CHAPTER V ANALYSIS & INTERPRETATION -...

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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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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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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.

166

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:

167

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).

169

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.

176

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

178

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



Ease of Use (PEOU)

0.267 Supported

179



Security (PS)

0.345 Supported

H 4 Awareness (AWA) positively

influences Perceived Usefulness

(PU)

0.237 Supported


influences Perceived Ease of Use

(PEOU)

0.230 Supported


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)


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



0.309 Supported

H 14 Bank Integrity (BI) positively



0.299 Supported

H 15 Bank Benevolence (BB) positively



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


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


Total 108.98 654


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


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


187

Table 5.17 Levene’s Test of Homogeneity of variances- Income


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


Total 108.98 654


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


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


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


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


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

190

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).

191

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.

CHAPTER V ANALYSIS & INTERPRETATION -...

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