
H6
0.241
***
Attitude_towards_Using
←
Perceived_Ease_of_Use
H7
0.047
0.523
Perceived_Usefulness
←
Perceived_Ease_of_Use
H8
0.779
***
Behavioural_Intention_to_Use
←
Attitude_towards_Using
H9
0.609
***
Note: *** indicates p<0.001
17
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
Figure 4: Result of structural analysis: primary model
5.7 Hypotheses testing
H1: Trust has a positive effect on the behavioural intention to use e-banking.
H2: Trust has a positive impact on the attitude towards using
e-banking.
The results presented in Figure 4 and Table 13 indicate that the relationship between Trust
and Behavioural intention to use is positive (standardized regression weight = 0.093) and
significant (at 5% level of significant), thereby Hypothesis 1 is supported. Moreover, the
relationship between Trust and Attitude towards using is positive and significant
(standardized regression weight = 0.242, p<0.001) as reported in Table 13. Thus,
hypothesis 2 is supported. These findings are also consistent with the previous study by
Suh and Han (2002).
H3: Perceived usefulness has a positive impact on trust in e-banking.
H4: Perceived usefulness has a positive impact on the attitude towards using e-banking.
H5: Perceived usefulness has a positive impact on the behavioural intention to use ebanking.
The relationship between Perceived usefulness and Trust is positive and significant
(standardized regression weight = 0.418, p<0.001), providing support for Hypothesis 3
18
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
(Table 13). Accordingly, hypothesis 4 suggested that perceived usefulness has a positive
impact on the attitude towards using e-banking. The results in Table 13 indicate that
perceived usefulness is positive, in the relation with attitude towards using, and significant
(standardized regression weight = 0.513, p<0.001), thus, hypothesis 4 is supported. This
finding is also consistent with the previous study by Çelik (2008). Moreover, perceived
usefulness has a positive effect on the behavioural intention to use e-banking and is
significant with standardized regression weight = 0.186, p<0.05; thus hypothesis 5 is
supported (Table 13). This finding is not consistent with the previous study by Kasheir et
al. (2009), which found that perceived usefulness did not have any significant effect on
customers’ behavioural intention to use e-banking.
H6: Perceived ease of use has a positive impact on trust in e-banking.
H7: Perceived ease of use has a positive impact on the attitude towards using e-banking.
H8: Perceived ease of use has a positive impact on perceived usefulness.
The results show that perceived ease of use has a positive effect and is significant on trust
in e-banking (standardized regression weight = 0.241, p<0.001), providing strong evidence
to support hypothesis 6 (Table 13). Interestingly, perceived ease of use was a negative
relationship and has no significant influence on the attitude towards using e-banking
(standardized regression weight = -0.047, p<0.523). Thus, hypothesis 7 that perceived
ease of use has a positive impact on the attitude towards using e-banking is not
supported. This finding is in contrast with the previous studies by Suh and Han (2002),
Amin (2007), Çelik (2008), and Jahangir and Begum (2008) that perceived ease of use
directly affects customer attitude towards using e-banking. Moreover, this result also
contradicts the original TAM models. On the other hand, it is consistent with the finding of
Pikkarainen et al. (2004), which suggested that there is no significant impact of perceived
ease of use on customer intention to use e-banking. However, perceived ease of use has
a positive effect and is significant with perceived usefulness (standardized regression
weight = 0.779, p<0.001), providing strong evidence to support hypothesis 8. This finding
is consistent with findings by Suh and Han (2002) that a customers’ perceived ease of use
has a positive impact on his/her perceived usefulness of e-banking.
H9: Attitude towards using has a positive impact on the behavioural intention to use ebanking.
Hypothesis 9 suggested that there is a positive relationship between the attitude towards
using and behavioural intention to use e-banking. The result seen in Table 13 shows that
the attitude towards using has a positive impact on the behavioural intention to use ebanking and is significant (standardized regression weight = 0.609, p<0.001), thus,
hypothesis 9 is supported. This finding is consistent with findings by Shih and Fang (2006),
who concluded that the attitude towards using e-banking is significantly related to the
behavioural intention to use e-banking.
In the data analysis used to test the hypotheses, SEM using two-step approach was
applied. First, measurement models were tested to measure the reliability and validity of
the latent variables (perceived usefulness, perceived ease of use, trust, attitude towards
using, and behavioural intention to use). Structural models were then developed to
evaluate the relationships among those latent variables.
The results provided support for all the hypotheses, except for H7 (Table 14). The trust
has significant effects on both attitude and behavioural intention to use e-banking. The
19
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
perceived usefulness has positive significant impact on trust in, attitude towards and the
behavioural intention to use e-banking. The perceived ease of use has positive significant
effect on trust and perceived usefulness. However, perceived ease of use does not have
significant effect on the attitude towards using e-banking. Attitude towards using e-banking
has positive impact on the behavioural intention to use e-banking.
Table 14: Summarized results of hypotheses testing
Hypotheses
Statement
P-value
Results
H1
Trust has a positive effect on the behavioural intention to use ebanking.
0.051
Supported
H2
Trust has a positive impact on the attitude towards using e-banking.
***
Supported
H3
Perceived usefulness has a positive impact on trust in e-banking.
***
Supported
H4
Perceived usefulness has a positive impact on the attitude towards
using e-banking.
***
Supported
H5
Perceived usefulness has a positive impact on the behavioural
intention to use e-banking.
0.008
Supported
H6
Perceived ease of use has a positive impact on trust in e-banking.
***
Supported
0.523
Not
supported
***
Supported
***
Supported
H7
H8
H9
Perceived ease of use has a positive impact on the attitude towards
using e-banking.
Perceived ease of use has a positive impact on perceived
usefulness.
Attitude towards using e-banking has a positive impact on the
behavioural intention to use e-banking.
Note: *** indicates p<0.001
6. Conclusion
This study has discovered that the trust is one of the most significant factors in explaining
customer intention to use e-banking. According to Davis (1989), the TAM suggested that
customers’ perceived usefulness and perceived ease of use also affect their attitude
towards using significantly. Then, behavioural intention to use e-banking is highly related
to attitude towards using, perceived usefulness, and trust. However, the results in this
study show that customers’ perceived usefulness is highly related to attitude towards
using, while customers’ perceived ease of use has not affected attitude towards using.
Overall, these results imply that customers in online environments still rely on trust that
their sensitive information is being processed.
In the online environment, trust plays an important role in financial transactions. This study
has extended the TAM by added an additional belief, trust, one of the most important
determinants of customer intention to use e-banking. This model is tested empirically to
explain customer intention to use e-banking in Vietnam. These results give a better
understanding of the factors affecting customers’ behavioural intention to use e-banking in
a developing country like Vietnam. In comparison with other countries, Vietnamese
customers might have different cultures, so it is important to consider whether trust will
affect their intention to use e-banking.
These results will benefit practitioners, e-banking system developers, bank decision
makers and bank service providers. Bank managers and decision makers in Vietnam can
20
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
use these findings when making their strategies and plans for future development. It is
suggested that banks should focus on improving their e-banking websites to be more and
more friendly and attractive, but the perceived usefulness of e-banking is more important
than the perceived ease of use. Banks should also further explore what features are useful
to the Vietnamese customers, then design the e-banking website’s characteristic based on
these features. Through education, banks can show the advantages of e-banking to
customers and more useful features should be investigated to attract more e-banking
customers. Trust is one of the most important factors affecting customer intention to use ebanking in Vietnam, so banks in Vietnam should ensure that security and privacy of ebanking systems are regularly upgraded, while customers should be advised that their
systems are secure and personal information is absolutely protected.
There are several limitations in this study. First, as in previous studies, the selected model
and factors may not cover all the reasons that could affect customers’ intention to use ebanking in Vietnam. Therefore, future research needs to explore other models and factors
related to cultural issues or consumers’ habits which may have influences to the intention
to use e-banking. Second, this study is based on customer experience in using e-banking
to answer the questionnaire survey to collect data for analysis. Future research analysis
can be based on a demographic profile such as age group, income, or level of education.
For example, customers in an older age group might find it more of a challenge to use ebanking transactions, thus perceived ease of use might be one important factor affecting
customer intention to use e-banking. Third, this study was conducted with Vietnamese
customers in a developing country like Vietnam, so all ideas reflect the Vietnamese
perspective. Future study can apply the model used in this study to other developing
countries. Lastly, data for this research was collected in selected provinces in Vietnam, so
the results might have not covered other provinces because of the difference between
area cultural issues. Future research can collect data in many more areas and provinces in
order to reflect more generally the customer intention to use e-banking in Vietnam.
References
ABDULLAHAL-SOMALI, S., ROYAGHOLAMI & BENCLEGG 2009. An investigation into the
acceptance of onlinebanking in Saudi Arabia. Technovation, 29, 130–141.
AJZEN, I. & FISHBEIN, M. 1980. Understanding attitudes and predicting social behaviour,
Prentice-Hall.
ALAIN YEE-LOONG CHONG, KENG-BOON OOI, BINSHAN LIN & TAN, B.-I. 2010. Online
banking adoption: an empirical analysis. International Journal of Bank Marketing, Vol. 28,
pp.267 - 287.
ALDÁS-MANZANO, J., LASSALA-NAVARRÉ, C., RUIZ-MAFÉ, C. & SANZ-BLAS, S. 2009. Key
drivers of internet banking services use. Online Information Review, Vol. 33, pp.672 - 695.
ALSAJJAN, B. A. & DENNIS, C. 2006. The Impact of Trust on Acceptance of Online Banking.
European Association of Education and Research in Commercial Distribution.
AMIN, H. 2007. Internet Banking Adoption Among Young Intellectuals. Journal of Internet Banking
& Commerce, 12.
ANDERSON, J. C. & GERBING, D. W. 1988. Structural equation modeling in practice: A review
and recommended two-step approach. Psychological bulletin, 103, 411.
ANITA LIFEN ZHAO, NICOLE KOENIG-LEWIS, STUART HANMER-LLOYD & WARD, P. 2010.
Adoption of internet banking services in China: is it all about trust? International Journal of
Bank Marketing, Vol. 28, pp. 7 - 26.
BA, S. & PAVLOU, P. A. 2002. Evidence of the effect of trust building technology in electronic
markets: Price premiums and buyer behaviour. MIS Quarterly, 243-268.
21
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
BENAMATI, J. S. & SERVA, M. A. 2007. Trust and distrust in online banking: Their role in
developing countries. Information Technology for Development, Volume 13, pages 161–
175.
BYRNE, B. M. 2013. Structural equation modeling with AMOS: Basic concepts, applications, and
programming, Routledge.
CASALÓ, L. V., FLAVIÁN, C. & GUINALÍU, M. 2007. The role of security, privacy, usability and
reputation in the development of online banking. Online Information Review, 31, 583-603.
ÇELIK, H. 2008. What determines Turkish customers' acceptance of internet banking?
International Journal of Bank Marketing, Vol. 26, pp.353 - 370.
CHANG, C., LEE, A. C. & LEE, C. F. 2009. Determinants of capital structure choice: A structural
equation modeling approach. The quarterly review of economics and finance, 49, 197-213.
CHIEMEKE, S. C., EVWIEKPAEFE, A. E. & CHETE, F. O. 2006. The Adoption of Internet Banking
in Nigeria: An Empirical Investigation. Journal of Internet Banking and Commerce, vol. 11,
no.3.
CHIN, W. W. 1998. Commentary: Issues and opinion on structural equation modeling. JSTOR.
CHIN, W. W., GOPAL, A. & SALISBURY, W. D. 1997. Advancing the theory of adaptive
structuration: The development of a scale to measure faithfulness of appropriation.
Information systems research, 8, 342-367.
CURRAN, P. J., WEST, S. G. & FINCH, J. F. 1996. The robustness of test statistics to
nonnormality and specification error in confirmatory factor analysis. Psychological methods,
1, 16.
DAVIS, F. D. 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology. MIS Quarterly, Vol. 13, No. 3, pp. 319-340.
DAVIS, F. D., BAGOZZI, R. P. & WARSHAW, P. R. 1989. User Acceptance of Computer
Technology: A Comparison of Two Theoretical Models. Management Science, 35, 9821003.
DIXIT, N. & DATTA, S. K. 2010. Acceptance of E-banking among Adult Customers: An Empirical
Investigation in India. Journal of Internet Banking and Commerce, vol. 15, no.2.
DONEY, P. M. & CANNON, J. P. 1997. An examination of the nature of trust in buyer-seller
relationships. the journal of marketing, 35-51.
FORNELL, C. & LARCKER, D. F. 1981. Evaluating structural equation models with unobservable
variables and measurement error. Journal of Marketing Research (JMR), 18.
FRONELL, C. 1982. A second generation of multivariate analysis. Methods, New York, Praeger, 1.
GEFEN, D. 2000. E-commerce: the role of familiarity and trust. Omega, 28, 725-737.
GRABNER-KR.AUTERA, S. & KALUSCHA, E. A. 2003. Empirical research in on-linetrust: a review
and critical assessment. Int. J. Human-Computer Studies, 58, 783–812.
GRABNER-KRÄUTER, S. & FAULLANT, R. 2008. Consumer acceptance of internet banking: the
influence of internet trust. International Journal of Bank Marketing, Vol. 26, pp.483 - 504.
HAIR, J. & BLACK, W. 2010. Multivariate Data Analysis. New Jersey: Pearson Prentice Hall.
HAIR, J. F. 2009. Multivariate data analysis.
HOY, W. K. & TARTER, C. J. 2004. Organizational justice in schools: no justice without trust.
International Journal of Educational Management, 18, 250-259.
HOYLE, R. H. 1995. Structural equation modeling: Concepts, issues, and applications, Sage.
JAHANGIR, N. & BEGUM, N. 2008. The role of perceived usefulness, perceived ease of use,
security and privacy, and customer attitude to engender customer adaptation in the context
of e-banking. African Journal of Business Management, 2, 32-40.
JÖRESKOG, K. G. & SÖRBOM, D. 1993. Lisrel 8: Structured equation modeling with the Simplis
command language, Scientific Software International.
KASHEIR, D. E.-., ASHOUR, A. S. & YACOUT, O. M. 2009. Factors Affecting Continued Usage of
Internet Banking Among Egyptian Customers. Communications of the IBIMA, Vol 9, 252263.
KASSIM, N. M. & ABDULLA, A. K. M. A. 2006. The influence of attraction on internet banking: an
extension to the trust-relationship commitment model. International Journal of Bank
Marketing, Vol. 24, pp.424 - 442.
KHALIL MD NOR, JANEJIRA SUTANONPAIBOON & MASTOR, N. H. 2010. Malay, Chinese, and
internet banking. Chinese Management Studies, Vol. 4, pp.141 - 153.
22
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
KLINE, R. B. 2011. Principles and practice of structural equation modeling, Guilford press.
KOUFARIS, M. & HAMPTON-SOSA, W. 2004. The development of initial trust in an online
company by new customers. Information & Management, 41, 377-397.
LICHTENSTEIN, S. & WILLIAMSON, K. 2006. Understanding consumer adoption of internet
banking: an interpretive study in the Australian banking context. Journal of Electronic
Commerce Research, 7, 50-66.
LIMBU, Y. B., WOLF, M. & LUNSFORD, D. 2012. Perceived ethics of online retailers and
consumer behavioural intentions: The mediating roles of trust and attitude. Journal of
Research in Interactive Marketing, 6, 133-154.
LIU, T.-C. & WU, L.-W. 2007. Customer retention and cross-buying in the banking industry: an
integration of service attributes, satisfaction and trust. Journal of Financial Services
Marketing, 12, 132-145.
LOMAX, R. G. & SCHUMACKER, R. 2012. A beginner's guide to structural equation modeling,
Routledge Academic.
LU, C.-S., LAI, K.-H. & CHENG, T. E. 2007. Application of structural equation modeling to evaluate
the intention of shippers to use Internet services in liner shipping. European Journal of
Operational Research, 180, 845-867.
MACINTOSH, G. & LOCKSHIN, L. S. 1997. Retail relationships and store loyalty: a multi-level
perspective. International Journal of Research in marketing, 14, 487-497.
MAYER, R. C., DAVIS, J. H. & SCHOORMAN, F. D. 1995. An integrative model of organizational
trust. Academy of management review, 709-734.
MCKNIGHT, D. H., CHOUDHURY, V. & KACMAR, C. 2002. Developing and validating trust
measures for e-commerce: An integrative typology. Information systems research, 13, 334359.
MENDENHALL, W., BEAVER, R. & BEAVER, B. 2012. Introduction to probability and statistics,
Cengage Learning.
MORGAN, R. M. & HUNT, S. D. 1994. The commitment-trust theory of relationship marketing. the
journal of marketing, 20-38.
MUÑOZ-LEIVA, F., LUQUE-MARTÍNEZ, T. & SÁNCHEZ-FERNÁNDEZ, J. 2010. How to improve
trust toward e-banking. Online Information Review, Vol. 34, pp.907 - 934.
NDUBISI, N. O. & SINTI, Q. 2006. Consumer attitudes, system's characteristics and internet
banking adoption in Malaysia. Management Research News, Vol. 29, pp. 16 - 27.
PALLANT, J. 2007. A step-by-step guide to data analysis using SPSS version 15. Open University
Press, Maidenhead.
PALMER, A. & BEJOU, D. 1994. Buyer‐seller relationships: A conceptual model and empirical
investigation. Journal of Marketing Management, 10, 495-512.
PIKKARAINEN, T., PIKKARAINEN, K., KARJALUOTO, H. & PAHNILA, S. 2004. Consumer
acceptance of online banking: an extension of the technology acceptance model. Emerald
14.
PLOUFFE, C. R., VANDENBOSCH, M. & HULLAND, J. 2001. Intermediating technologies and
multi‐group adoption: A comparison of consumer and merchant adoption intentions toward
a new electronic payment system. Journal of Product Innovation Management, 18, 65-81.
RATNASINGHAM, P. 1998. The importance of trust in electronic commerce. Internet Research, 8,
313-321.
ROUSSEAU, D. M., SITKIN, S. B., BURT, R. S. & CAMERER, C. 1998. Not so different after all: A
cross-discipline view of trust. Academy of management review, 23, 393-404.
SATHYE, M. 1999. Adoption of Internet banking by Australian consumers: an empirical
investigation. International Journal of Bank Marketing, Vol. 17, pp. 324 - 334.
SHAH, R. & GOLDSTEIN, S. M. 2006. Use of structural equation modeling in operations
management research: Looking back and forward. Journal of Operations Management, 24,
148-169.
SHIH, Y.-Y. & FANG, K. 2006. Effects of network quality attributes on customer adoption intentions
of Internet Banking. Total Quality Management & Business Excellence, 17:1, 61-77.
SHUMAILA Y. YOUSAFZAI, JOHN G. PALLISTER & FOXALL, G. R. 2003. A proposed model of
e-trust for e-banking. Technovation, 23, 847–860.
23
Proceedings of Global Business and Finance Research Conference
5-6 May, 2014, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-50-4
SMITH, P. A. & BIRNEY, L. L. 2005. The organizational trust of elementary schools and
dimensions of student bullying. International Journal of Educational Management, 19, 469485.
STRAUB, D. W. 1989. Validating instruments in MIS research. MIS Quarterly, 147-169.
SUH, B. & HAN, I. 2002. Effect of trust on customer acceptance of Internet banking. Electronic
Commerce Research and Applications, 1, pp 247–263.
TABACHNICK, B. & FIDELL 2007. Using multivariate statistics. 5.
TAN, M. & TEO, T. S. H. 2000. Factors Influencing the Adoption of Internet Banking. Journal of the
Association for Information Systems, Volume 1.
TITMAN, S. & WESSELS, R. 1988. The determinants of capital structure choice. The Journal of
Finance, 43, 1-19.
VATANASOMBUT, B., IGBARIA, M., STYLIANOU, A. C. & RODGERS, W. 2008. Information
systems continuance intention of web-based applications customers: The case of online
banking. Information & Management, 45, 419-428.
WAN, W. W. N., LUK, C.-L. & CHOW, C. W. C. 2005. Customers' adoption of banking channels in
Hong Kong. International Journal of Bank Marketing, Vol. 23, pp.255 - 272.
WANG, A. 2010. THE PRACTICES OF MOBILE ADVERTISING DISCLOSURE ON CONSUMER
TRUST AND ATTITUDE. International Journal of Mobile Marketing, 5.
WANG, J.-S. & PHO, T.-S. 2009. Drivers of customer intention to use online banking: An empirical
study. African Journal of Business Management, Vol.3 (11), pp. 669-677.
WHITENER, E. M., BRODT, S. E., KORSGAARD, M. A. & WERNER, J. M. 1998. Managers as
initiators of trust: An exchange relationship framework for understanding managerial
trustworthy behaviour. Academy of management review, 23, 513-530.
WONG, D. H., LOH, C., YAP, K. B. & BAK, R. 2009. To Trust or Not to Trust: The Consumer’s
Dilemma with E-banking. Journal of Internet Business, pp 1-27.
24