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© 2010 Macmillan Publishers Ltd. 1363-0539 Journal of Financial Services Marketing Vol. 15, 1, 76–94
INTRODUCTIONThis study defines Internet banking (IB) as
‘Web-Based Banking’, whereby bank
account holders can interact with and obtain
a bank’s financial services (both informational
and transactional) in a virtual environmentusing any device connected to the Internet.
Internet Banking is a business model through
Correspondence: Ali Hussein Saleh Zolait
Faculty of Business and Accountancy, University of Malaya,
50603 Kuala Lumpur, Malaysia
E-mails: [email protected]; [email protected]
which a bank integrates both offline (bricks)
and online (clicks) presences (Bricks-and-
clicks).1 According to Cyree et al ,1 there are
two general business models used to provide
IB: ‘bricks and clicks’ and ‘Internet-primary’
banks. The ‘bricks and clicks’ model utilizestraditional brick and mortar offices,
supplemented by the Internet, similar to a
firm with a physical market presence, such as
Barnes and Noble, also operating a website
in which products can be purchased.1 Banks
utilize the web networks to provide
customers with information on their financial
Original Article
An examination of the factors
influencing Yemeni Bank users’behavioural intention to useInternet banking servicesReceived (in revised form): 24th February 2010
Ali Hussein Saleh Zolaitis Visiting Research Fellow and Lecturer of MIS in the Faculty of Business and Accountancy at the University of Malaya. His research
interests are management information systems (MIS), diffusion of innovation, security, and e-commerce application and performance.
ABSTRACT The purpose of this study was to examine the potential prominent factorsrelating to the adoption and use of the financial services of Internet banking (IB). The
study was carried out using a self-administered survey involving a convenience sample
of 369 Yemeni bank customers. The survey revealed that the overall prominent predictors
include Relative Advantage / Compatibility, User’s Informational-Based Readiness, Attitude,
Observability, Technology Facilitating Condition, Perceived Behavioural Control and
Self-efficacy. The model accounted for 75 per cent of the variation of an individual’s
behavioural intention to use IB. In addition, it was also discovered that a majority of the
respondents are innovators and early adopters of IB. Yet, the adoption of IB financial
service is still relatively low.
Journal of Financial Services Marketing (2010) 15, 76–94. doi:10.1057/fsm.2010.1
Keywords: financial services; Internet banking; adoption factors; diffusion of innovation
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Factors influencing Yemeni Bank users’ BI to use IB services
services, replace transactions conducted
in branch offices and assist the bank’s
management to execute internal
administration. In addition, IB eliminates the
need to build new branches, eliminating
the overhead expenses of conventional banks
and service customers more efficiently.According to Mattila et al. 2 and Dandapani
et al. 49 IB services can provide timely,
speedy, accurate and convenient banking
opportunities round the clock. In addition,
customers can benefit from new banking
services such as paying bills online, finding
mortgages or auto loans, applying for credit
cards and locating the nearest ATM or
branch office. For the banks, IB offers them
many opportunities such as an additional
delivery channel, low-cost banking, profitablebanking, quality banking, and allows them to
sell products customized to individual
needs.3 – 4
IB broadens the geographical reach of
banks and can help to build and retain
additional customers.4
IB has appeared as the trend in banking,
nowadays, and emerged as one of the
payment models required to enable pure
e-commerce models to occur in online
business, rather than traditional banking.
There is a clear need to study the factors thatinfluence customers’ intention to adopt IB so
that banks can better formulate their
marketing strategies to increase IB usage in
the future. It is also important to note that
nations around the world need to get
connected and join the global networked
community.5 On the other hand, banking is
an information-intensive business4 in which
information technology (IT) is increasingly
becoming an invaluable and powerful tool
driving development, supporting growth,promoting innovation and enhancing
competitiveness.5 Furthermore, emerging IT
offers opportunities for developing nations to
leapfrog the earlier stages of development.5
Therefore, according to Cheng et al ,6 bankers
should accentuate the full functionality of
their systems to cater to the different banking
needs of the users efficiently. Moreover,
understanding the benefits of IB adoption
will encourage banks to develop new
products and services to fully utilize the
Internet’s capabilities.6 IB is widespread in
developed countries, but not yet in some
countries such as the Republic of Yemen.For instance, research on the factors that
identify and influence the adoption of IB has
not yet been studied academically in non-
western and developing countries such as the
Republic of Yemen. In the light of stiff
competition among banks in Yemen, the
banks themselves are now competing to gain
a larger market share and to use advanced
technologies to retain customers in the future.
The Theory of Planned Behaviour (TPB)
was developed as an extension of the theoryof reasoned action (TRA) to justify
conditions in which individuals do not have
complete control over their behaviour.7 This
theory posits that behaviour is determined by
the intention to perform the behaviour. In
TPB, there are three constructs that
determine the user ’s intention, which are
attitude, Subjective Norms (SN) and
Perceived Behavioural Control (PBC).
The TPB has been used to study the
adoption of different information systems
such as spreadsheets,8 computer resourcecentres9 and electronic brokerages by
Battacherjee10 and negotiation support
systems by Lim et al. 11 This study is of
value as it extends the TPB and Extended
Theory of Planned Behaviour (ETPB)12
by incorporating user readiness and
examines its impact on an individual’s
intention to adopt IB. From a theoretical
perspective, the findings will help and
expand the understanding of the constructs
that affect technology adoption. It alsoconfirms the multidimensionality of user
readiness and its role in the adoption of IB.
From a practical perspective, the findings can
help banks that have plans to offer IB
services to make informed decisions about
the actions they can take to increase their
chances of success.
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From the above discussion, the benefits of
IB are numerous for both the customers and
banks. Banks all over the world have been
offering their financial services via the
Internet to their customers. The adoption
rate among customers, however, varies from
country to country, as too do the reasonswhy individuals use IB. Therefore, there is a
need to study IB adoption in Yemen as there
are only a few studies examining the
adoption of IB in developing countries
specifically those in the Arab world and
Yemen. Empirical studies conducted in this
area are mainly either from the western or
Asian developed contexts.2 – 4,13 Yemen is
different from other countries as their IT
usage level is low.14 In addition, there is a
lack of government policies regardingonline activities compared to other countries
around the world. As mentioned earlier,
the factors influencing IB adoption vary
among customers. Many have studied this
occurrence using TAM, TPB, and ETPB.
This study adapted both the TPB and
ETPB models and incorporated user
readiness as a factor influencing IB adoption.
The following section describes the
framework adopted.
FACTORS INFLUENCING THEINTENTION TO USE IBThe development of this study’s conceptual
framework is based on TPB, which is an
extension of TRA.7 This theory posits that
behaviour is determined by the intention to
perform the behaviour. There are three
constructs that determine the user ’s intention,
which are attitude, norms and PBC. TPB
has been used in several studies in the field
of information systems.8,9,11,15
This studyextends TPB to account for User ’s
Informational-Based Readiness (UIBR) in
studying acceptance predictors. It is argued
by Zolait et al 16 that when a customer
gains awareness, knowledge, prior experience
and exposure to related products of
innovation, those factors may contribute to
predicting the behavioural intention to adopt
the technology.16
Behavioural intention to use IBMeasuring individuals’ behavioural intentions
is core to the theories of adoption, which are
used to study adoption in the InformationSystems field. Researchers have used the
post-implementation phase as the basis for
analysing a user ’s behaviour towards
technology acceptance; in particular, those
who worked on the TAM model as cited by
Mathieson.8 Users’ willingness to adopt is an
important means not only of understanding
the diffusion steps but also of validating the
timing for the decision makers regarding
when they should accept or reject the
proposed innovation.
17
Ajzen and Fishbein
18
defined intention as the individual’s location
on a subjective probability dimension
involving a relationship between the
individual and some action. An individual’s
intention, according to Ajzen,7 is the central
factor in TRA and TPB. It is a function of
three determinants. These determinants are
attitude towards the behaviour, the SN and
PBC.
Information about the existence of
innovations, according to Rogers,19 flows
through social systems to the potentialadopters, which is then processed by the
adopters to form perceptions about the
innovation, such as characteristics, and
perceptions in relation to other contextual
factors, which then serve as a determinant
of innovation adoption behaviour. On the
basis of Rogers’ definition, there are four
main elements existing in the diffusion of
innovation (DOI) process: (1) the characteristics
of innovation; (2) the communication
channels used to communicate the benefitsof the innovation; (3) the time elapsed since
the introduction of the innovation; and
(4) the social system in which the innovation
is to diffuse. This study categorizes the factors
influencing the users’ intentions to use IB
into three types, namely direct, indirect and
readiness factors. These factors will be
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Factors influencing Yemeni Bank users’ BI to use IB services
elaborated and discussed in the following
sections.
Direct antecedents of intention The three variables that are used in TPB as
antecedents of the Intention construct are
attitude, SN and PBC.7,8,18,20 Ajzen7,21 pointed out that most studies concerned with
the prediction of behaviour from attitudinal
variables were conducted in the framework
of TPB. To a certain extent, TPB is a
predecessor of TRA.18 Attitude is
characterized as a person’s inclination to
exhibit a certain response towards a concept
or object. Ajzen and Fishbein18 pointed out
that ‘The attitudinal component refers to the
person’s attitude towards performing the
behaviour under consideration’. Similarly,Rogers19 refers to individuals forming a
favourable or unfavourable attitude towards
an innovation, but with much concern on
the attitude formation, which is equivalent to
persuasion. The main outcome of the
persuasion stage in the innovation-decision
process is either a favourable or an
unfavourable attitude towards the innovation.
Subjective norms comprise the second
component of intention in both the TRA
and TPB models. They deal with the
influence of the social environment. Norms,according to Rogers,19 are established
behavioural patterns for members of the
social system, which tell an individual what
behaviour is expected. Mathieson8 defined
SN as ‘the individual’s perception of social
pressure to perform the behaviour.
PBC, according to TPB, is the third
antecedent of intention. Doll and Ajzen20
posited that when the behaviour or situation,
affords a person complete control over
behavioural performance, intention aloneshould be sufficient to predict behaviour, as
specified in TRA. According to Ajzen,7 PBC
refers to an individual’s perception of the
ease or difficulty of performing the behaviour
of interest. Similarly, Mathieson’s8 definition
of PBC ‘is the individual’s perception of his
or her control over performance of the
behaviour ’. Mathieson8 demonstrated that
behavioural control influences the intention
to use an information system. A positive
relationship between PBC and intentions is
also found in Taylor and Todd’s9 study,
which examines users in a computer
resources centre. In the context of IB, Tanand Teo13 demonstrated that the intention
to adopt IB services could be predicted by
PBC factors.
Indirect antecedents of intention The three direct antecedents discussed above,
according to Ajzen and Fishbein,18 are
themselves determined by multiple salient
behavioural beliefs towards the behaviour.
For instance, according to Taylor and
Todd,
9,15
although attitude directly influencesbehavioural intention, attitude itself is
determined by multiple salient behavioural
beliefs. Along these lines, Rogers19 suggested
five attributes that are assumed to have an
effect on the rate of the adoption of an
innovation. They are (1) relative
advantage, (2) compatibility, (3) complexity,
(4) trialability and (5) observability. With
respect to those five innovation attributes,
Kautz and Larsen22 argue that the more
favourable an individual’s perceptions of
these attributes, the higher are the chancesof a successful adoption of an innovation.
Subjective norm itself is determined by
multiple salient behavioural beliefs based on
personal referents towards the behaviour.
Bearden et al 23 and Karahanna et al 24
categorized social influence (normative belief)
into two types, which are informational-
based influence and normative influence.
This study examines the effect of two types
of salient normative beliefs, the personal and
media salient beliefs. Rogers19
pointed outthat information about an innovation can be
actively sought by individuals once they are
aware that the innovation exists and also
when they know those sources or channels
that can provide further information about
the innovation. Rogers19 noted that the
importance of different channels or
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information sources about the innovation
is determined by their availability to the
potential adopter. Battacherjee10
demonstrated that SN is determined by
interpersonal influence (for example, word of
mouth). The mass media are often the most
rapid and efficient means of informing anaudience of potential adopters about the
existence of an innovation, that is, to create
awareness-knowledge.19 Furthermore, the
expected effects of mass media channels were
generalized by Rogers19 as relatively more
important at the knowledge stage of the
innovation-decision process.
According to Ajzen and Fishbein18 and
Taylor and Todd,9 although PBC directly
influences behavioural intention, PBC itself is
determined by multiple salient control beliefstowards the behaviour. The formal PBC
determinants in the ETPB are self-efficacy
(SE), the technology facilitating condition
and the resource facilitating condition. TPB
was extended (ETPB) to enable prediction of
behaviours that an individual may not be
able to perform at will. Thus, ETPB
incorporated perceptions of control over
performance of the behaviour as an
additional predictor (Ajzen).7 The following
sections will elaborate those determinants in
further detail.9 SE reflects a more complex process
involving the construction and orchestration
of adaptive performance to fit changing
circumstances.25 Recent studies (of IS) have
provided empirical support for the
relationship between SE and outcome
expectations. For instance, Hartzel26 showed
that computer SE is an important
determinant of an individual’s decision for
software adoption and use. The facilitating
conditions (FC) construct was originallyviewed as an external control related to the
environment.9,27 Therefore, understanding
the anticipated influence of FC is important
in studying human behaviour in IS and
especially in studies such as the ‘adoption of
IB’. FC originally have two dimensions:
resource factors (such as the time and money
needed) and technology factors regarding
compatibility issues that may constrain
usage.9 Early studies of IB adoption look
into factors that influence its adoption. For
example, Tan and Teo’s13 framework
investigates FC within two dimensions.
The two dimensions are the availability of government support and the availability
of technology support.
User ’ s Informational-based Readiness Factor The user ’s readiness for IB is a proposed
construct developed to take account of the
informational aspects related to the user ’s
behavioural intention that may affect an
adopters’ decision to accept or reject the
introduced innovation. The framework inthis study proposes four dimensions of the
‘User ’s Informational-Based Readiness’
construct. These exogenous variables are
‘awareness’,19,28 ‘knowledge’,19,28
‘experience’29 and ‘exposure’.30 Some of
these attributes are examined as a single
variable in the emerging field of IB. For
instance, Chang’s30 study undertook the
exposure attribute, whereas Karjaluoto et al 29
sought consumers’ experiences. The way this
study utilizes these attributes differs from
the previous studies in that it integrates thetwo attributes with a further two attributes
that are not yet studied widely in the field of
IB. The study defines UIBR as the potential
adopters’ assessment of their awareness,
knowledge, experience and exposure to the
related technologies available or recommended
by referents, which reflect their informational
abilities to adopt or reject the innovation. In
previous research, Dickerson and Gentry 31
indicated that adopters of home computers
are likely to be more active informationsearchers and have more previous experience
in the use of computers and other related
technologies. In Finland, Karjaluoto et al 29
found that attitude towards IB and actual
behaviour were influenced by prior
experience of computers and technology as
well as attitudes towards computers.
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Factors influencing Yemeni Bank users’ BI to use IB services
Furthermore, Sarel and Marmorstein32 noted
that prior experience with computers and
technology seems to be a key correlate of
early adoption. Similarly, Black et al 33
concluded that previous computer experience
is the main factor positively influencing the
adoption of e-banking services. Some authors(for example, Taylor and Todd15 ) point out
that exposure to and experience of related
products may increase perceived
compatibility.
METHODOLOGYThis study relies on the ‘Hypothetic-
Deductive methods’ in which a set of
quantitative approach rules are employed.
According to Sekaran,34 the hypothetico-
deductive method is a scientific methodwhereby researchers should establish testable
hypotheses and then try to falsify them,
rather than trying to confirm them directly
by accumulating favourable evidence. The
questionnaire was the instrument used for
data collection and the major items of the
survey were adapted from the prior
literature review as shown in Appendix D.
The scale adopted was a 7-point Likert
scale, ranging from 1 (strongly disagree)
to 7 (strongly agree). There are many
tools to measure behaviour, and ‘manyresearchers prefer to use a Likert-type
scale because it is very easy to analyze
statistically’.35 Likert scales, according to
Neuman,36 are ‘summative-rating or
additive scales because a person’s score
on the scale is computed by summing
the number of responses the person gives’
(p. 207). Owing to the difficulty in
obtaining a comprehensive and up-to-date
sample from the banks, convenience
sampling was used.One thousand questionnaires were self-
administered to the 14 banks in Sana’a,
Yemen. Fifty forms were assigned to each
of the 14 locations and the remaining 300
questionnaires were self-administered to bank
customers in a few industrial companies such
as Yemen Petroleum Company. This was
carried out, as these respondents are not able
to go to the banks during office hours
because of work restrictions. Six hundred
and twenty-three responses were received,
achieving a response rate of 62 per cent; of
these, 369 were satisfactorily complete and
useable for analysis with 254 having beenreturned incomplete. Thus, the gross
response rate of the research survey was
36.9 per cent.
A path-analysis approach, using the
Ordinary Least-Squares method (OLS), was
performed to test the proposed model37
facilitating testing of the cause and effect
among variables, as well as estimating the
direct and indirect effects of the variables
and understanding the magnitude and
direction of the relationships among themodel variables. The SPSS program and
multivariate techniques of Multiple Linear
Regression were used as a means of testing
the validity of the information obtained via
the procedures of data analysis.
ANALYSIS AND RESEARCHRESULTSPath analysis, according to Bryman and
Cramer,38 is an extension of multiple
regression procedures. It was developed as a
method for studying the direct and indirecteffects of variables hypothesized as causes of
variables treated as effects.39 Path analysis and
path coefficient are among the oldest terms
in causal analysis, where standardized s are
usually employed as estimates of causal
effects.37 The aim of path analysis is to
provide quantitative estimates of the causal
connections between sets of variables.
According to Bryman and Cramer,38 a direct
effect occurs when a variable has an effect on
another variable without a third variableintervening between them. An indirect effect
occurs when there is a third intervening
variable through which two variables are
connected. Along these lines, Pedhazur 39
points out that multiple regression analysis
can be viewed as a special case of path
analysis. Following Pedhazur ’s39 guidelines,
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the constructs in this study can be interpreted
as loadings in factor analysis, whereas the
paths can be interpreted as standardized beta
weights in regression analysis. In order to
illustrate the study further, path diagrams and
path coefficients are utilized. The path
diagram, according to Pedhazur,39 is veryuseful for displaying graphically the
hypothesized pattern of causal relations
among a set of variables. In line with
Bryman and Cramer,38 the arrows indicate
expected causal connections between
variables. Thus, in the diagram presented in
Figure 1, the study used upper case letters
and numerical figures to represent variables
in the model. Letters such as ‘I ’ refer to the
variable Intention, ‘R ’ User Informational-
Based Readiness, ‘ A ’ Attitude, ‘N ’ SubjectiveNorm and ‘C ’ Perceived Behavioural
Control. Meanwhile, numbers like ‘1’ refer
to the variable Relative Advantage/
Compatibility, ‘2’ Ease of use, ‘3’
Observability, ‘4’ Trialability, ‘5’ Personal
Norm, ‘6’ Mass Media Norm, ‘7’ Technology
Facilitating Condition, ‘8’ Resource
Facilitating Condition, ‘9’ Government
Support and ‘10’ Self-Efficacy.
In studying causal connections, the
researcher has to distinguish between
exogenous and endogenous variables.40 Therefore, all the variables represented by
numerical figures are examples of exogenous
variables, whereas those represented by
letters, with the exception of ‘R ’, are
endogenous variables. In Pedhazur ’s words:39
An exogenous variable is one whose
variation is assumed to be determined by
causes outside the hypothesized model.
Therefore, no attempt is made to explain
the variability of an exogenous variable
or its relations with other exogenousvariables. An endogenous variable …
is one whose variation is explained by
exogenous or other endogenous variables
in the model. (p. 770)
On the basis of this distinction of variables in
the path analysis, it is implied that variables
could be dependent and independent in the
same model. Kerlinger and Pedhazur 40
highlight some assumptions underlying the
application of path analysis as follows:
1. The relationships among the variables in
the model are linear, additive and causal.2. Residuals are not correlated with variables
preceding them in the model.
3. There is a one-way causal flow in the
system.
4. Variables are measured on an interval
scale (7-point Likert scale).
This study checks for the aforementioned
assumption required for using the application
of path analysis and there is no violation. In
addition, both the simple and multiple linear regressions employed in this study were
helpful in explaining the predictive power of
independent variables in direct relation. The
arrows in Figure 1 were drawn from the
independent variable (exogenous) to the
dependent variable (endogenous) . For instance,
the variable ‘attitude’ denoted by A is
conceived to be dependent on variables 1, 2,
3, 4 and variable R . Similarly, variable N is
conceived to be dependent on variables 5
and 6 and variable C is conceived to be
dependent on variables 7, 8, 9 and 10.Consequently, variable I is conceived to be
dependent on A , N , C and R . The diagram
in Figure 1 represents the a priori model.
As shown in the a priori model, variables
with a numerical symbol from one to 10,
including the variable R , are exogenous
variables, whereas the variables in uppercase
letters (I , A , N and C ) are endogenous
variables. Furthermore, an endogenous
variable treated as a dependent variable in
one set of variables may also be conceived asan independent variable in relation to other
variables.40 Along these lines, the path
coefficient indicates the direct effect of
variables taken as a cause of a variable
taken as an effect. The variable 1 is
exogenous and is, therefore, represented
by a residual (e 1 ).
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Factors influencing Yemeni Bank users’ BI to use IB services
Testing the full effects model toidentify significance pathsAccording to Kerlinger and Pedhazur,40 a set
of equations referred to as a recursive model
(p. 376)37 are required to assess the full
effects model and identify significant paths.
Recursive models, according to Cohen and
Cohen,37 can be estimated by ordinary
regression equations. In testing thehypotheses, this study performed a series
of multiple regressions to derive the
various path coefficients for the full effects
model and to identify significance paths
(Appendix A). A path analytic approach
using the OLS technique, which was utilized
to test the proposed model as recommended
by Cohen and Cohen37 is shown in
Figure 2. The relationships among the
variables in the recursive model are depicted
in a series equations as follows:
X e11
=
X e22
=
X e33
=
X e44
=
X e55
=
X e66
=
X e77
=
X e88
=
X e99
=
X e10 10=
XR e R
=
XA PA X PA X PA X
PA X PARXR e A
= + +
+ + +
1 1 2 2 3 3
4 4
XN PN X PN X e N
= + +5 5 6 6
XC PC X PC X PC X
PC X eC
= + +
+ +
7 7 8 8 9 9
10 10
XI PI X PIRXR PIAXA PA X
PI X PN X PINXN PI X
PI X P
= + + +
+ + + +
+ +
1 1 2 2
3 3 5 5 6 6
7 7 I I X PI X
PICXC PCI X e I
8 8 9 9
10 10
+
+ + +
The notions PA 1X 1, PN 5X 5, PC 7X 7,
PIAXA and so on denote a specific path
(1)(1)
(2)(2)
(3)(3)
(4)(4)
Variables Key
I: Intention,
R: User Informational Based Readiness
A: Attitude
N: Subjective Norm
C: Perceived Behavioural Control
1: Relative Advantage/Compatibility
2: Ease of use
3: Observability
4: Trialability5: Personal Norm
6: Mass Media Norm
7: Technology Facilitating Condition
8: Resource Facilitating Condition
9: Government Support
10: Self-Efficacy
Unverified direct effect
β AR
eI
eR
e5
eC
e6
e8
e7
e9
e10
e3
e4
e1
e2
β I P
β I R
β I N
β C10
β C9
β C8
C7
β IA
β N5
β N 6
β A3
β A4
β A1
β A2
I
5
2
N
A
C
9
8
10
7
3
6
1
4
R
eN
eA
Figure 1: A priori model (conceptual framework).
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coefficient. Thus, PA 1X 1 would indicate the
path coefficient relating the exogenous
variable X 1 to the endogenous variable A 1.
The full effects model is displayed in Figure 2
and the results of the series of regressions are
shown in Table 1.
In Pedhazur ’s words (p. 776):39
In path analysis, more than oneregression analysis may be called for.
At each stage, an endogenous variable
is regressed on the variables that are
hypothesized to affect it. The ’s thus
calculated are the path coefficients for
the path leading from the particular
set of independent variables to the
dependent variable under consideration.
The model in Figure 3 requires five regression
analyses for the calculation of all the path
coefficients. The path coefficient from R to I (P IR ) is calculated by regressing I on R , from A
to I (P IA ) is calculated by the regression of I on
A , from N to I (P IN ) is calculated by regressing
I on N and from C to I (P IC ) is calculated by
regressing I on C . For the purpose of this
research, a path is deemed significant if it passes
the 90 per cent confidence level.
Model revision to derive a trimmedmodelIn the model revision analysis, all non-
significant paths (identified through the
multiple regression carried out in the first part
of the analysis) were eliminated from the full
effects model. This step is necessary to derive
a more parsimonious model. Many
researchers, such as Pedhazur,39 prefer to use acriterion of meaningfulness for the deletion of
the paths, even when their coefficients are
statistically significant. Another round of
regression analysis was performed to derive
new regression statistics. The regressions were
carried out based on the following equations,
which represent the significant relationship
identified in the full effect model.
XI Pi X PiRXR PiAXA Pi X
Pi X PicXc Pi X ei
= + + +
+ + + +
1 1 3 3
7 7 10 10
XA PA X PA X PA X
PARXR e A
= + +
+ +
1 1 2 2 4 4 XA PA X PA X PA X
PARXR e A
= + +
+ +
1 1 2 2 4 4
(5)(5)
XN PN X PN X e N
= + +5 5 6 6 XN PN X PN X e N
= + +5 5 6 6 (6)(6)
XC PC X ec
= +10 10 XC PC X ec
= +10 10 (7)(7)
(8)(8)
Figure 2: Full effect model of causal path findings via linear regression analyses (LRA).Note : Numbers in parenthesis indicate zero-order correlation, whereas other numbers are path coefficients.
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Factors influencing Yemeni Bank users’ BI to use IB services
Consequently, the diagram in Figure 3
presents the results of the path analysis of thetrimmed model of this study. The trimmed
model result shows a lack of support for
the SN effect on BI. This is not consistent
with Ajzen’s theory,7 that potential adopters
intend to act based on others’ perceptions,
and also with the results reported by
Taylor and Todd9 , who found subjective
norms to be important in affecting adoption.
In contrast, the results of this study
are consistent with findings of previous
IB studies.1,2,13,41
The findings support theimportance of observability in directly
affecting a person’s intention to use IB in a
negative way.
Determining the indirect effects Following Pedhazur ’s suggestion,39 the
indirect effect of the exogenous variables
on the main endogenous variable ‘I ’,
behavioural intention, could be calculatedbased on path multiplication. The results
of path multiplication are displayed in
Appendix B.
Determining the total effects Results shown in Appendix C were obtained
by calculations reported in Figure 3. The
total indirect effect of an individual’s
behavioural beliefs on their Intention is equal
to the sum of four components (0.56), which
are composed of the products of standardizedregression coefficients. It is clear that
normative belief is virtually zero; a
potentially important finding that is obscured
only when the total indirect effect is
reported. The total indirect effect of an
individual’s behavioural beliefs on their
Intention was almost 0.10. Table 1 provides
PA4=0.085** (0.211*)
PAR=0.224*(0.600*)
PIN=0.083** (534*)
β I7=0.090**(0.384*)
Not significant
β i10= -0.089***(0.564*)
β i c.136*(620*)
eN eI
eR
e5
eC
e6
e8
e7
e9
e10
e3
e4
e1
e2
β I R.182*(0.657*)
β C10=0.444* (0.798*)
β IA.=592*(822*)
β N5=0.596*(0.714* )
βN6=0.196*(0.555*)
β i3= -0.060**(0.130**)
β A1=0.389*(0.716* )
β A2=0.240*(0.672*)
I
0.746
5
2
N
0.53
A
0.59
C
0.63
9
8
10
7
3
6
1
4
R
eA
β i1=0.161*(0.707* )
*** P<0.1
** P <0.05
Number in bold: Adjusted R2
Figure 3: Trimmed model.
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T a b l e
1 :
S u m m a r y a s s e s s m e n t o f r e s e a r c h h y p o t h e s e s
A n a l y s i s t e c h n i q u e s
C r i t e r i o n ( D V )
H y p o t h e s e s
P r e d i c t o r s ( I V )
S t a t i s t i c t e s t
R e s u l t s
t
S i g .
B e
t a
M u l t i p l e r e g r e s s i o n + F A
B e h a v i o u r a l
i n t e n t i o n ( B I )
H 1 , H 2
H 1 a
A t t i t u d e
1 7 . 8 0
0 . 0 0 0 * * *
0 . 6 5
3
S u p p o r t e d
H 1 b
S u b j e
c t i v e n o r m
2 . 8 5
0 . 0 0 4 * *
0 . 0 9
7
S u p p o r t e d
H 1 c
P B C
6 . 1 3
0 . 0 0 0 * * *
0 . 2 0
7
S u p p o r t e d
F a c t o r a n a l y s i s
H 3
H 4
B e h a
v i o u r a l b e l i e f s
—
—
—
S u p p o r t e d
H 5
N o r m
a t i v e b e l i e f s
—
—
—
S u p p o r t e d
H 6
C o n t r o l b e l i e f s
—
—
—
S u p p o r t e d
F a c t o r a n a l y s i s + M R
A t t i t u d e
H 4
H 4 a
R e l a t
i v e A d v a n t a g e / C o m p a t i b i l i t y
8 . 7 6 4
0 . 0 0 0 * * *
0 . 4 6
6
S u p p o r t e d
H 4 b
O b s e
r v a b i l i t y
− 0 . 6 2 7
0 . 5 3 1
− 0 . 0 2
6
R e j e c t e d
H 4 c
E a s e
o f u s e
6 . 2 3 0
0 . 0 0 0 * * *
0 . 3 2
5
S u p p o r t e d
H 4 d
T r i a l a
b i l i t y
1 . 6 5 3
0 . 0 9 9 *
0 . 0 6
8
R e j e c t e d
M R + F a c t o r a n a l y s i s
S u b j e c t i v e
n o r m ( S N )
H 5
H 5 a
P e r s o
n a l ( P R )
1 3 . 3 3
0 . 0 0 0 * * *
0 . 5 9
6
S u p p o r t e d
H 5 b
M e d i a ( M M )
4 . 4 0
0 . 0 0 0 * * *
0 . 1 9
6
S u p p o r t e d
H i e r a r c h i c a l r e g r e s s i o n +
F a c t o r a n a l y s i s
P B C H 6
H 6 a
F a c i l i t a t i n g t e c h n o l o g y ( F T )
5 . 5 5 5
0 . 0 0 0 * * *
0 . 1 0
9
( a )
H 6 b
F a c i l i t a t i n g r e s o u r c e ( F R )
3 . 8 1 5
0 . 0 0 0 * * *
0 . 6 6
2
( a )
H 6 c
G o v e
r n m e n t s u p p o r t G O V S P
2 . 3 4 7
0 . 0 1 9 * *
0 . 0 1
6
( a )
H 6 d
S e l f - e
f fi c a c y ( S E )
2 1 . 1 8 1
0 . 0 0 0 * * *
0 . 7 9
1
S u p p o r t e d ( b )
B e h a v i o u r a l
i n t e n t i o n ( B I )
H 7 a
A w a r e n e s s ( A W )
5 . 4 8 1
0 . 0 0 0 * * *
0 . 2 4
4
S u p p o r t e d
H 7 b
K n o w
l e d g e ( K W )
7 . 2 8 4
0 . 0 0 0 * * *
0 . 2 9
8
S u p p o r t e d
H 7 c
E x p e r i e n c e ( E X T )
7 . 4 3 4
0 . 0 0 0 * * *
0 . 3 6
3
S u p p o r t e d
H 7 d
E x p o s u r e ( E X P O S )
4 . 7 1 9
0 . 0 0 0 * * *
0 . 2 2
4
S u p p o r t e d
S t e p w i s e r e g r e s s i o n
B e h a v i o u r a l
i n t e n t i o n ( B I )
H 8 a
A t t i t u d e ( A T T )
1 5 . 7 6 3
0 . 0 0 0 * * *
0 . 5 9
2
S u p p o r t e d
H 8 b
U s e r ’ s i n f o r m a t i o n a l b a s e d r e a d i n e s s ( U I B R )
4 . 8 8 0
0 . 0 0 0 * * *
0 . 1 8
2
S u p p o r t e d
H 8 c
P B C
( P B C )
3 . 7 1 2
0 . 0 0 0 * * *
0 . 1 3
6
S u p p o r t e d
H 8 d
S u b j e
c t i v e n o r m s ( S N )
2 . 5 5 0
0 . 0 1 1 * *
0 . 0 8
3
S u p p o r t e d
* * * P < 0 . 0
0 1 ,
* * P < 0 . 0
5 ,
* P < 0 . 1 .
( a ) M a r g i n a l p o s i t i v e a n d
s i g n i fi c a n t r e l a t i o n s h i p s .
( b ) S i g n i fi c a n t a t P < 0 . 0
0 1 .
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Factors influencing Yemeni Bank users’ BI to use IB services
a summary of the entire results of the
hypotheses testing. It shows that 22 out of
the 24 main and sub-hypotheses were
supported. There are two hypotheses that
were rejected, and three that exhibited
marginal positive and significant relationships.
CONCLUSIONSThe findings of this study indicate that the
majority of the respondents are innovators
and early adopters of IB. In addition, the
relative advantages combined with
compatibility represent the IB attributes that
are of most interest to Yemeni bank
customers followed by ease of use (EOU).
The influence of both personal and media
referents shows up as a prominent
determinant of the SN. SE is a prominentdeterminant of PBC, whereas FC of
technology, resources and government
support are marginal predictors. Although
customers perceived that the Relative
Advantage/Compatibility and EOU are
significant and important attributes of IB, the
extent of actual usage of IB by the bank
customers in Yemen is still not strong. The
finding on the observability attribute of
conducting IB could lead one to conclude
that this innovation’s attribute is an undesired
attribute for IB, which negatively affects thecustomers’ intention to adopt IB. It was
noted that customers’ intention to adopt IB
will be influenced by both personal and
media norms.
The test of generalizability of the results
conducted in this research used both statistical
tools (spilt sample, and Adjusted R 2 )42 and
the findings of previous research.42 The test
of generalizability could lead one to conclude
that SN is the weakest psychological
determinant of Intention in this study withrespect to the IB adoption in Yemen,
whereas it could lead one to conclude that
attitudes, readiness and PBC are prominent
direct predictors of IB. This study
has fulfilled both the objectives of the
research and supported TPB. The decrease
of awareness, knowledge, experience and
exposure in the adopter led to the decrease
of intention to use the IB service. As the
number of actual users of IB was very
limited at the time of this study, further
promotion is required to make customers
aware of the existence of IB. This research
has highlighted that UIBR is an importantfactor that must be considered in research
adoption. Although there have now been
quite a lot of papers on the adoption of IB,
this study contributes to the body of
knowledge by giving much focus on a new
integrated approach of user ’s readiness that
gives much concern/pays great attention to
investigating the user ’s informational and
psychological readiness to use technology.
In this study, we look into the adoption of
use and integration of both UIBR andpsychological readiness to accept IB services.
Furthermore, the study benefited from DOI,
which provides the widely applied concepts
of DOI by modelling the attributes of
innovation as part of attitudinal belief and
the communication channels as part of
normative belief.
The contribution of this study can be
viewed from two perspectives, that is
theoretical as well as managerial.
Subsequently, there are four theoretical
implications. First, the study empiricallyillustrated that SN acts as a second-order
formative structure, formed by two distinct
dimensions: (personal and media referents),
and accordingly, normative belief is viewed
as a two-dimensional construct, which allows
a more detailed examination of external
normative beliefs. Previous studies were
concerned only with the influence of
referents that rely on person-to-person
interaction (personal).
Second, this study provides anunderstanding of the nature and role of PBC
which, according to Pavlou and Fygenson,43
is still not well understood. Third, the
empirical data affirm that UIBR is a new
predictor of an individual’s behavioural
intention to use IB. Fourth, a formative
structure permits a more detailed prediction
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Zolait
of external, behavioural, normative and
control beliefs by allowing a distinct
prediction of (1) IB attributes as perceived
by the respondents and their role in the
prediction of an individual’s attitude in
formative structure, (2) personal and media
referents which, in turn, contribute to abetter prediction of SN and (3) SE and FC
(controllability), which lead to a better
prediction of PBC, and behavioural
intention.
In terms of the managerial contribution,
the study has shown that managers have to
be prepared to provide financial services via
the Internet, as there are customers who
prefer to conduct IB. In addition, offering IB
would also mean that Yemeni banks would
be able to expand their customer base.Furthermore, the study proved that personal
interaction is influential in shaping an
individual’s SN to adopt IB. Therefore,
banks should focus on personal referents to
increase the number of IB adopters among
their customers. In addition, the banks
should use MM as a channel of interaction
with individuals in their effort to increase IB
users.
Despite the various contributions, the
study has two main limitations. One is the
sample size. It was difficult to get bankcustomers to respond to the survey; hence,
the targeted sample of 1000 was not
achievable. A larger sample would enhance
the results statistically. Future studies should
incorporate more respondents. The second
limitation is the target population. The
study concentrated only on certain regions
in Yemen and, hence, may not be
representative of the entire Yemeni
population. Future studies should expand
to other regions as well as other MiddleEast countries. In addition, it is proposed
that comparative studies between developed
and developing countries and between
different regions of the world be
conducted using the framework used
in this study to further validate the existing
findings.
REFERENCES1 Cyree, k., Delcoure, N. and Dickens, R. (2009) An
examination of the performance and prospects for the
future of internet-primary banks. Journal of Economics and
Finance 33(2): 128 – 147.
2 Mattila, M., Karjaluoto, H. and Pento, T. (2003) Internet
banking adoption among mature customers: Early
majority or laggards. Journal of Services Marketing 17(5):
514 – 528.
3 Lu, M., Liu, C., Jing, J. and Huang, L. (2005) Internet
banking: Strategic responses to the accession of WTO by
Chinese banks. Industrial Management & Data System
105(4): 429 – 442.
4 Sahut, J. and Kucerova, Z. (2009) Enhanced internet
banking service quality with quality function deployment
approach. Journal of Internet Banking and Commerce ,
http://www.arraydev.com/commerce/jibc/0311-09.htm.
5 Kamel, S. (2009) The use of information technology to
transform the banking sector in developing nations.
Information Technology for Development 11(4): 305 – 312.
6 Cheng, T.C., Lam, D. Y. and Andy, C. Y. (2006)
Adoption of internet banking: An empirical study
in Hong Kong. Decision Support Systems 42(3):
1558 – 1572.
7 Ajzen, I. (1991) The theory of planned behaviour .
Organizational Behaviour and Human Decision Processes
50(2): 179 – 211.
8 Mathieson, K. (1991) Predicting user intention: Comparing
the technology acceptance model with the theory of planned
behaviour . Information Systems Research 2(3): 173 – 191.
9 Taylor , S. and Todd, P.A. (1995a) Understanding
information technology usage: A test of competing
model. Information Systems Research 6(2): 144 – 176.
10 Battacherjee, A. (2000) Acceptance of e-commerce
services: The case of electronic brokerages. IEEE
Transactions on Systems, Man and Cybernetics – Part A:
Systems and Humans 30(4): 411 – 420.
11 Lim, J., Gan, B. and Chang, T.-T. (2002) A survey onNSS adoption intention. Proceedings of the 35th Hawaii
International Conference on System Sciences; 7 – 10
January 2002, pp. 399 – 408, http://www.ieee.org/portal/
site, accessed 6 May 2004.
12 Shih, Y. and Fang, K. (2004) The use of a decomposed
theory of planned behaviour to study internet banking in
Taiwan. Internet Research 14(3): 213 – 223.
13 Tan, M. and Teo, T.S.H. (2000) Factors influencing the
adoption of internet banking. Journal for Association of
Information System 1(5): 1 – 42, http://portal.acm.org/
citation.cfm?id:374134.
14 ITU. (2009) International telecommunication union:
News related to ITU telecommunication/ICT statistics,
http://www.itu.int/ITU-D/ict/newslog/Internet+
Subscribers+Rise+36+In+2008+Yemen.aspx, accessed31 July 2009.
15 Taylor , S. and Todd, P. (1995b) Decomposition and
crossover effects in the theory of planned behaviour:
A study of consumer adoption intentions. International
Journal of Research in Marketing 12(2): 137 – 155.
16 Zolait, A.H., Mattila, M. and Sulaiman, A. (2009)
The effect of user’s informational-based readiness on
innovation acceptance. International Journal of Bank
Marketing 27(1): 76 – 100.
8/8/2019 Mr Project Article
http://slidepdf.com/reader/full/mr-project-article 14/20
© 2010 Macmillan Publishers Ltd. 1363-0539 Journal of Financial Services Marketing Vol. 15, 1, 76–94 89
Factors influencing Yemeni Bank users’ BI to use IB services
17 Courter , E. (1999) Home banking missteps. Credit Union
Management 22(3): 10 – 12.
18 Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes
and Predicting Social Behaviour . Englewood Cliffs, NJ:
Prentice-Hall.
19 Rogers, E.M. (1995) Diffusion of Innovations , 4th edn.
New York: Free Press, Collier Macmillan.
20 Doll, J. and Ajzen, I. (1992) Accessibility and stability
of predictors in the theory of planned behaviour . Journal of Personality and Social Psychology 63(5):
754 – 765.
21 Ajzen, I. (2001) Nature and operation of attitudes.
Annual Review of Psychology: Pro Quest Medical Library 52(1):
27 – 58.
22 Kautz, K. and Larsen, E. (2000) Diffusion theory and
practice: Disseminating quality management and software
process improvement innovations. Information Technology
& People 13(1): 11 – 26.
23 Bearden, W.O., Calcich, S.E., Netemeyer , R. and Teel,
J.E. (1986) An exploratory investigation of consumer
innovativeness and interpersonal influences. Advances in
Consumer Research 13(1): 77 – 82.
24 Karahanna, E., Straub, D.W. and Chervany, N.L. (1999)
Information technology adoption across time: A cross-
sectional comparison of pre-adoption and post-adoption
beliefs. MIS Quarterly 23(2): 183 – 213.
25 Gist, M.E. and Mitchell, T.R. (1992) Self-efficacy: A
theoretical analysis of its determinants and malleability.
Academy of Management: The Academy of Management
Review, Briarcliff Manor 17(2): 183 – 211.
26 Hartzel, K. (2003) How self-efficacy and gender issues
affect software adoption and use. Communications of the
ACM 46(9): 167 – 171.
27 Triandis, H.C. (1980) Values, attitudes, and interpersonal
behaviour . In: M.M. Page (ed.) Nebraska Symposium on
Motivation , 1979: Beliefs, Attitudes, Values Lincoln, NE:
University Nebraska Press, pp. 195 – 259.
28 Hall, G., George, A. and Rutherford, W. (1977)
Measuring stages of concern about the innovation: A
manual for use of the soc questionnaire. Research and
development centre for teacher education. Austin, TX:
Southwest Educational Development Laboratory (SEDL),
p. 104, http://www.eric.ed.gov/ERICDocs/data/
ericdocs2sql/content_storage_01/0000019b/80/33/88/f5
.pdf .
29 Karjaluoto, H., Mattila, M. and Pento, T. (2002) Factors
underlying attitude formation towards online banking in
Finland. International Journal of Bank Marketing 20(6):
261 – 272.
30 Chang, Y.T. (2004) Dynamics of banking technology
adoption: An application to internet banking. Warwick
Economic Research Papers. Coventry, UK: Department
of Economics, University of Warwick.
31 Dickerson, M.D. and Gentry, J.W. (1983) Characteristics
of adopters and non-adopters of home computers. Journal
of Consumer Research 10(2): 225 – 235.
32 Sarel, D. and Marmorstein, H. (2003) Marketing online
banking services: The voice of the customer . Journal of
Financial Services Marketing 8(2): 106 – 118.
33 Black, N. J., Lockett, A., Winklhofer , H. and Ennew, C.
(2001) The adoption of internet financial services: A
qualitative study. International Journal of Retail &
Distribution Management 29(8): 390 – 398.
34 Sekaran, U. (2003) Research Methods for Business: A Skill
Building Approach , 4th edn. New York: John Wiley &
Sons.
35 Jackson, S.L. (2006) Research Methods and Statistics: A
Critical Thinking Approach , 2nd edn. Belmont, CA:Wadsworth.
36 Neuman, W.L. (2006) Social Research Methods: Qualitative
and Quantitative Approach , 5th edn. Boston, MA: Allyn &
Bacon.
37 Cohen, J. and Cohen, P. (1983) Applied Multiple
Regression/Correlation Analysis for the Behavioural Sciences ,
2nd edn. New Jersey: Lawrence Erlbaum Associates.
38 Bryman, A. and Cramer , D. (2001) Quantitative Data
Analysis with SPSS Release 10 for Windows: A Guide for
Social Scientists. Philadelphia, PA: Taylor & Francis
Group; Routledge.
39 Pedhazur , E. J. (1997) Multiple Regression in Behavioural
Research: Explanation and Prediction , 3rd edn. Florida:
Thomson Learning.
40 Kerlinger , F.N. and Pedhazur , E. J. (1973) Multiple
Regression in Behavioural Research. New York: Holt,
Rinehart and Winston.
41 Liao, S., Yuan, P.S., Huaiqing, W. and Ada, C. (1999)
The adoption of virtual banking: An empirical study.
International Journal of Information Management 19(1):
63 – 74.
42 Hair , J.F., Black, W.C., Babin, B. J., Anderson, R.E. and
Tatham, R.L. (2006) Multivariate Data Analysis , 6th edn.
Upper Saddle River, NJ: Prentice Hall international.
43 Pavlou, P.A. and Fygenson, M. (2006) Understanding
and predicting electronic commerce adoption an
extension of the theory of planned behaviour . MIS
Quarterly 30(1): 115 – 143.
44 Venkatesh, V. and Davis, F.D. (2000) A theoretical
extension of the technology acceptance model: Four
longitudinal field studies. Management Science 45(2):
186 – 204.
45 Moore, G.C. and Benbasat, I. (1991) Development of an
instrument to measure the perceptions of adopting an
information technology innovation. Information Systems
Research 2(3): 192 – 222.
46 Laforet, S. and Li, X. (2005) Consumers’ attitudes
towards online and mobile banking in China. International
Journal of Bank Marketing 23(5): 362 – 380.
47 Pedersen, P.E. (2005) Adoption of mobile internet
services: An exploratory study of mobile commerce early
adopters. Journal of Organizational Computing & Electronic
Commerce 15(3): 203 – 222.
48 Lassar , W., Manolis, C. and Lassar , S.S. (2005) The
relationship between consumer innovativeness, personal
characteristics, and online banking adoption. International
Journal of Bank Marketing 23(2): 176 – 199.
49 Dandapani, K., Karels, G.V. and Lawrence, E.R. (2008)
Internet banking services and credit union performance.
Managerial Finance 34(6): 437 – 446.
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APPENDIX ASee Table A1.
Table A1: Regression results: Predicting overall behavioural intention by psychological determinants and UIBR
Independent variable B t R 2 F p
DV1 – Behavioural intention (BI) — — 0.735 252.101 0.000Constant − 3.504 − 3.384 — — 0.001IV1 – Attitude (ATT) 0.790 15.763 — — 0.000IV2 – Readiness (UIBR) 0.101 4.880 — — 0.000IV3 – PBC (PBC) 0.128 3.712 — — 0.000IV4 – Subjective norms (SN) 0.064 2.550 — — 0.011
Model R R 2 Adj. R 2 F p
Summary table 1 0.822 0.676 0.675 764.538 0.0002 0.847 0.718 0.716 465.059 0.0003 0.854 0.730 0.728 329.006 0.0004 0.857 0.735 0.732 252.101 0.000
Independent variable B t R 2 F p
DV2 – Attitude (ATT) — — 0.562 234.693 0.000
Constant 6.380 8.361 — — 0.000IV1 – Relative advantage / compatibility(RAC)
0.247 9.600 — — 0.000
IV2 – Ease of use (EOU) 0.279 6.375 — — 0.007
Model R R 2 Adj. R 2 F P
Summary table 1 0.716 0.513 0.512 386.958 0.0002 0.750 0.562 0.559 234.693 0.000
Independent variable B T R 2 F P
DV3 – Subjective norms (SN) — — 0.535 210.169 0.000Constant 10.821 13.774 — — 0.000IV1 – Personal referent (PR) 0.078 13.334 — — 0.000IV2 – Media referent (MM) 0.047 4.396 — — 0.000
Model R R 2
Adj. R 2
F P Summary table 1 0.714 0.510 0.509 381.947 0.0002 0.731 0.535 0.532 210.169 0.000
Independent variable B T R 2 F P
DV3 – PBC (PBC) — — 0.192 28.906 0.000Constant 13.120 11.319 — — 0.000IV1 – Technology-facilitating condition
(TFC)0.070 5.555 — — 0.000
IV2 – Resource-facilitating condition(RFC)
0.051 3.815 — — 0.000
IV3 – Government support (GOVSP) 0.024 2.347 — — 0.019
Model R R 2 Adj. R 2 F P
Summary table 1 0.372 0.138 0.136 58.921 0.0002 0.424 0.180 0.175 40.111 0.0003 0.438 0.192 0.185 28.906 —
Independent variable B T R 2 F P
DV3 – PBC (PBC) — — 0.637 643.747 0.000Constant 8.033 12.498 — — 0.000IV1 – Self-efficacy (SE) 0.112 25.372 — — 0.000
P < 0.05.
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Factors influencing Yemeni Bank users’ BI to use IB services
APPENDIX BSee Table B1.
APPENDIX CSee Table C1.
APPENDIX DSee Table D1.
Table B1: Path analysis indirect effects
Cause / effects Indirect paths Path coefficient
Behavioural belief / intention X1; XA; XI (0.389×0.592)=0.230288
X2; XA; XI (0.240×0.592)=0.14200X4; XA; XI (0.085×0.592)=0.05032XR; XA; XI (0.224×0.592)=0.132608
Normative belief / intention X5; XN; XI (0.596×0.083)=0.049468X6; XN; XI (0.196×0.083)=0.016268
Control belief / intention X10; XC; XI (0.798×0.124)=0.098952
Table C1: The total effects of behavioural belief, normative belief and control belief on the behavioural intention
Cause / effects Indirect effect Direct effect Total effect
Behavioural belief / intention + 0.230288 0.161 —+ 0.14200 − 0.060 —+ 0.05032 — —+ 0.132608 — —
Total 0.555216 0.10 0.655216
Normative belief / intention + 0.049468 — —+ 0.016268 — —
Total 0.065736 0.00 0.065736
Control belief / intention 0.098952 − 0.089 0.075688
Table D1: Questionnaire items in a 7-point Likert scale
Q. No. Items (in a 1– 7 Likert scale: Strongly disagree (1) and strongly agree (7))
Models references
Intention (I) INT1 Given the chance, I predict that I would use Internet banking (IB)
in the future to achieve my banking activities.Venkatesh and Davis;44
Mathieson;8 Shih and Fang12
INT2 I will strongly recommend others to use IB.INT3 My favourable intention would be to use (IB) rather than my
(traditional banking) for my banking practice.INT4 I plan to use IB.INT5 When I have access to the IB system, I intend to use it.
Attitude (A) ATT 1 In my opinion, using the IB services is a good idea.ATT 2 I think it is a wise idea for me to use the IB services.ATT 3 I like the idea of using the IB services.ATT 4 Using the IB services would be a pleasant experience.
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Table D1: Continued
Q. No. Items (in a 1– 7 Likert scale: strongly disagree (1) and strongly agree (7))
Models references
Relative advantage (RA) RA1 If I were to use IB, it would enable me to accomplish my
tasks more quickly.Moore and Benbasat;45
Karahanna et al 24 RA2 If I were to use IB, the quality of my work would improve.
RA3 If I were to use IB, it would enhance my effectiveness on the job.RA4 If I were to use IB, it would make my job easier.RA5 Using IB gives me greater control over my work.
Complexity (PEOU) COX1 Learning to operate IB would be easy for me. Moore and Benbasat;45
Karahanna et al ;24 Tan and Teo13
COX2 Overall, if I were to use IB, it would be easy to use.COX3 It would be easy for me to become skilful at using IB.COX4 I believe that it is easy to get IB to do what I want it to do.COX5 If I were to use IB, it would be (not available) difficult to use.COX6 Using IB requires a lot of mental effort.
Compatibility (COMPT) COM1 If I were to use IB, it would be compatible with most aspects
of my work.Moore and Benbasat;45
Karahanna et al; 24
Tan and Teo13 COM2 If I were to use IB, it would fit my work style.COM3 If I were to use IB, it would fit well with the way I like to work.
Trialability (TR) TR1 Before deciding on whether or not to use IB, I want to be able to
use it on a trial basis.Moore and
Benbasat;45 Karahannaet al ;24 Tan and Teo13
TR2 Before deciding on whether or not to use IB, I want to be able toproperly try it out.
TR3 I want to be permitted to use IB, on a trial basis long enough tosee what it can do.
Observability (OBSRV): If the bank introduces IB service OBS1 I will use it when many use it. Karahanna et al 24 OBS2 I will use it when I have seen others using IB.
OBS3 I will use it as soon as I get to know about it.OBS4 I will use it if this service becomes popular.OBS5 I will wait until other customers start to use it.OBS6 I will use it when other people have successful experience
of using it.OBS7 If IB is unknown to me, I will not use it.
Subjective norm (SN) SN1 Most people, who are important to me would think that I should
use IB to get bank services.Taylor and Todd;15
Shih and Fang12 SN2 The people who influence my decisions would think that I should
use IB.SN3 Most people who are important to me would think that I should try
out the bank’s website to get access to the bank’s IB.SN4 The people who influence my decisions would think that I should try
out the bank’s website to get access to the bank.SN5 Most people who are important to me would think that using IB is a
good idea.SN6 Most people who are important to me would think I should use IB.
Perceived behavioural control PBC PBC1 I would be able to use IB. Taylor and Todd9 PBC2 I have the resources necessary to make use of IB.PBC3 I have the knowledge necessary to make use of IB.PBC4 I have the ability to make use of IB.PBC5 Using IB would be entirely within my control.
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Factors influencing Yemeni Bank users’ BI to use IB services
Table D1: Continued
Q. No. Items (in a 1– 7 Likert scale: Strongly disagree (1) and strongly agree (7))
Models references
UIBR attribute Awareness
AW1 I do not even know what IB is. Hall et al 28 AW2 I am not concerned about IB.
AW3 I am completely occupied with other things.AW4 Although I do not know about IB, I am concerned about thingsin the area.
AW5 At this time, I am not interested in learning about IB.
Knowledge KW1 I have a very limited knowledge of IB. Hall et al 28 KW2 I would like to discuss the possibility of using IB.KW3 I would like to know what resources are available if I want decide to
adopt IB.KW4 I would like to know what the use of IB will require in the immediate
future. .KW5 I would like to know how this innovation is better than other banking
innovation.
Experience EXP1 I have a great deal of experience using the Internet. Laforet and Li;46 Karjaluoto
et al 29 EXP2 I have a great deal of experience using computers.EXP3 I have a great deal of experience using personal banking.
Exposure Expo1 I have seen advertisements recommending the use of IB. Chang30 Expo2 I have used IB before.Expo3 I have been exposed to a recommendation to use IB.
Normative influences (NB × MC) Personal reference
MCPER1 Peers / colleagues think that I should use IB and I will do whatpeer / colleagues suggest I do.
Taylor and Todd15
MCPER2 Peers / colleagues think that I should try out IB and I will do whatpeer / colleagues suggest I do.
MCPER3 Opinion leaders think that I should use IB and I will do what leaders
suggest I do.MCPER4 Opinion leaders think that I should try out IB and I will do whatleaders suggest I do.
MCPER5 Bank’s employees think that I should use IB and I will do what thebank’s people suggest I do.
MCPER6 Bank’s employees think I should try out IB and I will do what thebank’s people suggest I do.
Media reference MCMEDIA1 The media suggest using IB is a good idea and I will do what the
media suggest.Pedersen;47 Battacherjee10
MCMEDIA2 The media consistently recommend using IB services and I will dowhat the media suggest.
MCPRFS3 For my profession, it is advisable to use IB services and I will dowhat it suggests.
MCMEDIA4 I read / saw news reports that using IB is a good way of managingmy bank account and I will do what the media suggest.
Self-efficacy DSE1 I feel comfortable using IB on my own and for me this aspect is
important.Lassar et al 48
DSE2 I can easily operate IB from the bank’s website on my own and forme this aspect is important.
DSE3 I can use IB without others’ help and for me this aspect is important.
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Table D1: continued
Q. No. Items (in a 1– 7 Likert scale: Strongly disagree (1) and strongly agree (7))
Models references
Government support FGS1 Government supports e-commerce. Tan and Teo13 FGS2 Government endorses e-commerce.FGS3 Setting up the facilities to enable e-commerce.
FGS4 Government promotes e-commerce.
Facilitating resources FR1 Facilitate computers for everyone to use IB services. Taylor and Todd9 FR2 Facilitate access to the Internet at low prices to make it affordable to
use IB.FR3 Facilitate IB availability then I would be able to use IB when I need it.FR4 I have the time to set up IB services.FR5 I have enough money to use IB services.
Facilitating technology conditions FT1 I have the computers, Internet access and applications needed to
use IB.Taylor and Todd;9
Shih and Fang12 FT2 Facilitate the bank’s IB transactional websites to use IB.FT3 Facilitate a good quality of Internet connection to use IB.FT4 Facilitate high quality of Internet wireless to use IB.
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