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CHAPTER IV

RESEARCH METHOD

This chapter presents the research design adopted for the study

to investigate the antecedent influences on consumer intention to use

internet banking using the TAM as the base model. First the significance

and objectives of the study is presented followed by the sampling

design, development of the research instrument and the tools used for

analysis.

4.1 Significance of the Study

Internet banking was chosen for the study because it has

revolutionized the way banking functions are performed. Although

banks spend huge amounts of money, this investment would be

fruitful only if customers use internet banking. This calls for

developing a holistic model to explain consumer intention to use

internet banking.

This study is significant for designing educational and

communication strategies to foster greater acceptance of internet

banking among consumers. Extending the technology acceptance

model for internet banking acceptance promises to assist in predicting

attitude and acceptance and thereby provides meaningful information

that can serve as a basis for this designing. The study is also

significant for the following reasons:

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Internet banking is a relatively new innovation in India and a

study of consumer adoption of internet banking will enhance

the quality of services of the Indian banking sector in the future.

Internet banking has been widely studied in developed countries

but literature reveals that studies of internet banking adoption

in developing countries like India is far less.

Literature shows that adoption of internet banking is very slow

in India.

The development of a conceptual model that explains and

predicts the factors that influence the adoption of an

information technology system such as internet banking, in the

Indian banking sector will help marketers and managements of

banks in their efforts to identify reasons for adopting internet

banking.

The empirical support for the proposed hypotheses based on an

integrated research framework and extensive literature review

will help academicians and can be used as a research model for

further studies.

The model has the potential to be generalized nation-wide.

The study is also significant because, for the first time in the

Indian context, SEM technique is used to test a proposed model

for internet banking adoption.

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4.2 Objectives

This study was carried out to test an extended Technology

Acceptance Model (TAM) in the internet banking context, by drawing

on constructs from a range of theories. Following an extensive

literature review, Self Efficacy, Awareness, Perceived Security and

Consumer Trust on Internet Banking (CTIB) were included as

additional variables to the Technology Acceptance Model (TAM) and

the following objectives were framed.

To propose a theoretical framework for establishing a research

model that gives a good understanding of factors that influence

consumer intention to use internet banking.

To extend the Technology Acceptance Model by incorporating

Awareness (AWA) , Self efficacy(SEF) , Perceived Security(PS),

Consumer Trust on Internet Banking (CTIB) and examine its

influence on consumers’ intention to adopt internet banking

To bring out a set of antecedents for Consumer Trust on

Internet banking(CTIB), that can explain individual’s intention

to adopt internet banking

To assess the empirical validation of the proposed model for

internet banking acceptance.

To identify the significant difference, if any, in consumer

intention to use internet banking by age, gender, education and

income.

To examine the influence of age, education and income on

consumer intention to use internet banking.

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4.3 Research Instrument

This exploratory study uses a questionnaire (quantitative

treatment) to collect data. Quantitative research is defined as "the

numerical representation and manipulation of observations for the

purpose of describing and explaining the phenomena that those

observations reflect," (Babbie, 2004). Questionnaires are a vital tool to

obtaining information from a large population in a short period of

time. Sudman and Bradburn (1982) asserted that using a

questionnaire can assist researchers in obtaining feedback on facts,

figures, attitudes, opinions, experiences, and judgment.

The study is based on the Technology Acceptance Model (Davis,

1989). To extend previous research, an instrument that could be used

to measure a wide range of user perceptions concerning internet

banking was developed. In order to bring an understanding of the

complex issue of internet security, and to extend and add strength to

what is already known through previous research Consumer Trust on

Internet banking, Awareness, Self Efficacy and Perceived Security

were included as additional dimensions to this study. To determine

the factors of influence on internet banking usage, a survey was

conducted on bank customers.

The goal of the research was to extend the TAM for predicting

consumer intention to use internet banking and in the process, to find

out the perceptions of bank customers towards internet banking, and

also to find out whether or not these perceptions have any effect on

their decision to bank on the internet. To achieve the objectives of the

study, the research focused on various widely tested instruments

developed by earlier researchers.

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4.3.1 Constructs of the Instrument

The survey instrument (questionnaire) is shown in the

Appendix. The questionnaire of this study consists of two sections.

The first section of the questionnaire gathers demographic information

regarding age, gender, education and income levels of the customer.

The second section of the questionnaire includes questions related to

the general perception of adopting internet banking in the lines of the

TAM and added variables from well established research. A five-point

Likert-type scale ranging from strongly agree to strongly disagree was

applied to assess the perceived attributes of internet banking.

Before the questionnaire was prepared, four bank managers

were interviewed. These managers were not only familiar with the

characteristics of internet banking as a distribution channel, but also

possessed first-hand information about the needs and wants of their

customers through maintaining contacts with their customers. Indeed

one line of research relies heavily on bank managers as key

informants about the prospects and benefits of different banking

channels (Aladwani, 2001; Daniel, 1999; Hway-Boon and Yu, 2003;

Nath et al., 2001). Their insights helped to finalize the questionnaire

used for the main survey.

First, dimensions identified were presented to these bank

managers and they were asked to choose the factors that were

relevant to internet banking adoption in the Indian context. Only the

relevant dimensions were retained so as to keep the number of items

in the questionnaire to a minimum. The final items of the constructs

drawn from previous studies and modified to suit the present study

are shown in Table 4.1.

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Table 4.1 Items of the constructs

STATEMENTS

Perceived Usefulness

1. Internet banking enables people to conduct financial transactions

more quickly.

2. Internet banking improves one’s effectiveness in conducting

banking transactions.

3. Internet banking makes it easier to conduct banking transactions

4. Internet banking provides convenience since it is available 24

hours, 7 days of the week.

5. Internet banking saves time compared to traditional banking.

Perceived Ease of Use

6. It would be easy for me to become skilful at using internet banking.

7. Learning to use internet banking is easy.

8. Overall I believe that Internet banking is easy to use.

Attitude

9. Using internet banking is definitely advantageous.

10. Using internet banking is a good idea.

11. Using internet banking is a wise idea.

12. I would like to use internet banking.

Perceived Security

13. Banks offering Internet banking implement security measures to

protect their customers and have adequate safeguard

mechanisms.

14. Internet banking ensures that transactional information is

protected and cannot be altered.

15. Internet banking systems have adequate safeguard mechanisms

to ensure that financial or personal data of customers is not

divulged to other parties.

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16. I feel safe about the security and privacy issues connected with

internet banking.

17. Using internet banking is as safe as using other modes of

banking.

Intention

18. I intend to use internet banking is the near future.

19. Assuming I have access to computer systems, I intend to use

internet banking.

20. I intend to increase my use of internet banking in the near future.

Self Efficacy

21. I would feel comfortable using Internet banking on my own.

22. I am skilled at using computers and internet.

23. I have sufficient knowledge, ability and experience in using

computers and internet.

24. Given the facilities, I will be able to use internet banking.

Awareness

25. I am aware of internet banking and the facilities it offers.

26. I am aware of what needs to be done, to become an internet

banking user.

27. I am aware of the services that could be done using internet

banking.

28. I am aware of the security and privacy issues of internet banking.

Bank Integrity

29. Banks offering Internet banking, deal sincerely with customers.

30. Banks offering Internet banking are honest with their customers.

31. Banks offering Internet banking will keep promises they make.

Bank Benevolence

32. The intentions of banks offering Internet banking are benevolent

and kind.

33. Banks offering Internet banking, act in the best interest of their

customers.

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34. Banks offering Internet banking are concerned about their

customers.

Bank Competence

35. Banks offering Internet banking have sufficient expertise and are

competent to do banking business on the Internet.

36. Banks offering Internet banking have sufficient resources to do

banking business on the Internet.

37. Banks providing Internet banking have adequate knowledge to

manage their business on the Internet.

Disposition to Trust

38. It is easy for me to trust technology.

39. My tendency to trust technology is high.

40. I tend to trust a technology, even if I have little knowledge of it.

Structural Assurances

41. There are adequate laws to protect me when I use internet

banking.

42. The existing regulations / legal framework are good enough to

protect Internet banking users.

43. There are reputable third party certification bodies to assure the

trustworthiness of internet banks (ex. VeriSign, VISA).

Consumer Trust on Internet Banking (CTIB)

44. Internet banking is reliable and can be used for my banking

transactions.

45. Internet banking can be trusted. There are not many

uncertainties.

46. In general I can trust internet banking for my banking activities.

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4.3.2 Construct Sources

Items of the constructs were drawn from well established

studies and were modified to suit the present study. The table 4.2

shows the sources from which items of the constructs were drawn.

Table 4.2 Sources of the Constructs

Constructs Source

Self Efficacy Compeau and Higgins (1995),

Bandura, (1977)

Awareness Sathye (1999)

Perceived Usefulness Agarwal and Prasad,(1999); Venkatesh

and Davis, 1996, Wang et al., (2003)

Perceived Ease of Use Agarwal and Prasad, (1999); Davis et

al.,(1989), Moore & Benbasat (1991),

Venkatesh and Davis, (1996), Wang et

al.,(2003)

Perceived Security Cheung and Lee (2000)

Attitude Agarwal and Prasad, (1999); Taylor

and Todd(1995), Venkatesh and Davis

(1996), Wang et al., (2003)

Intention Agarwal and Prasad (1999), Venkatesh

and Davis(1996), Wang et al., (2003)

Bank Competence Bhattacherjee (2002)

Bank Benevolence Bhattacherjee (2002)

Bank Integrity Cheung and Lee (2000)

Disposition to Trust Cheung and Lee(2000)

Structural Assurances Cheung and Lee (2000)

Consumer Trust on

Internet Banking

Mayer et al., (1995), Cheung and Lee

(2000)

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4.4 Sampling Design

For this study it was decided to use stratified random sampling

of registered internet banking customers of banks in Coimbatore.

The final sample size was 655. The sampling and data collection

method is presented below.

4.4.1 Sampling and Data Collection Method

Banks in India fall under three broad categories i.e. Public

sector banks, Private sector banks and foreign banks. Customers can

be grouped under each of this stratum. A list of banks in Coimbatore

along with branch addresses was obtained by requesting a manager of

the regional processing centre of a private bank. The total number of

banks is Coimbatore was 249 as on January 2008. Of these Public

sector bank branches accounted for 193, private sector accounted for

52 bank branches and foreign banks accounted for a total of four

bank branches as shown in Table 4.3 below.

Table 4.3 Proportion of Bank Branches in Coimbatore

as on January 2008

In the first stage of sampling (using stratified random sampling),

two top ranked retail bank branches in terms of size of the customer

base, from each stratum of the public, private and foreign sectors were

Banks In Coimbatore Number of

branches

Proportion

Public sector banks 193 77%

Private sector banks 52 21%

Foreign Banks 4 2%

Total 249 100%

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chosen. Then, from the chosen set of six top bank branches, the

managers of each bank were approached for obtaining details of

internet banking users who had registered for internet banking before

January 2007 in each of the bank branches. The list, thus obtained

from managers of the banks contained 3679, 6546, 1234 numbers of

customers respectively.

In the second stage (using simple random sampling method),

using random table , 30 percent from each of the above mentioned

total was drawn and sample size was arrived at as 1104,1964,370 in

the public, private and foreign bank sectors respectively. These 3438

customers were approached for collecting responses for the study.

When these customers were contacted in person as well as over

phone, 1435 agreed to respond. All of them were contacted and

responses received over a period of one year from January 2008 to

December 2008.

Finally, usable number of questionnaires was 655. The final

sample from each stratum was 286, 346, and 23 from Public Sector,

Private Sector and Foreign banks. The final sample proportion is

presented in table 4.4 below.

Table 4.4 Final Sample Proportion

Bank group Sample size Proportion

(%)

Public sector banks 286 43.7

Private sector banks 346 52.8

Foreign Banks 23 3.5

Total 655 100

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4.4.2 Sample Size

In Structural Equation Modeling (SEM) techniques sample size

is what one has to be particularly careful about. The sample size in

this study was 655 bank customers who had registered for internet

banking before January 2007.

It is generally understood among statisticians that SEM requires

large sample sizes (Kline, 2005). More complex models may require

the estimation of more statistical effects, and thus larger samples are

necessary in order for the results to be reasonably stable. The type of

estimation algorithm used in the analysis also affects sample size

requirements. There is more than one type of estimation method in

SEM, and some of these may need very large samples because of

assumptions they make (or do not make) about the data.

According to Kline (2005), ‘With less than 100 cases, almost any

type of SEM analysis may be untenable unless a very simple model is

evaluated. Such simple models may be bare-bones. Sample sizes less

than 100 would be considered ‘small’. A sample between 100 and 200

subjects is considered ‘medium’ and is a better minimum, but again

this is not absolute because things such as the model’s complexity

must also be considered. Sample sizes that exceed 200 cases could be

considered ‘large’.’’

Another empirical guideline about sample size is given by

Breckler (1990), who surveyed 72 studies published in personality and

social psychology journals in which some type of SEM was conducted.

The median sample size across these studies was 198, which is

approximately, “medium” according to the guidelines mentioned by

Kline (2005). The range of sample sizes reported by Breckler was from

40 to 8,650 cases. A total of 18 studies (25percent) had sample sizes

greater than 500, but 16 studies (22percent) had fewer than 100

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subjects, or “small” sample sizes. One survey by MacCallum and

Austin (2000) of about 500 applications of SEM published in 16

different research journals from 1993 to 1997 found that about 20

percent of studies used samples of fewer than 100 cases.

Another consideration for sample size is that more complex

models— those with more parameters— requires larger samples than

more parsimonious models in order for the estimates to be

comparably stable. Thus, a sample size of 200 or even much larger

may be necessary for a very complicated path model. Although there

are no absolute standards in the literature about the relation between

sample size and path model complexity, the following

recommendations are offered by Kline (2005): ‘a desirable goal is to

have the ratio of the number of cases to the number of free

parameters be 20:1; a 10:1 ratio, however, may be a more realistic

target. Thus, a path model with 20 parameters should have a

minimum sample size of 200 cases.’

McQuitty (2004) suggested that when SEM is used, it is

important to determine the minimum sample size required in order to

achieve a desired level of statistical power with a given model prior to

data collection. Schreiber et al., (2006) mentioned that although

sample size needed is affected by the normality of the data and

estimation method that researchers use, the generally agreed-on value

is 10 participants for every free parameter estimated. Although there

is little consensus on the recommended sample size for SEM Sivo et

al., (2006), and Hoelter (1983) proposed a ‘critical sample size’ of 200.

In other words, as a rule of thumb, any number above 200 is

understood to provide sufficient statistical power for data analysis

while using SEM. This study meets the recommended size of above

200 samples. Hence the sample of 655 is considered sufficient and

justified.

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4.4.3 Place of Study

The study was conducted in Coimbatore city. Coimbatore is the

highest revenue earning district in Tamil Nadu and is called the

Manchester of South India. The city's industrial growth started in

1920’s and accelerated after independence. Of late, information

technology companies have started opening offshore development

centers in the city.

Major type of Industries located in Coimbatore include Textile

Mills, Power looms, Handlooms, Hosiery Units, Motor, Pumps and

Foundry Units, Wet grinder and accessories Units, Coir Industries,

Textile/Automobile Machinery/ Engineering Industries. Twenty

percent of the India’s Foreign Exchange is earned by Cotton Textile

units in Coimbatore. Coimbatore is developing as Tier II City with

respect to the IT Sector. It is considered a second line city next only to

major metros in India, along the lines of Chandigarh and Pune, which

are flowering as true indicators of economic growth in India.

More significantly, Coimbatore hosts almost all the major banks

in India and has the distinction of being one of the most active

commercial centers in South India.

Additionally Coimbatore was chosen for this study as the

investigator is located here, is familiar with the place, and has

personal contacts with some of the retail banking institutions in

Coimbatore.

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4.5 Pilot Study

Before going for the main study a pilot study was undertaken to

assess the reliability of the instrument using Cronbach’s Alpha, and

also to ascertain the viability of data collection. Seventy respondents

were selected from a population similar to those who were surveyed in

the main study. These respondents were internet banking registered

customers and included teaching professionals, bank managers and

other professionals.

The data collected from the pilot study was subjected to

reliability test using Cronbach Alpha to check for internal consistency.

Cronbach’s Alpha is the most prominent reliability coefficient. It

measures the reliability of a set of indicators. Value ranges between

zero to one (if all indicators have positive correlation). Greater than

0.70 is acceptable (0.60 accepted for survey research)

The construct reliability coefficient alpha arrived at, from the

pilot study data is presented in table 4.5 for all the constructs.

As can be seen from the table, all values range from 0.79 to

0.97. Seven of the thirteen have alpha scores greater than 0.90, while

five constructs have scores greater than 0.80 and one construct has

score of > 0.70.

The pilot test results showed that the constructs’ alpha

coefficients had an acceptable level (> 0.70) which is considered

sufficient (Nunnally, 1978).

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Table 4.5 Instrument’s Cronbach’s Alpha Reliability

Constructs No. of

Items

Cronbach’s

Alpha

Perceived Usefulness 5 0.97

Perceived Ease of use 3 0.83

Perceived Security 5 0.93

Attitude 4 0.87

Awareness 4 0.93

Self Efficacy 4 0.88

Bank Integrity 3 0.79

Bank Benevolence 3 0.96

Bank Ability 3 0.97

Disposition to Trust 3 0.92

Structural Assurances 3 0.86

Consumer Trust on Internet Banking 3 0.95

Intention 3 0.89

4.6 Psychometric Checks

A structured questionnaire was used as the instrument for the

study. Items selected for the constructs were mainly adopted from

prior studies to ensure content validity. However the instrument was

validated for the main study again for the sample size of 655.

Given the theory-driven approach to scale development, the

Confirmatory Factor Analysis (CFA) approach was employed for scale

validation. The measurement model of Structural Equation Modeling

called the Confirmatory Factor Analysis (CFA) helps in establishing

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validity and reliability. CFA combines ex ante theoretical expectations

with empirical data for factor validation, and is therefore a stronger

statistical method than alternative approaches such as exploratory

factor analysis (Bhattacherjee, 2002). CFA was performed using the

maximum likelihood as the model estimation technique. The various

psychometric checks performed are presented in a gist below while the

actual analysis of validity and reliability through the measurement

model are presented in the next chapter under analysis and

interpretation.

4.6.1 Validity

4.6.1.1 Content Validity

For the content validity, a thorough review of the literature was

conducted. As mentioned earlier, all items of the constructs have been

drawn from well established studies to ensure content validity. The

questionnaire was also validated by having a panel of experts (bank

managers and academicians) review it, after which necessary changes

were incorporated to improve both the content and clarity of the

questionnaire. The instrument was tested through two stages. In the

first stage, the two English faculty members reviewed the modified

instrument to ensure the clarity of items and the accuracy of the

language. In the second stage, a panel of experts was selected to

establish face and content validity of the instrument. The panel of

experts consisted of six individuals - four members of the banking

industry, who had earlier participated in the instrument development

and two PhD students, who were fluent in English and who had

experience in fields related to the instrument design and technology

use. The reviewed questionnaire was then piloted before being

accepted as the final version. This process was followed to ensure the

validity, clarity, and consistency with the main purpose of this

research.

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4.6.1.2 Convergent Validity

This study establishes convergent validity by checking the

values of factor loadings and the Average Variance Extracted (AVE).

For the convergent validity the factor loadings and Average Variance

Extracted should be greater than 0.5 (Fornell and Larcker, 1981).

4.6.1.3 Discriminant Validity

To examine discriminant validity, the shared variances between

factors is compared with the average variance extracted of the

individual factors (Fornell & Larcker, 1981). The shared variances

must be lower than the AVE’S of all the individual factors.

4.6.1.4 Nomological Validity

Nomological validity is a form of construct validity. It is the

degree to which a construct behaves as it should within a system of

related constructs called a nomological set. Nomological validity is

particularly important while using SEM. Nomological validity gives the

overall perspective of the model. It refers to whether measures are

related to other constructs in a way that is theoretically meaningful. It

is required in SEM that nomological validity is established. Assessing

the nomological validity of any scale requires specifying the construct

within a nomological network of antecedent and consequent variables,

in order to examine the predictive ability of the focal scale.

Conceptual distinction and causality between beliefs and

intentions are derived from Fishbein and Ajzen's (1975) typology of

beliefs, attitude, and intention in the social psychology literature.

Attitude mediates the impact of beliefs on intention. In consumer-

based e-commerce contexts, trusting intention represents users'

willingness to engage in subsequent transactions with online firms

(Jarvenpaa et al., 2000). Higher levels of trust in a firm will therefore

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lead to greater intention on the part of users to engage in online

transactions. Hence, an individual's trust in an online firm is directly

related to their willingness to transact with that firm. The hypotheses

presented for this study are relevant from a practitioner's perspective,

since they present perceived usefulness, perceived ease of use and

perceived security as influencing attitude towards internet banking

adoption and trust as a viable means of improving transaction levels

of internet banks following a well established causal chain of beliefs,

Attitudes and intentions. Nomological validity is typically established

by the strength of the directional relationship between a measure and

theoretically related constructs (Peter & Churchill, 1986).In this study

it is seen that all the relationships are positive and significant at the

.001 significance level, providing evidence of nomological validity.

4.6.2 Reliability

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).

The internal reliability of the measurement models was tested

using Cronbach’s alpha and Fornell’s composite reliability (Fornell and

Larcker 1981). The Cronbach’s reliability coefficients of all variables

should be higher than the minimum cutoff score of 0.70 (Nunnally

1978; Nunnally and Bernstein, 1994).

Composite reliability should be greater than the benchmark of

0.7 to be considered adequate (Fornell and Larcker 1981). Reliability

and convergent validity of the factors were estimated by checking

composite reliability and average variance extracted (Fornell and

Larcker, 1981) and is presented in the next chapter.

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4.7 Tools Used for Data Analysis

The tools used for analysis consisted of Structural Equation

Modeling (SEM) including Confirmatory factor analysis (CFA) using

AMOS (Analysis of Moment Structures) 18. Additionally Multiple

regression analysis and one way ANOVA were used.

To empirically validate the extended TAM model, Structural

Equation Modeling was used. One way ANOVA was used for

examining differences in consumer intention to use internet banking

among across select demographic variable and multiple regression

was used to find out the influence of select demographic variables

(Age, education and Income) on consumer intention to use internet

banking. The following section briefly describes the tools used for data

analysis in this study.

Structural Equation Models (SEMs) describe relationships

between variables. It is similar to combining multiple regression and

factor Analysis. SEM offers a more effective way of dealing with multi-

co linearity, and has methods for taking into account the unreliability

of consumer response data. SEM consists of two components: a

measurement model linking a set of observed variables to a usually

smaller set of latent variables and a structural model linking the

latent variables through a series of recursive and non-recursive

relationships.

Confirmatory Factor Analysis (CFA) corresponds to the

measurement model of SEM. Confirmatory Factor Analysis (CFA) is

theory or hypothesis driven. With CFA it is possible to place

substantively meaningful constraints on the factor model. Researchers

can specify the number of factors or set the effect of one latent

variable on observed variables to particular values. CFA allows

researchers to test hypotheses about a particular factor structure.

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Additionally, One Way Analysis of Variance (ANOVA) was used

to test the significant difference between age, gender, income and

education groups and multiple regression analysis was used to find

out the influence of age, education and income on consumer intention

to use internet banking.

Given below is a brief explanation of the above mentioned tools

of analysis.

4.7.1 Structural Equation Modeling

Structural Equation Modeling (SEM) is a family of statistical

models that seek to explain the relationships among multiple

variables. In the process, the structure of interrelationships expressed

in a series of equations is examined, similar to a series of multiple

regression equations. These equations depict all of the relationships

among constructs (both the dependent and the independent).

Constructs are unobservable or latent factors represented by multiple

variables. A latent construct is a hypothesized and unobserved

concept that can be represented by observable variables. It is

measured indirectly by examining consistency among multiple

measured variables, also refered to as manifest variables or indicators.

SEM’s foundation lies in two familiar multivariate techniques : factor

analysis and multiple regression analysis.

Structural Equation Modeling (SEM) takes a confirmatory

approach (i.e. hypotheses testing approach), to the analysis of a

structural theory bearing on some phenomenon. Typically this theory

represents “causal” processes that generate observations on multiple

variables (Bentler, 1988).

Structural Equation Modeling is a technique of specifying,

estimating, and evaluating models of linear relationships among a set

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of observed variables. SEM models consist of observed variables (also

called manifest or measured variables) and unobserved variables (also

called underlying or latent variables) that can be independent

(exogenous) or dependent (endogenous) in nature. Latent variables are

hypothetical constructs that cannot be directly measured, and in SEM

are typically represented by multiple measured variables that serve as

indicators of the underlying constructs. The SEM model is an ‘a

priori’ hypothesis about a pattern of linear relationships among a set

of observed and unobserved variables. The objective in using SEM is

to determine whether the ‘a priori’ model is valid, rather than to ‘find’

a suitable model (Gefen et al., 2000)

In its broadest sense, SEM models represent translation of a

series of hypothesized cause-effect relationships between variables

into a composite hypothesis concerning patterns of statistical

dependencies (Shipley, 2000). The relationships are described by

parameters that indicate the magnitude of the effect (direct or indirect)

that independent variables (either observed or latent) have on

dependent variables (either observed or latent).

Several aspects of SEM set it apart from the older generation of

multivariate procedures. First, it takes a confirmatory rather than an

exploratory approach to the data analysis. Second, whereas traditional

multivariate procedures are incapable of either assessing or correcting

for measurement error, SEM provides explicit estimates of these error

variance parameters. Third, although data analyses using the former

methods are based on observed measurements only, those using SEM

procedure can incorporate both unobserved (latent) and observed

variables. Finally, there are no widely and easily applied alternative

methods for modeling multivariate relations, or for estimating point

and/or indirect effects; these important features are available using

SEM methodology.

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Structural Equation Modeling (SEM) is widely used in behavioral

research. SEM is used in this study because of the following three

distinct characteristics:

1. Estimation of multiple and inter-related dependence

relationships.

2. An ability to represent unobserved concepts/ latent variables in

these relationships and correct for measurement error in the

estimation process.

3. Defining a model to explain the entire set of relationship.

AMOS (Analysis of Moment Structures) is an easy to use Structural

Equation Modeling (SEM) program that tests relations among

observed and latent variables and then uses those models to test

hypotheses and confirm relationships. Some of the advantages of

AMOS are, Graphical language, no need to write equations or type

commands, easy to learn, user-friendly features such as drawing

tools, configurable toolbars, and drag and drop capabilities, fast.

Models that once took days to create can now be completed in

minutes using AMOS.

The general SEM model can be decomposed into two sub

models: a measurement model and a structural model. The

measurement model defines relations between the observed and

unobserved variables. In other words it provides a link between scores

on a measuring instrument (the observed indicator variable) and the

underlying constructs they are designed to measure (the unobserved

latent variables). Therefore the measurement model represents the

Confirmatory Factor Analysis (CFA) model described in the next

section. In contrast, the structural model defines relations among the

unobserved variables. Accordingly, it specifies the manner by which

particular latent variables directly or indirectly influence (cause)

changes in the values of certain other latent variables in the model.

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In other words the measurement model 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.

It can also be said that CFA is a way of testing how well the measured

variables represent a particular construct. The purpose of CFA is

twofold:

1) It is used as a validity procedure in the measurement model.

2) It confirms a hypothesized factor structure

On the other hand structural model is concerned with how

constructs are associated with each other and is used for hypotheses

testing.

Data in this study was analyzed using the two step approach

suggested by Anderson and Gerbing’s (1988), whereby the estimation

of the confirmatory measurement model precedes the estimation of the

structural model. Given below is a brief description of the CFA and

structural model.

4.7.1.1 Measurement Model - CFA

Confirmatory Factor Analysis (CFA) is used when the researcher

has some knowledge of the underlying latent variable structure. Based

on knowledge of the theory, empirical research, or both, relationships

between observed measures and the underlying factors are postulated

‘a priori’ and then the hypothesized structure is tested statistically.

Once a theory has been proposed, it can be tested against empirical

data. The process of testing a proposed theoretical model is commonly

referred to as the “confirmatory’ aspect of SEM (Raykov and

Marcoulides, 2000).

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The CFA is different from Exploratory Factor Analysis (EFA), in

that, EFA allows for theory development and does not require ‘a priori’

hypotheses about how indicators about how indicators are related to

underlying factors.

The technique of CFA analyses ‘a priori’ measurement models in

which both the number of factors and their correspondence to the

indicators are explicitly specified. In CFA, factor loadings are usually

interpreted as regression coefficients that may be in standardized or

un-standardized form. (Kline, 2005). 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

‘measurement model’.

4.7.1.2 Structural Model- Hypotheses Testing

The Structural Equation Model is concerned with how

constructs are associated with each other and is used for hypotheses

testing. First the structural model’s validity is established and overall

fit assessed after which the structural relationships hypothesized are

tested.

4.7.2 Multiple Regression Technique

Multiple regression analysis is a statistical technique that allows

researchers to use more than one independent variable to predict a

single dependent variable. It can also show how a set of independent

variables explain a proportion of the variance in a dependent variable

at a significant level. Brace, Kemp, and Snelgar (2006) specify four

conditions for using multiple regression technique in statistical

analysis:

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There are linear relationships between the predictor and

dependent variables (i.e., the relationship follows a straight

line).

The criterion variable is measured on a continuous scale such

as interval or ratio scale.

The predictor variables are measured on a ratio, interval, or

ordinal scale.

When there are a large number of observations. The number of

participants must substantially exceed the number of predictor

variables used in the regression. The absolute minimum is five

times as many participants as predictor variables.

In this study multiple regression is applied to find out the impact of

select demographic variables (Age, education and Income) on

consumer intention to use internet banking.

4.7.3 One Way ANOVA

ANOVA is a statistical technique for examining the differences

among means for two or more populations. Essentially ANOVA is used

as a test of means for two or more populations. The null hypothesis,

typically, is that all means are equal. In one way ANOVA, the

dependent variable is denoted by Y and the independent variable by X.

X is a categorical variable having c categories. There are n

observations on Y for each category of X.

In examining the differences among means, one way analysis of

variance involves the decomposition of the total variation observed in

the dependent variable. This variation is measured by the sums of

squares corrected for the mean (SS). Analysis of variance is so named

because it examines the variability or variation in the sample

(dependent variable) and, based on the variability, determines whether

there is reason to believe that the population means differ.

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In analysis of variance, two measures of variation are estimated:

within groups and between groups. Within groups variation is a

measure of how much the observations, Y values, within the group

vary. This is used to estimate the variance within a group in a

population. It is assumed that all groups have the same variation in

the population. However, because it is not known that all the groups

have the same mean, the variance of all observations cannot be

calculated together. The variance for each of the groups must be

calculated individually, and these are combined into an “average” or

“overall” variance.

In this study one way analysis of variance is used to find out the

difference, if any, on consumer intention to use internet banking,

based on their ages, genders, education levels and income levels.

The description of the tools used concludes this chapter. The

next chapter presents the analysis and interpretations including

assessment of the measurement model, validity and reliability checks,

and, model fitness followed by hypotheses testing.