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CHAPTER V
ANALYSIS & INTERPRETATION
In this chapter the analysis of the data collected based on the
frame of reference of this thesis is presented. First the empirical
analysis of the proposed theoretical model using SEM is presented
followed by demographic profile of the respondents. The chapter
concludes by analyzing the demographic influences of consumers on
their intention to use internet banking.
5.1 Introduction to Analysis and Interpretation
To empirically validate the extended TAM model, Structural
Equation Modeling (SEM) was used and hypotheses one to twenty
were tested through the Structural Equation Modeling using AMOS
18. One way ANOVA was used for examining differences in consumer
intention to use internet banking across select demographic variables,
thereby testing hypothesis twenty one. Multiple regression was used
to find out the influence of select demographic variables on consumer
intention to use internet banking and tests hypothesis twenty two.
The following section briefly describes an introduction to
Structural Equation Modeling including the basic concepts of
Structural Equation Modeling and moves on to present the
psychometric checks done using the measurement model of SEM and
the analysis results of the hypotheses testing done using the
structural model. This is followed by the analysis of select
demographic influence on internet banking usage intention.
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5.2 Basic Concepts of SEM: An Introduction
Structural Equation Modeling (SEM) is a multivariate technique,
which estimates a series of inter-related dependence relationships
simultaneously. The term Structural Equation Modeling conveys that
the causal processes under study are represented by a series of
structural (i.e. regression) equations, and that these can be modeled
pictorially to enable a clearer conceptualization of the study. The
hypothesized model can be tested statistically in a simultaneous
analysis of the entire system of variables to determine the extent to
which it is consistent with the data. If the goodness-of-fit is adequate,
the model argues for the plausibility of postulated relations among the
variables. Given below are some of the basic concepts of SEM and a
few terms which are used in the analysis.
5.2.1 Latent and Observed Variables
With regard to the measurement instrument, the variables are
classifies as latent and observed variables. Latent variables are not
observed directly. They are operationally defined in terms of behavior
believed to represent it. The measured scores (measurements) are
termed as observed or manifest variables, and they serve as indicators
of the underlying construct which they presume to represent. Hence
one latent variable has three or four statements (observed variables) to
represent it.
5.2.2 Exogenous and Endogenous Latent Variables
Exogenous latent variables are synonymous with independent
variables; they ‘cause’ fluctuations in the values of other latent
variables in the model. Endogenous latent variables are synonymous
with dependent variables and, as such, are influenced by the
exogenous variables in the model, either directly or indirectly.
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5.2.3 The Factor Analytic Model
Factor analysis is one of the oldest and best known statistical
procedures for investigating relationship between sets of observed and
latent variables. In using factor analysis, the researcher examines the
co-variation among a set of observed variables in order to gather
information on their underlying latent constructs (i.e. factors).
There are two basic types of factor analysis: Exploratory Factor
Analysis (EFA) and Confirmatory Factor Analysis (CFA). The factor
analytic model (EFA or CFA) focuses solely on how, and the extent to
which, the observed variables are linked to their underlying latent
factors. Specifically speaking, it is concerned with the extent to which
observed variables are generated by the underlying latent constructs
and thus strength of the regression paths from the factors to the
observed variables (the factor loadings) are of primary interest.
Exploratory Factor Analysis is designed for situations where
links between the observed and latent variables are unknown or
uncertain. Hence after the formulation of questionnaire items, an EFA
will be conducted to determine the extent to which the item
measurements are related to the latent constructs.
In contrast, Confirmatory Factor Analysis (CFA) is used when
the researcher postulates relations between the observed measures
and the underlying factors ‘a priori’, based on knowledge of the theory,
empirical research, or both, and then tests this hypothesized
structure statistically. Because the CFA model focuses solely on the
link between factors and their measured variables, within the
framework of SEM, it represents what is called as a measurement
model. In this study, the model was developed ‘a priori’, hence only
the CFA was used.
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5.2.4 The Process of Statistical Modeling
The model in this study was based on the Technology
Acceptance Model. First the model was specified and the researcher
tested the plausibility of the model based on sample data that
comprised of all observed variables in the model. The primary task in
this model testing procedure was to determine the goodness-of-fit
between the hypothesized model and the sample data. As such the
structure of the hypothesized model was imposed on the sample data
to test how well the observed data fits this restricted structure.
Because it is highly unlikely that a perfect fit will exist between the
observed data and the hypothesized model, there will be a differential
between the two which is called as the ‘residual’.
According to Joreskog (1993), the general strategic framework
for testing Structural Equation Models could be strictly confirmatory
(SC), alternative models (AM) and model generating (MG). This study
adopts the strictly confirmatory scenario.
5.2.5 SEM Assumptions and Requirements
The major assumptions of Structural Equation Modeling (SEM)
are as follows:
All the four levels of measurement (Nominal, ordinal, interval
and ratio scales) can be used.
Either a variance-covariance or correlation data matrix derived
from a set of observed or measured variables can be used. But a
covariance matrix is preferred. In other words,
S = , then model fits the data, where
S= Empirical/ observed/ sample variance/ covariance matrix
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= Model implied variance / covariance matrix
SEM deals with data in the variance- covariance matrix as
shown below in table 5.1.
Table 5.1 Variance and Covariance Matrix of SEM
x1 x2 y
x1 Var ( x1 )
x2 Cov (x1, x2 ) Var (x2)
y Cov (x1, y) Cov (x2, y) Var (y)
If correlation matrix is used, the following correlation
coefficients are calculated:
o product moment correlation – when both variables are
interval
o Phi-coefficient – when both variables are nominal
o Tetra choric coefficient – When both variables are
dichotomous
o Polychoric coefficient- When both variables are ordinal
o Point-biserial coefficient – when one variable is interval
and other is dichotomous
o Poly-serial coefficient – when one variable is ordinal
and the other is interval variable
Latent variables are smaller than the number of measured
variables.
Data are normally distributed. Here, the usual univariate
normality checks are made by analyzing the skewness and
kurtosis of each variable. In case of non-normality, one has
to look for outliers and transformation of data. Tests such as
Mardia-Statistic can be used for checking the multivariate
normality of all the variables considered together (Bentler
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and Hu, 1995). The Satorra –Bentler statistic (Satorra and
Bentler, 1988, 1994) or the use of item parcels (subscales in
the scale) and transformation of non-normal variables (West,
Finch and Curran, 1995) can also be adopted.
SEM assumes a linear relationship among the indicators of
measured variables. In case of non-linear relationships,
Kenny –Judd model (Kenny and Judd, 1984) can be used.
Even though a consensus has not been reached on the issue
of sample size, a large sample size is required. Different
authors have suggested different sample sizes as discussed
earlier. However it is recommended that any sample less
than 150 may not produce reliable estimates.
There is a stochastic relationship between exogenous and
endogenous latent variables. That is, not all of the variation
in the dependent variable is accounted for by the
independent latent variable (Kunnan, 1998).
5.2.6 Basic Composition of SEM
As mentioned earlier, in SEM there are two models: the
Measurement model and the structural model.
The measurement model defines relations between the observed
and unobserved variables. It provides the link between scores on a
measuring instrument (i.e. the observed indicator variables) and the
underlying constructs they are designed to measure. The
measurement model represents therefore the Confirmatory Factor
Analysis (CFA), in that it specifies the pattern by which each measure
loads on a particular factor. It concentrates on validating the model
and does not explain the relationships between constructs. It
represents how the measured variables come together to represent
constructs and is used for validation and reliability checks. In other
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words CFA is a way of testing how well the measured variables
represent a particular construct. The purpose of CFA is twofold:
1) It confirms a hypothesized factor structure
2) It is used as a validity procedure in the measurement model
On the other hand, the structural model defines relations
among the unobserved variables. Accordingly it specifies the manner
by which particular latent variables directly or indirectly influence (i.e.
‘cause’) changes in the values of certain other latent variables in the
model. Therefore it is concerned with how constructs are associated
with each other and is used for hypotheses testing.
In this study data was analyzed using Anderson and Gerbing’s
(1988) two step approach whereby the estimation of the confirmatory
measurement model precedes the estimation of the structural model.
Before evaluating the model fit, it is necessary to present the
analysis of the psychometric properties of the instrument using the
measurement model. The next section does so by presenting the
validation and reliability checks of the instrument.
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5.3 Validation of the Measurement Model: Psychometric Checks
A Confirmatory Factor Analysis (CFA) was conducted using
AMOS 18. Measurement model validity depends on establishing
acceptable levels of goodness-of–fit for the measurement model and
finding specific evidence of construct validity. Validity is defined as the
extent to which data collection methods accurately measure what they
were intended to measure (Saunders and Thornhill, 2003). To satisfy
the validity procedure, the following are the validity and reliability
checks that were carried out:
Content validity
Convergent validity
Composite Reliability
Discriminant validity
Nomological validity
The content validity and nomological validity of the research
model have already been presented in chapter four under
methodology. The other psychometric property checks of the
instrument are presented here.
5.3.1 Convergent Validity
Convergent validity is shown when each measurement item
correlates strongly with its assumed theoretical construct. In other
words the items that are the indicators of a construct should converge
or share a high proportion of variance in common. The value ranges
between zero and one (0 – 1) .The ideal level of standardized loadings
for reflective indicators is 0.70 but 0.60 is considered to be an
acceptable level (Barclay et al., 1995).
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Convergent validity was verified through the t-statistic for each
factor loading. All factor loadings are greater than 0.70 and range
from 0.77 to 0.92. The standardized factor loadings (λ) of construct
items of the measurement model are presented in table 5.2
Table 5.2 AMOS Output Extract: Standardized Factor Loadings of
Construct Items
No Construct statements Standardized factor loadings
(λ)
Perceived Usefulness
1 Internet banking enables people to conduct
financial transactions more quickly.
0.849
2 Internet banking improves one’s effectiveness in
conducting banking transactions.
0.853
3 Internet banking makes it easier to conduct
banking transactions.
0.879
4 Internet banking provides convenience since it is
available 24 hours, 7 days of the week.
0.840
5 Internet banking saves time compared to
traditional banking.
0.838
Perceived Ease of Use
6 It would be easy for me to become skilful at using
internet banking.
0.942
7 Learning to use internet banking is easy. 0.892
8 Overall I believe that Internet banking is easy to
use.
0.926
Attitude
9 Using internet banking is definitely advantageous. 0.836
10 Using internet banking is a good idea. 0.776
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No Construct statements Standardized
factor loadings (λ)
11 Using internet banking is a wise idea. 0.788
12 I would like to use internet banking. 0.826
Perceived Security
13 Banks offering Internet banking implement
security measures to protect their customers and
have adequate safeguard mechanisms.
0.899
14 Internet banking ensures that transactional
information is protected and cannot be altered.
0.882
15 Internet banking systems have adequate
safeguard mechanisms to ensure that financial or
personal data of customers is not divulged to
other parties.
0.892
16 I feel safe about the security and privacy issues
connected with internet banking.
0.856
17 Using internet banking is as safe as using other
modes of banking.
0.872
Intention
18 I intend to use internet banking is the near future. 0.780
19 Assuming I have access to computer systems, I
intend to use internet banking.
0.774
20 I intend to increase my use of internet banking in
the near future.
0.782
Self Efficacy
21 I would feel comfortable using Internet banking on
my own.
0.857
22 I am skilled at using computers and internet. 0.860
23 I have sufficient knowledge, ability and experience
in using computers and internet.
0.937
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No Construct statements Standardized
factor loadings (λ)
24 Given the facilities, I will be able to use internet
banking
0.927
Awareness
25 I am aware of internet banking and the facilities it
offers.
0.906
26 I am aware of what needs to be done, to become
an internet banking user.
0.891
27 I am aware of the services that could be done
using internet banking.
0.818
28 I am aware of the security and privacy issues of
internet banking.
0.783
Bank Integrity
29 Banks offering Internet banking, deal sincerely
with customers.
0.910
30 Banks offering Internet banking are honest with
their customers.
0.913
31 Banks offering Internet banking, will keep
promises they make.
0.883
Bank Benevolence
32 The intentions of banks offering Internet banking
are benevolent and kind.
0.807
33 Banks offering Internet banking, act in the best
interest of their customers.
0.925
34 Banks offering Internet banking are concerned
about their customers.
0.863
Bank Competence
35 Banks offering Internet banking have sufficient
expertise and are competent to do banking
business on the Internet.
0.900
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No Construct statements Standardized
factor loadings (λ)
36 Banks offering Internet banking have sufficient
resources to do banking business on the Internet.
0.841
37 Banks providing Internet banking have adequate
knowledge to manage their business on the
Internet.
0.919
Disposition to Trust
38 It is easy for me to trust technology. 0.819
39 My tendency to trust technology is high. 0.833
40 I tend to trust a technology, even though I have
little knowledge of it.
0.847
Structural Assurances
41 There are adequate laws to protect me when I use
internet banking.
0.846
42 The existing regulations / legal framework are
good enough to protect Internet banking users.
0.883
43 There are reputable third party certification bodies
to assure the trustworthiness of internet banks
(ex. VeriSign, VISA).
0.893
Consumer Trust on Internet banking
44 Internet banking is reliable and can be used for
my banking transactions.
0.794
45 Internet banking can be trusted. There are not
many uncertainties.
0.807
46 In general I can trust internet banking for my
banking activities.
0.791
Note: All Factor loadings are significant at p<0.01
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Convergent validity was evaluated for the thirteen constructs
using three criteria recommended by Fornell and Larcker (1981):
(1) All measurement factor loadings must be significant and
exceed 0.70,
(2) Construct reliabilities must exceed 0.80, and
(3) Average Variance Extracted (AVE) by each construct must
exceed the variance due to measurement error for that
construct (that is, AVE should exceed 0.50).
In Structural Equation Modeling, for the convergent validity the
factor loadings and Average Variance Extracted (AVE) should be
greater than 0.5 (Fornell and Larcker, 1981). The average variance
extracted (AVE) for each of the factors is calculated manually for all
the constructs using the formula suggested by Hair et al., (1995) as
given below:
∑ λ
∑ λ
∑
Where λ is the standardized factor loadings and is the
indicator measurement error.
This can be put forth in simple terms as sum of squared
standard loadings divided by sum of squared standard loadings plus
sum of indicator measure errors. For example, the AVE for the first
factor Awareness was calculated as:
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That is AVE for awareness is 2.89 / (2.89 +0.25)
Therefore AVE for Awareness = 0.92
The Average variance extracted and the construct factor
loadings are presented in table 5.3. As seen from the table, all AVE
values and factor loadings are greater than 0.5 with almost all values
above 0.80. For all the constructs, all items have high loadings, with
majority above 0.80 therefore demonstrating convergent validity. This
study satisfied this criteria hence convergent validity was established.
Table 5.3 AVE and Factor Loadings of the Constructs
Construct AVE Construct
Factor loadings
Awareness 0.92 0.85
Self Efficacy 0.94 0.89
Perceived Usefulness 0.90 0.85
Perceived Ease of Use 0.94 0.92
Perceived Security 0.93 0.88
Consumer Trust on Internet banking 0.86 0.80
Bank Benevolence 0.94 0.86
Bank Integrity 0.95 0.90
Bank Competence 0.93 0.89
Structural Assurances 0.93 0.87
Disposition to trust 0.90 0.83
Attitude 0.87 0.81
Intention 0.87 0.78
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5.3.2 Composite Reliability
A requirement for construct validity is score reliability.
Reliability can be defined as the degree to which measurements are
free from error and, therefore yield consistent results. Reliability, also
called consistency and reproducibility, is defined in general as the
extent to which a measure, procedure, or instrument yields the same
result on repeated trials (Carmines & Zeller, 1979). It can be used to
assess the degree of consistence among multiple measurements of
variables (Hair, Anderson, Tathman, & Black, 1998).
Operationally reliability is defined as the internal consistency of
a scale, which assesses the degree to which the items are
homogeneous. For reflective measures, all items are viewed as
parallel measures capturing the same construct of interest. Thus, the
standard approach for evaluation, where all path loadings from
construct to measures are expected to be strong (i.e. >=0.70) is used.
Composite reliability measures the overall reliability of a set of items
loaded on a latent construct. Value ranges between zero and one.
Values greater than 0.70 reflect good reliability. Between 0.60 – 0.70
is also acceptable if other indicators of the construct’s validity are
good (Hair et al., 2006)
The internal reliability of the measurement models was tested
using Fornell’s composite reliability (Fornell and Larcker, 1981).
Reliability of the factors was estimated by checking composite
reliability. Composite reliability should be greater than the benchmark
of 0.7 to be considered adequate (Fornell and Larcker, 1981). The
formula for calculating composite reliability is as follows:
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∑
∑ ∑
Where λ is the standardized factor loadings and is the
indicator measurement error.
This can be explained as square of sum of standardized factor
loadings divided by square of sum of loadings plus sum of indicator
measurement errors. For example the composite reliability for the
dimension ‘Awareness’ was calculated as follows:
i.e.
Therefore the composite reliability for the construct ‘Awareness’
is found to be 0.98. Similarly composite reliabilities for other
constructs were estimated. The composite reliability and AVE’S of all
constructs are presented in Table 5.4. All composite reliabilities of
constructs have a value higher than 0.70, indicating adequate internal
consistency.
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Table 5.4 Composite Reliability and AVE of Constructs
Construct Composite
Reliability
AVE
Awareness 0.98 0.92
Self Efficacy 0.98 0.94
Perceived Usefulness 0.98 0.90
Perceived Ease of Use 0.98 0.94
Perceived Security 0.98 0.93
Consumer Trust on Internet banking 0.95 0.86
Bank Benevolence 0.98 0.94
Bank Integrity 0.98 0.95
Bank Competence 0.98 0.93
Structural Assurances 0.97 0.93
Disposition to trust 0.96 0.90
Attitude 0.96 0.87
Intention 0.95 0.87
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5.3.3 Discriminant Validity
Discriminant validity is the extent to which a construct is truly
distinct from other constructs. It means that a latent variable should
explain better the variance of its own indicators than the variance of
other latent variables. In other words the loading of an indicator on its
assigned latent variable should be higher than its loadings on all other
latent variables.
Discriminant validity check is done by comparing the AVE’s
with the squared correlation for each of the constructs. The AVE of a
latent variable should be higher than the squared correlations
between the latent variable and all other latent variables. The rule of
thumb for assessing discriminant validity requires that the square
toot of AVE be larger than the squared correlations between
constructs (Cooper & Zmud, 1990, Hair et al., 1998)
Discriminant validity is shown when each measurement item
correlates weakly with all other constructs except for the one to which
it is theoretically associated. Discriminant validity is shown when two
things happen:
1. The correlation of the latent variable score with
measurement item need to show an appropriate pattern of
loading, one in which the measurement item load highly
on their theoretically assigned factor and not highly on
other factors.
2. Establishing discriminant validity requires an appropriate
AVE (Average Variance Extracted) analysis. The test is to
see if the square root of every AVE for each construct is
much larger than any correlation among any pair of latent
construct. As a rule of thumb, the square root of each
construct should be much larger than the correlation of
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the specific construct with any of the other constructs in
the model (Chin,1998) and should be at least 0.50
(Fornell and Larker,1981)
To examine discriminant validity, the shared variances between
factors were compared with the Average Variance Extracted (AVE) of
the individual factors (Fornell & Larcker, 1981). The proof of
discriminant validity is presented in table 5.5. The diagonal items in
the table represent the square root of AVE’s, which is a measure of
variance between construct and its indicators, and the off diagonal
items represent squared correlation between constructs.
As seen from the factor correlation matrix in Table 5.5. The
lowest AVE value was 0.93 (for CTIB, INT, ATT constructs), which
exceeded the largest squared correlation between any pair of
constructs (0.49 - between Structural Assurance and CTIB). This
analysis showed that the shared variance between factors were lower
than the AVE’s of the individual factors, which confirmed discriminant
validity.
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Table 5.5 Factor Matrix Showing Discriminant Validity
Diagonal are square root of AVE and others squared correlation
AWA SE PU PEU SEC ATT DIS STA BB BI BC CTIB INT
AWA 0.96
SEF 0.06 0.97
PU 0.10 0.06 0.95
PEU 0.08 0.09 0.08 0.97
SEC 0.08 0.14 0.08 0.29 0.96
ATT 0.08 0.15 0.14 0.43 0.47 0.93
DIS 0.06 0.10 0.10 0.27 0.22 0.24 0.95
STA 0.05 0.10 0.07 0.20 0.14 0.22 0.47 0.96
BB 0.02 0.02 0.04 0.00 0.06 0.04 0.08 0.09 0.97
BI 0.03 0.05 0.01 0.08 0.07 0.10 0.21 0.24 0.10 0.98
BC 0.06 0.05 0.02 0.10 0.08 0.10 0.22 0.33 0.04 0.23 0.97
CTIB 0.06 0.16 0.11 0.25 0.32 0.34 0.42 0.49 0.15 0.36 0.38 0.93
INT 0.08 0.06 0.17 0.38 0.17 0.26 0.30 0.23 0.04 0.13 0.17 0.35 0.93
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5.4 Confirming the Measurement Model Using CFA
After validation of the measurement instrument was satisfied,
the results of the Confirmatory Factor Analysis (CFA) using AMOS 18
was used to evaluate the model fit of the measurement model to
confirm the hypothesized structure.
5.4.1 The Measurement Model
The measurement model shown in figure 5.1 comprises of
thirteen factors. Each factor is measured by a minimum of three to a
maximum of five observed variables, the reliability of which is
influenced by random measurement error, as indicated by the
associated error term. Each of these observed variables is regressed
into its respective factor. Finally all the thirteen factors are shown to
be inter-correlated.
5.4.2 Type of Model
The hypothesized model is recursive, i.e., uni-directional.
Recursive models are the most straightforward and have two basic
features: their disturbances are uncorrelated, and all causal effects
are unidirectional.
5.4.3 Model Identification
Structural models may be just-identified, over-identified, or
under-identified. A just identified model is one in which there is a one-
to-one correspondence between the data and the structural
parameters. That is, the number of data variances and co variances
equals the number of parameters to be estimated. An under-identified
model is one which the number of parameters to be estimated exceeds
the number of variances and co-variances. As such the model would
contain insufficient information for attaining a solution.
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Figure 5.1 The Measurement Model
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An over-identified model is one which the number of estimable
parameters is less than the number of data points (i.e. variances and
co variances of the observed variables). This results in positive degrees
of freedom that allow for rejection of the model thereby rendering it of
scientific use. The aim in SEM therefore is to specify a model which is
over-identified.
There are two basic requirements for the identification of any
kind of Structural Equation Model: (1) there must be at least as many
observations as free model parameters (df ≥ 0), and (2) every
unobserved (latent) variable must be assigned a scale (metric).
The proposed model in this study is an over-identified model
with positive degrees of freedom (911) as shown in table 5.6 drawn
from the AMOS output. In this model there are 1081 distinct sample
moments (i.e., pieces of information) from which to compute the
estimates of the default model, and 170 distinct parameters to be
estimated, leaving 911 degrees of freedom, which is positive (greater
than zero). Hence the model is an over identified one.
Table 5.6 AMOS Output: Computation of degrees of freedom
Number of distinct sample moments 1081
Number of distinct parameters to be estimated 170
Degrees of freedom (df) (1081 - 170) 911
Looking at the amount of information available with respect to
the data, these constitute the variances and co variances of the
observed variables. With p variables, there are [p(p+1)/2] such
elements. Given that there are 46 observed measures in the model, it
is known that there are 1081 [i.e. (46 [46+1]/2)] pieces of information
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from which to derive the parameters of the model. Counting up the
unknown parameters in the model, it can be seen that there are 170
parameters to be estimated (33 regression weights, 78 co variances
and 59 variances) The degrees of freedom is positive (911), thus it is
an over-identified model.
5.4.4 Model Estimation Method
The most widely used estimation method is Maximum
Likelihood (ML) estimation. The term maximum likelihood describes
the statistical principle that underlies the derivation of parameter
estimates: the estimates are the ones that maximize the likelihood (the
continuous generalization) that the data (the observed co variances)
were drawn from this population. That is, ML estimators are those
that maximize the likelihood of a sample that is actually observed
(Winer, Brown, & Michels,1991). It is a normal theory method because
ML estimation assumes that the population distribution for the
endogenous variables is multivariate normal. Other methods are
based on different parameter estimation theories, but they are not
currently used as often. In fact, the use of an estimation method other
than ML requires explicit justification (Hoyle, 1995). Most forms of ML
estimation in SEM are simultaneous, which means that estimates of
model parameters are calculated all at once. For this reason, ML
estimation is described in the statistical literature as a full
information method.
The method of ML estimation is very complicated and is often
iterative, which means that the computer derives an initial solution
and then attempts to improve these estimates through subsequent
cycles of calculations. “Improvement” means that the overall fit of the
model to the data generally becomes better from step to step. For most
just-identified structural equation models, the fit will eventually be
perfect. For over identified models, the fit of the model to the data may
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be imperfect, but iterative estimation will continue until the
increments of the improvement in model fit fall below a predefined
minimum value. Iterative estimation may converge to a solution
quicker if the procedure is given reasonably accurate start values,
which are initial estimates of a model’s parameters. If these initial
estimates are grossly inaccurate—for instance, the start value for a
path coefficient is positive when the actual direct effect is negative—
then iterative estimation may fail to converge, which means that a
stable solution has not been reached. Iterative estimation can also fail
if the relative variances among the observed variables are very
different; that is, the covariance matrix is ill scaled.
In this study the minimum iteration was achieved, thereby
providing an assurance that the estimation process yielded an
admissible solution, eliminating any concern about multicollinearity
effects.
5.4.5 Model Evaluation Criteria: Goodness of Fit
Of primary interest in Structural Equation Modeling is the
extent to which a hypothesized data “fits”, or in other words,
adequately describes the sample data. Ideally evaluation of a model fit
should derive from a variety of perspectives and be based on several
criteria that assess model fit from a diversity of perspectives.
The model fitting process involves determining the goodness-of
fit between the hypothesized model and the sample data. Goodness of
fit (GOF) indicates how well the specified model reproduces the
observed covariance matrix among the indicator items (i.e. the
similarity of the observed and estimated covariance matrices). Ever
since the first GOF measure was developed, researchers have been
striving to refine and develop new measures that reflect various facets
of the model’s ability to represent the data. As such, a number of
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alternative GOF measures are available to the researcher. Each GOF
measure is unique, but the measures are classified into three general
groups: absolute measures, incremental measures, and parsimony fit
measures.
For all goodness of fit measures, statistics are presented in a
continuum, with the independence model (a model in which all
correlations among variables are zero) as the most restricted model
and the saturated model (just identified model) as the least restricted
one. The hypothesized model lies in between. In other words once the
specified model is estimated, model fit compares the theory to reality
by assessing the similarity of the estimated covariance matrix (theory)
to reality (the observed covariance matrix). If the theory is perfect, the
observed and estimated covariance matrices would be the same.
The values of any GOF measure result from a mathematical
comparison of these two matrices. The closer the values of these two
matrices are to each other, the better the model is said to fit. Given
below is a description of the goodness-of–fit indicators used to
evaluate model fitness in Structural Equation Modeling (SEM)
5.4.5.1 Chi Square ( ) Goodness of Fit
The Chi square goodness of fit metric is used to assess the
correspondence between theoretical specification and empirical data
in a CFA. By default, the null hypothesis of SEM is that the observed
sample and SEM estimated covariance matrices are equal, meaning
perfect fit. The chi-square value increases as differences (residuals)
are found when comparing the two matrices. With the chi-square test,
the statistical probability that the observed sample and SEM
estimated covariance matrices are equal is assessed. The probability is
the traditional p- value associated with parametric statistical tests.
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Chi-square GOF test is the only statistical test of the difference
between matrices in SEM and is represented mathematically by the
following equation where N is the overall sample size.
Or
This statistic ( ) is also known as the likelihood ratio chi-
square or generalized likelihood ratio. The estimation process in SEM
will focus on yielding parameter values so that the discrepancy
between sample covariance matrix (S) and the SEM estimated
covariance matrix ( ) is minimal. The value of for a just-identified
model generally equals zero and has no degrees of freedom. If = 0,
the model perfectly fits the data (i.e., the predicted correlations and
covariance’s equal their observed counterparts). As the value of chi
square increases, the fit of an over identified model becomes
increasingly worse. Thus, chi square is actually a “badness-of-fit”
index because the higher its value, the worse the model’s
correspondence to the data.
5.4.5.2 Degrees of Freedom (df)
Degrees of freedom represent the amount of mathematical
information available to estimate model parameters. The number of
degrees of freedom for a SEM is calculated by the formula:
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Where p is the total number of observed variables and k is the
number of estimated (free) parameters. Subtracting the number of
estimated parameters from the total amount of available mathematical
information is similar to other multivariate methods. But the
fundamental difference in SEM is in the method of calculation -
, which represents the number of covariance terms
below the diagonal plus the variances on the diagonal. It is not derived
from sample size as in other multivariate techniques. The degrees of
freedom in SEM are based on the size of the covariance matrix, which
comes from the number of indicators in the model.
5.4.5.3 The Goodness-of-fit Index (GFI & AGFI)
The goodness-of-fit index (GFI) was the very first standardized
fit index (Joreskog & Sorbom, 1981). It is analogous to a squared
multiple correlation ( ) except that the GFI is a kind of matrix
proportion of explained variance. Thus, GFI = 1.0 indicates perfect
model fit, GFI > .90 may indicate good fit, and values close to zero
indicate very poor fit. However, values of the GFI can fall outside the
range 0–1.0. Values greater than 1.0 can be found with just identified
models or with over identified models with almost perfect fit; negative
values are most likely to happen when the sample size is small or
when model fit is extremely poor.
Another index originally associated with AMOS is the adjusted
goodness-of-fit index (AGFI; Joreskog & Sorbom, 1981). It corrects
downward the value of the GFI based on model complexity; that is,
168
there is a greater reduction for more complex models. The AGFI differs
from the GFI only in the fact that it adjusts for the number of degrees
of freedom in the specified model. The GFI and AGFI can be classified
as absolute indices. The parsimony goodness-of-fit index (PGFI;
Mulaik et al., 1989) corrects the value of the GFI by a factor that
reflects model complexity, but it is sensitive to model size.
5.4.5.4 Normed Fit Index (NFI)
The NFI is one of the original incremental fit indices introduced
by Bentler and Bonnet (1980). It is a ratio of the difference in the
value for the fitted model and the null model divided by the value
for the null model. It ranges between zero to one. A Normed fit index
of one indicates perfect fit.
5.4.5.5 Relative Fit Index (RFI)
The relative Fit Index (RFI; Bollen, 1986) represents a derivative
of the NFI; as with both the NFI and CFI, the RFI coefficient values
range from zero to one with values close to one indicating superior fit
(Hu and Bentler, 1999).
5.4.5.6 Comparative Fit Index (CFI)
The CFI is an incremental fit index that is an improved version
of the NFI (Bentler, 1990; Bentler and Bonnet, 1980; Hu and Bentler,
1999). The CFI is Normed so that values range between zero to one,
with higher values indicating better fit. Because the CFI has many
desirable properties, including its relative, but not complete,
insensitivity to model complexity, it is among the widely used indices.
CFI values above 0.90 are usually associated with a model that fits
well. But a revised cut off value close to 0.95 was suggested by Hu
and Bentler (1999).
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5.4.5.7 Tucker Lewis Index (TLI)
The Tucker Lewis Index (Tucker and Lewis, 1973) is
conceptually similar to the NFI, but varies in that it is actually a
comparison of the Normed chi-square values for the null and specified
model, which to some degree takes into account model complexity.
Models with good fit have values that approach one (Hu and Bentler,
1999), and a model with a higher value suggests a better fit than a
model with a lower value.
5.4.5.8 Root Mean Square Error of Approximation (RMSEA)
Root Mean Square Error Approximation (RMSEA) was first
proposed by Steiger and Lind (1980). It is one of the most widely used
measures that attempts to correct for the tendency of the GOF test
statistic to reject models with a large sample or a large number of
observed variables. Thus it better represents how well a model fits a
population, not just the sample used for estimation. Lower RMSEA
values indicate better fit. Earlier research suggest values of <0.05.
(Browne and Cudeck, 1993), Hu and Bentler (1999) have suggested
value of <0.06 to be indicative of good fit.
5.4.5.9 Root Mean Square Residual (RMR)
The Root Mean Square Residual represents the average residual
value derived from the filling of the variance- covariance matrix for the
hypothesized model to the variance covariance matrix of the
sample data (S). Therefore, the RMR is the square root of the mean of
the standardized residuals. Lower RMR values represent better fit and
higher values represent worse fit. Recommended value of RMR is <
0.02.
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5.4.6 Assessing Overall Measurement Model Fitness
The results shown in table 5.7 provide a quick overview of the
model fit, which includes the value (1249.47), together with its
degrees of freedom (911) and probability value (0.000).
In the table NPAR stands for Number of parameters, and CMIN
( ) is the minimum discrepancy and represents the discrepancy
between the unrestricted sample covariance matrix S and the
restricted covariance matrix . Df stands for degrees of freedom and
P is the probability value.
Table 5.7 AMOS Output Showing Model Fit
Model NPAR df P /df
Default model 170 1249.473 911 .000 1.372
Saturated model 1081 .000 0
Independence model 46 25250.036 1035 .000 24.396
In SEM a relatively small chi-square value supports the
proposed theoretical model being tested. In this model the value is
1249.47 and is small compared to the value of the independence
model (25250). Hence the value is good.
Although the seems good, it is also appropriate to check the
value of divided by df (Wheaton, Muthen, Alwin and Summers,
1977) as the statistic is particularly sensitive to sample sizes (that
is, the probability of model rejection increases with increasing sample
size, even if the model is minimally false), and hence chi-square ( )
divided by degrees of freedom is suggested as a better fit metric
(Bentler and Bonnett, 1980). It is recommended that this metric not
171
exceed five for models with good fit (Bentler, 1989). For the current
CFA model, as shown in table 5.7, ⁄ was 1.372 ( = 1249.473;
df = 911), suggesting acceptable model fit.
The other different common model-fit measures used to assess
the models overall goodness of fit as explained earlier is shown in
table 5.8.
Table 5.8 Fit statistics of the Measurement model
Fit statistic Recommended Obtained
- 1249.47
df - 911
significance p < = 0.05 0.000
⁄ < 5.0 1.372
GFI > 0.90 0.92
AGFI >0.90 0.91
NFI > 0.90 0.95
RFI > 0.90 0.94
CFI > 0.90 0.98
TLI >0.90 0.98
RMSEA < 0.05 0.02
RMR <0.02 0.006
Goodness of Fit index (GFI) obtained is 0.92 as against the
recommended value of above 0.90, The Adjusted Goodness of Fit Index
(AGFI)is 0.91 as against the recommended value of above 0.90 as well.
The Normed fit Index (NFI), Relative Fit index (RFI), Comparative Fit
172
index (CFI), Tucker Lewis Index (TLI) are 0.95, 0.94, 0.98, 0.98
respectively as against the recommended level of above 0.90.
RMSEA is 0.02 and is well below the recommended limit of 0.05,
and Root Mean Square Residual (RMR) is also well below the
recommended limit of 0.02 at 0.006. This can be interpreted as
meaning that the model explains the correlation to within an average
error of 0.006 (Hu and Bentler, 1990). Hence the model shows an
overall acceptable fit. The model is an over identified model.
The confirmatory factor analysis showed an acceptable overall
model fit and hence, the theorized model fit well with the observed
data. It can be concluded that the hypothesized thirteen factor CFA
model fits the sample data very well.
5.5 The Structural Model Path Diagram
The structural model shown in Figure 5.2 shows the hypotheses
formulated. Before moving on to the structural model analysis it is
necessary to understands the structural model path diagram.
SEM is actually the graphical equivalent of its mathematical
representation whereby a set of equations relates dependent variables
to their explanatory variables.
In reviewing the model presented in figure 5.2 it can be seen
that there are 13 unobserved latent factors and 46 observed variables.
These 46 observed variables function as indicators of their respective
underlying latent factors.
Associated with each observed variable is an error term (e1 –
e46). And with the factor being predicted, for example Perceived
173
Usefulness (PU), a residual term (r1) is associated. Errors associated
with observed variables represent measurement error, which reflects
on their adequacy in measuring the related underlying factors.
Residual terms represent error in the prediction of endogenous
factors from exogenous factors. For example the residual r1 in figure
5.1 represents error in prediction of PU (the endogenous factor) from
SE (the exogenous factor).
Certain symbols are used in path diagrams to denote
hypothesized processes involving the entire system of variables. In
particular, one-way arrows represent structural regression coefficients
and thus indicate the impact of one variable on another. In figure 5.2,
for example, the unidirectional arrow pointing toward the endogenous
factor PU (Perceived Usefulness), implies that the exogenous factor SE
(Self Efficacy) ‘Causes’ PU.
Likewise the four unidirectional arrows leading from SE to each
of the four observed variables (SE1, SE2, SE3, SE4); suggest that
these score values are each influenced by their respective underlying
factors. As such these path coefficients represent the magnitude of
expected change in the observed variables for every change in the
related latent variable (or factor).
The one-way arrows pointing from the enclosed error terms (e1 –
e46) indicate the impact of measurement error on the observed
variables, and from the residual (r1), the impact of error in the
prediction of PU.
174
Figure 5.2 The Structural Model
175
5.6 Structural Model – Hypotheses Testing
Next the SEM was conducted on the structural model using
Amos18 to test the hypotheses formulated as shown in figure 5.2.
Here the full structural equation model is considered and the
hypotheses to be tested relates to the pattern of causal structure
linking several variables that bear on the construct of usage intention.
In reviewing the SEM path model it can be seen that Usage
Intention is influenced by the Perceived Usefulness, Attitude and
Consumer Trust on Internet Banking. Perceived Usefulness, Perceived
Ease of Use and Perceived Security are influenced both by Awareness
and Self-Efficacy. Perceived Ease of Use is hypothesized to influence
Perceived Usefulness and the antecedents of Consumer Trust on
Internet Banking are hypothesized as Perceived Security, Bank
Competence, Bank Integrity, Bank Benevolence, Structural
Assurances and Disposition to Trust.
All these paths reflect finding in the literature and the model
shown in figure 5.2 represents only the structural portion of the
Structural Equation Modeling (SEM).
In this section of analysis the hypotheses testing and results
are presented before which, the inter- construct correlation matrix is
presented in table 5.9.
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Table 5.9 Inter Construct Correlation Matrix.
** Correlation is significant at the 0.01 level
AWA SE PU PEU SEC ATT DIS STA BB BI BC CTIB INT
AWA 1
SE 0.238** 1
PU 0.314** 0.245** 1
PEU 0.279** 0.303** 0.286** 1
SEC 0.279** 0.375** 0.284** 0.538** 1
ATT 0.276** 0.385** 0.377** 0.659** 0.684** 1
DIS 0.246** 0.314** 0.311** 0.519** 0.468** 0.485** 1
STA 0.224** 0.319** 0.262** 0.450** 0.375** 0.471** 0.686** 1
BB 0.148** 0.157** 0.194** 0.063** 0.241** 0.207** 0.290** 0.299** 1
BI 0.176** 0.219** 0.104** 0.291** 0.270** 0.312** 0.456** 0.486** 0.314** 1
BC 0.240** 0.230** 0.154** 0.315** 0.280** 0.322** 0.470** 0.574** 0.193** 0.476** 1
CTIB 0.244** 0.394** 0.339** 0.498** 0.564** 0.582** 0.651** 0.699** 0.384** 0.599** 0.616** 1
INT 0.281** 0.236** 0.416** 0.613** 0.417** 0.513** 0.549** 0.484** 0.189** 0.367** 0.407** 0.594** 1
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5.6.2 Assessing Structural Model Fitness
The process of establishing the structural model’s validity
follows the general guidelines adopted for the measurement model. A
new SEM estimated covariance matrix is computed and it is different
from the measurement model, since the measurement model assumes
that all constructs are correlated, but in structural model the
relationships between some constructs are assumed to be zero.
Therefore, for almost all conventional SEM models, the chi square
GOF for the measurement model will be less than the GOF for the
structural model. Table 5.10 presents select fit indices of the
structural model.
Table 5.10 Fit Indices of the Structural Model
Fit statistics Values
2671.96
df 969
Goodness of fit index(GFI) 0.82
Adjusted Goodness of Fit Index ((AGFI) 0.80
Normed Fit Index (NFI) 0.89
Relative Fit Index (RFI) 0.88
Comparative Fit Index (CFI) 0.93
Incremental Fit Index (IFI) 0.93
Tucker Lewis Index (TLI) 0.92
Root mean Square Error of Approximation
( RMSEA)
0.05
Root Mean Square Residual (RMR) 0.05
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The model fit indices also provide a reasonable model fit for the
structural model. Goodness of Fit index (GFI) obtained is 0.82. The
Adjusted Goodness of Fit Index (AGFI) is 0.80. The Normed fit Index
(NFI), Relative Fit index (RFI), Comparative Fit index (CFI), Tucker
Lewis Index (TLI) are 0.89, 0.88, 0.93, 0.92 respectively. RMSEA is
0.05, and Root Mean Square Residual (RMR) is also 0.05. Hence it is
concluded that the proposed research model fits the data reasonably.
5.6.3 Testing Structural Relationships
The hypothesized research model exhibited good fit with
observed data as mentioned above. Of greater interest for nomological
validity is the path estimates in the structural model and variance
explained ( value) in each dependent variable. All the 20
hypothesized paths are significant (p value <0.001), and hence
supported. The standardized regression weights of the output and
result of the hypotheses testing providing support for hypotheses HI
through H20 is presented in table 5.11.
Table 5.11 AMOS Output Extract: Standardized Regression
Estimates of the Hypotheses Tested
No Hypotheses Path coefficients
(β value)
Supported / not
supported
H 1 Computer self efficacy(SE)
positively influences Perceived
Usefulness (PU)
0.141 Supported
H 2 Computer self efficacy(SE)
positively influences Perceived
Ease of Use (PEOU)
0.267 Supported
179
H 3 Computer self efficacy(SE)
positively influences Perceived
Security (PS)
0.345 Supported
H 4 Awareness (AWA) positively
influences Perceived Usefulness
(PU)
0.237 Supported
H 5 Awareness (AWA) positively
influences Perceived Ease of Use
(PEOU)
0.230 Supported
H 6 Awareness (AWA) positively
influences Perceived Security
(PS)
0.214 Supported
H 7 Perceived Usefulness (PU)
positively influences attitude
(ATT)
0.134 Supported
H 8 Perceived Usefulness (PU)
positively influences consumer
intention (INT)to use internet
banking
0.233 Supported
H 9 Perceived Ease of use (PEOU)
positively influences attitude
(ATT)
0.405 Supported
H 10 Perceived Ease of use (PEOU)
positively influences Perceived
Usefulness (PU)
0.181 Supported
H 11 Perceived Security (PS) positively
influences attitude (ATT)
0.418 Supported
H 12 Perceived Security (PS) positively
influences Consumer Trust in
Internet banking (CTIB)
0.359 Supported
180
H13 Bank Competence (BC) positively
influences Consumer Trust in
Internet banking (CTIB)
0.309 Supported
H 14 Bank Integrity (BI) positively
influences Consumer Trust in
Internet banking (CTIB)
0.299 Supported
H 15 Bank Benevolence (BB) positively
influences Consumer Trust in
Internet banking (CTIB)
0.133 Supported
H 16 Structural Assurances (STAS)
positively influences Consumer
Trust in Internet banking (CTIB)
0.357 Supported
H 17 Personal Disposition to trust
(DIS) positively influences
Consumer Trust in Internet
banking (CTIB)
0.208 Supported
H 18 Consumer Trust in Internet
banking (CTIB) positively
influences attitude (ATT)
0.138 Supported
H 19 Consumer Trust in Internet
banking (CTIB) positively
influences consumer intention
(INT)to use internet banking
0.345 Supported
H 20 Attitude (ATT) towards internet
banking influences consumer
intention (INT) to use internet
banking
0.217 Supported
*Significant at 0.01 level
181
All hypotheses are accepted. Consumer intention to use internet
banking is influenced by Perceived Usefulness (β= 0.23), Attitude
(β=0.21), and Consumer Trust on Internet banking (β=0.34), where
Attitude is influenced by Perceived Usefulness (β=0.13), Perceived
Ease of Use (β=0.40), and Perceived Security (β=0.41).
The three beliefs: PU, PEOU and PS are influenced by
Consumer Awareness of internet banking (AWA) and Consumers’ Self
Efficacy (SE). Awareness influences Perceived Usefulness, Perceived
Ease of Use and Perceived Security as shown in the β values of 0.23,
0.23 and 0.21 respectively and Self Efficacy influences Perceived
Usefulness, Perceived Ease of Use and Perceived Security as shown in
the β values of 0.14, 0.26 and 0.34 respectively.
The antecedents of Consumer Trust on Internet banking (CTIB)
are Bank’s benevolence (β=0.13), Bank’s integrity (β=0.29), Bank’s
Competence (β=0.30), Perceived Security (β=0.36), Structural
Assurances (β=35) and Personal Disposition to Trust (β=0.21).
Consumer Trust on Internet Banking (CTIB) is a significant
predictor of consumers’ internet banking usage intentions (β= 0.34).
The six antecedents of trust although not substantially large,
demonstrate that the trust scales measures what it is purported to
measure (that is, users' intention to transact with internet banking)
and is predicted by theorized determinants, thereby satisfying the
nomological validity requirement of the proposed trust scale. The
model altogether explains 79 percent of the usage intentions of
consumers of internet banking.
In summary of the research, a theoretical model was proposed
for establishing a research model that gives a good understanding of
factors that influence consumer intention to use internet banking. The
TAM was extended by incorporating Awareness, Self Efficacy,
182
Perceived Security and Consumer Trust on Internet Banking, and
examining its influence on consumers’ intention to adopt internet
banking. In the process a clear set of antecedents for Consumer Trust
on Internet Banking (CTIB), that can explain individual’s intention to
adopt internet banking was brought out and the empirical validation
of the model for internet banking acceptance was successfully
assessed.
5.7 Demographic Description of the Respondents
Although not a major objective of the study, demographic
factors were analyzed, out of curiosity, to check if there were any
significant differences in the usage intentions of internet banking and
also to check, whether they had any influence on the usage
intentions.
This section briefly describes the demographic profile of the
respondents. The descriptive statistics of the respondents'
demographic characteristics is presented in table 5.12.
Of the 655 respondents, 36.9 percentage were in the 36 -45 age
group; followed by 33.6 percentage in the 25-35 age group and 15.7
percentage in the age group of 46-55; 32.7 percentage were earning a
monthly income of Rs.30001/- to Rs.50000/-, followed by 23.8
percentage in the income group of Rs.50001/- to Rs.70000/-, and
21.8 percentage in the income group of Rs.10001/- to Rs.30000/- and
7.5 percentage in the income group of less than Rs.10000/-. The
proportion of male and female respondents was 64.3 percent and 35.7
percent respectively. Majority (31.8 percent) had bachelor’s degree or
equal, followed by 30.4 percent having their master’s degrees. Another
16 percent were doctorates. The percentage of professionals was 11.5.
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Table 5.12 Demographic Profile of the Respondents
Demographic profile Frequency Percentage (%)
AGE (in years )
<25 65 9.9
25 - 35 220 33.6
36 - 45 242 36.9
46 - 55 103 15.7
>55 25 3.8
TOTAL 655 100
GENDER
Male 421 64.3
Female 234 35.7
TOTAL 655 100
MONTHLY INCOME (Rs.)
< 10000 49 7.5
10001 - 30000 143 21.8
30001 - 50000 214 32.7
50001 - 70000 156 23.8
>70000 93 14.2
TOTAL 655 100
EDUCATION
Higher Secondary and below 68 10.4
Undergraduate or equivalent 208 31.8
Masters / Post Graduate 199 30.4
Doctorates/ PhD 105 16.0
Other Professionals 75 11.5
TOTAL 655 100
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5.8 Demographic Differences in Adoption
To examine whether there exists any discrepancy of the internet
banking usage among different groups by age, education, income and
gender, a one way ANOVA was conducted for each of the select
demographic factors (Age, Gender, income and education).
At the heart of ANOVA is the notion of variance. The basic
procedure is to derive two different estimates of population variance
from the data, then calculate a statistic from the ratio of these two
estimates (Between groups and within groups variance). The F-ratio is
the ratio of between-groups variance to within-groups variance. A
significant F-value indicates that the population means are probably
not equal. And Levene’s test is used to determine if the scores in each
group have homogenous variances. Before ANOVA was conducted, it
was ensured that the necessary assumptions were met. The two
assumptions of concern were population normality and homogeneity
of variance.
5.8.1 One Way ANOVA- Age
First one-way ANOVA was used to test if there is any significant
difference in consumer intention to use internet banking across ages.
The Levene’s test of Homogeneity of Variances is presented in table
5.13 and the ANOVA result is shown in table 5.14.
Table 5.13 reveals that Levene’s test for homogeneity of
variances is not significant (p >0.05), therefore it can be confidently
said that the population variances for each group are approximately
equal.
185
Table 5.13 Levene’s Test of Homogeneity of variances- Age
Levene Statistic df1 df2 Sig.
1.133 4 650 .340
Table 5.14 One way ANOVA – Age
Sum of Squares
df Mean Square
F Sig
Between Groups 3.998 4 1.000 6.189 .000
Within Groups 104.985 650 .162
Total 108.983 654
* Significant at p <0.05 level
The F-value is only 6.189 with degrees of freedom four and 650
and is significant at p<0.05 value, hence the hypothesis that
consumer intention to use internet banking differs across age groups
is accepted. It must also be mentioned that it is possible that F values
are significant when there is a large sample size.
5.8.2 One Way ANOVA- Education
Second, one-way ANOVA was used to test if there is any
significant difference in consumer intention to use internet banking
across consumer groups by education. Levene’s test of homogeneity of
Variances is presented in table 5.15. This test reveals that Levene’s
test for homogeneity of variances is not significant (p >0.05), therefore
it can be confidently said that the population variances for each group
are approximately equal. The one way ANOVA showing the F value is
presented in table 5.16
186
Table 5.15 Levene’s Test of Homogeneity of variances- Education
Levene Statistic df1 df2 Sig.
2.160 4 650 .072
Table 5.16 One way ANOVA – Education
Sum of
Squares
df Mean
Square
F Sig
Between Groups 3.42 4 .855 5.26 .000
Within Groups 105.56 650 .162
Total 108.98 654
* Significant at p <0.05 level
The F-value is only 5.266 but is significant at p<0.05 value,
hence the hypothesis that consumer intention to use internet banking
differs across groups by education is accepted. It must also be
mentioned that it is possible that F values are significant when there
is a large sample size.
5.8.3 One Way ANOVA- Income
Thirdly, one-way ANOVA was used to test if there is any
significant difference in consumer intention to use internet banking
across consumer groups by income. Levene’s test of homogeneity of
Variances is presented in table 5.17. This test reveals that Levene’s
test for homogeneity of variances is not significant (p >0.05), therefore
it can be confidently said that the population variances for each group
are approximately equal. The one way ANOVA showing the F value is
presented in table 5.18.
187
Table 5.17 Levene’s Test of Homogeneity of variances- Income
Levene Statistic df1 df2 Sig.
2.176 4 650 .070
Table 5.18 One way ANOVA – Income
Sum of Squares df Mean
Square
F Sig
Between Groups 3.93 4 .983 6.083 .000
Within Groups 105.05 650 .162
Total 108.98 654
* Significant at p <0.05 level
The F-value is 6.083 and is significant at p<0.05 value, hence
the hypothesis that consumer intention to use internet banking differs
across income groups is accepted. It must also be mentioned that it is
possible that F values are significant as the sample size is large.
5.8.4 One way ANOVA- Gender
Finally one-way ANOVA was used to test if there is any
significant difference in consumer intention to use internet banking
among gender groups. Levene’s test of homogeneity of Variances is
presented in table 5.19. This test reveals that Levene’s test for
homogeneity of variances is not significant (p >0.05), therefore it can
be confidently said that the population variances for each group are
approximately equal. The one way ANOVA showing the F value is
presented in table 5.20.
The F-value is 12.28 with degrees of freedom one and 653
and is significant at p<0.05 value, hence the hypothesis that
188
consumer intention to use internet banking differs among genders is
accepted.
Table 5.19 Levene’s Test of Homogeneity of variances- Gender
Levene Statistic df1 df2 Sig.
0.057 1 653 .812
Table 5.20 One way ANOVA – Gender
Sum of Squares df Mean
Square
F Sig
Between Groups 2.012 1 2.01 12.28 .000
Within Groups 106.971 653 0.16
Total 108.98 654
* Significant at p <0.05 level
The one way ANOVA tests reveal that consumers’ usage
intentions across all categories of age, education, income and gender
were found to be significantly different. It must also be mentioned
here that although F values are significant, the values are less and it
is possible that F values are significant as the sample size is large.
The One-way ANOVA analysis was used to test if there is any
significant difference in the usage intentions of consumers by age,
education, gender and income. It can be concluded from the above
that there is a significant difference in usage intentions across groups
of age, gender, education and income. Thus hypothesis H 21 finds
support.
189
5.9 Impact of Select Demographics on Usage Intentions of
Internet Banking by Customers
Multiple regression was used to find out if there is any
significant relationship between select demographic variables of
consumers on their intention to use internet banking. Linear
regression estimates the coefficients of the linear equation involving
one or more independent variables that best predict the value of the
dependent variable. The linear regression model assumes that there is
a linear, or ‘straight line’ relationship between the dependent variable
and each predictor. The total score obtained from the sample
consumers (655) on their intention to use internet banking (INT) is
tested against three demographic variables, namely, age, education
and Income. Table 5.22 confers the regression conclusions. The
equation tested is y = α +β1 (x1) +β2 (x2) + β3 (x3), Where y is the
consumer intention to use internet banking and X1 is income, X2 is
education and X3 is Age.
The dependent variable consumer’s Intention to use internet
banking was configured as the total score obtained across the three
items. The independent variables were age, education and income and
were measured in continuous scales, where specifically speaking
education was measured in terms of the number of years of education
one has secured ranging from a minimum of less than twelve years to
greater than nineteen years. Sex was not considered in this test as it
was not possible to be measured on continuous scales.
In the multiple regression analysis tests only key statistics such
as standardized beta coefficient was used for determining the
significant impact. Table 5.21 shows the regression statistics. From
table 5.21 it can be seen that the three independent variables: age,
education and income together explain a very negligible or miniscule
3.6 percent of the variance in consumer intention to use internet
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banking, but is significant (p<0.05) as indicated by the F-value of
8.035.
Although the model fit looks positive, by examining the
standardized beta coefficients it is obvious that only age has a
significant negative impact on consumer intention to use internet
banking, while education and income do not have a significant
influence. Age of the consumer is found to have a significant negative
beta value, implying that age does influence consumers’ intention to
use internet banking (Standardized beta coefficient -.177, and t-value
-4.57); significant at p < 0.05 level), meaning as age increases;
consumer intention to use internet banking is less.
Table 5.21 Multiple Regression Test Results on Consumer
Intentions to Use Internet Banking by select Demographic
Variables
Std Beta Coefficient (T-Statistics) F
statistic
R2. Adj R2
Age Education Income
Consumer
Intention to
Use Internet
Banking
-0.177*
(-4.5 )
0.06
(1.66)
0.041
(1.05)
8.035*
0.036
0.031
* Significant at 0.05 level
Thus the demographic variable age alone has significant impact
on consumer intention to use internet banking and the extent of
influence is negative, while income and education are not found to
have significant influence on usage intentions. In summary, the
impact of demographic factors on usage intention of internet banking
was found to be negligible in support of the study by Lee and Lee
(2001).
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5.10 Results of Hypotheses Testing
The extended Technology Acceptance Model depicting
relationship between variables identified for the study and giving a
good understanding of factors that influence consumer intention to
use internet banking was validated as all the hypotheses from H1 to
H20 are accepted. It can thus be concluded that the conceptual model
developed is well validated and established, explaining 79 percent of
the variance explained of consumer intentions to use internet
banking. The extension of the Technology Acceptance Model by
incorporating Awareness (AWA), Self Efficacy (SE), Perceived Security
(PS), Consumer Trust on Internet Banking (CTIB) was examined and
was found to influence consumers’ intention to use internet banking.
A good set of antecedents for Consumer Trust on Internet banking
(CTIB), that can explain individual’s intention to adopt internet
banking was also developed and assessed empirically.
The hypothesized research model exhibited good fit with
observed data as mentioned earlier. The path estimates in the
structural model and variance explained ( value) in each dependent
variable were significant. All the 20 hypothesized paths were
supported at p<0.01. The standardized regression weights of the
output and result of the hypotheses tests provide support for
hypotheses HI through H20.
The demographic factors’ influence on usage intentions was
found to be negligible, with significant differences among different age,
gender, income and education groups with regards to usage
intentions. The multiple regression test revealed that only age has a
significant negative impact on consumer intention to use internet
banking. The summary of findings and conclusive remarks on findings
and implications are presented in the next chapter.