chapter 4 research design and methodology -...

Chapter 4

Research Design and Methodology

96

Chapter 4: Research Design and Methodology

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This chapter is concerned with formulating the plans and procedures, measurement instrument

development, methods for data collection, analysis and interpretation. The chapter proposes with

justification the methodology used for finding solutions to the research problems. This chapter

describes the various approaches for research and narrows down to describe the research

approach suitable for the current study.

4.1 Background

The previous chapters were dedicated to problem identification, formulating the research questions

and literature review to determine the work done in the field as well as research gap identification.

The previous chapter proposed the course of action to address these gaps by carrying out a

comprehensive study involving all stakeholders’ viz. Bank senior management, employees,

customers and technology solution providers. In the previous chapter, a model for internet banking

adoption, using constructs from extant literature was also proposed.

4.2 Research Design

The previous chapters discussed the first stage of the study i.e. formulation of the research

definition. Figure 4.1 illustrates the approach for planning applied research. The researcher needs

to perform five activities simultaneously during the research design process viz. selecting a design,

choosing data collection approaches, inventorying resources, assessing feasibility of executing the

proposed approach and determining trade-offs, (Bickman et al, 1998).

Research Design is a “blueprint” for empirical research aimed at answering specific research

questions or testing specific hypotheses, and must specify at least three processes: (1) The data

collection process, (2) The instrument development process, and (3) The sampling process,

(Bhattacherjee, 2012).

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Figure 4.1: Applied Research Planning (Adapted from Bickman & Rog, 1998)

4.2.1 The research design used in this study

The present study comprised of eight phases to understand the problem of low usage of internet

banking from a broader perspective. The methodology employed in each phase is different. Figure

4.2 illustrates the research design employed in this study. The research design was formulated

based on literature review on the internet and internet banking. Existing literature revealed the

various methods researchers employed to study the broad area of internet banking and the

literature was classified into different categories as discussed in chapter 2. The present study was

conducted in different phases. The idea was that the results of all phases, when tied together at the

conclusion stage, would produce illuminating results as compared with looking at the results of

each phase separately. This is true in most mixed methods research where the sum is larger than

the parts.

Understand the problem

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Chose design/data

collection approaches

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trade-offs

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feasibility

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resources

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Design/Plan

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Definition

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4.3 Research approach

Broadly, research can be classified as inductive and deductive.

Deductive research is a study in which a conceptual and theoretical structure is developed and then

tested by empirical observation; thus, particular instances are deduced from general inferences. For

this reason, the deductive method is referred to as moving from the general to the particular,

(Collis & Hussey, 2009).

Inductive research is a study in which theory is developed from the observation of empirical

reality; thus, general inferences are induced from particular instances, which is the reverse of the

deductive method, (Collis & Hussey, 2009)

Figure 4.3 depicts and explains the deductive and inductive approach of research.

Figure 4.3: The Deductive and Inductive approach to research

Inductive and deductive research is important for the advancement of science. It appears that

inductive (theory-building) research is more valuable when there are few prior theories or

explanations. While, deductive (theory-testing) research is more productive when there are many

competing theories of the same phenomenon and researchers are interested in learning which

theory works best and under what circumstances, (Bhattacherjee, 2012).

Theories have been developed for user acceptance of technology, and extant literature reveals that

researchers have used TRA, TPB, TAM, DTPB, IDT, SCT and other extended and integrated

models for studying internet banking. In this research, deductive research was selected as it was

found suitable for addressing the research problem. It was felt that constructs from various theories

can be borrowed, and a model appropriate for the Indian context can be developed and tested from

Theory

Hypothesis

Observation

Confirmation

Theory

Tentative Hypothesis

Pattern

Observation

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the existing theories. The goal of theory testing is not just to test a theory, but also to possibly

refine, improve, and extend it, (Bhattacherjee, 2012). In the last phase of the study, the proposed

model would be subject to refinement, improvement and making it more parsimonious.

4.4 Research Paradigm

A research paradigm is a framework that guides how research should be conducted, based on

peoples philosophies and their assumptions about the real world and nature of knowledge, (Collis

& Hussey, 2009).

4.4.1 Positivism and Interpretivism

Positivism is an approach that believes that society can be studied using similar scientific

techniques to those used in natural sciences such as physics, chemistry and biology. Interpretivism

is an approach emphasizing that people have consciousness involving personal beliefs, values and

interpretations and this influences the way they act, (Browne, 2006).

The main research objective in the study was to uncover the reasons for the low usage of the

internet-banking channel. In order to identify the reasons for this, eight phases of study were

envisaged. The research would use semi-structured interviews to understand the views of senior

managers, employees and customers about internet banking to confirm the existence of the

constructs found in literature. Survey questionnaires would be used to discover the perceptions of

the bank employees, use a survey questionnaire to test the proposed model for internet banking

acceptance, in addition to collecting information about the functional aspects of internet banking

websites, website traffic and the financial performance data of banks. Therefore, a positivistic

approach was appropriate for the quantitative part of the study. For the qualitative part of the

study, use semi-structured interviews, which require an interpretive approach.

4.5 Broad types of Research Designs

There are five broad type of research designs viz. experimental design, cross-sectional design, case

study design and comparative design. The following Table 4.1 enlists the research strategy based

on the research design.

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Table 4.1 Research strategy and research design, (Bryman, 2008)

Research Design Quantitative Qualitative

Experimental Quantitative comparison

between the experimental and

control group with regard to

the dependent variable.

No typical form.

Cross-sectional Survey research or structured

observation of a sample at a

single point in time.

Qualitative interviews or focus

groups at a single point in time

Longitudinal Survey research on a sample

on more than one occasion.

Ethnographic research over a

long period, qualitative

interviewing on more than one

occasion.

Case study Survey research on a single

case with a view to revealing

important features about its

nature.

Intensive study by

ethnography or qualitative

interviewing of a single case,

which may be an organization,

family or community.

Comparative Survey research in which there

is a comparison between two

or more cases.

Ethnographic or qualitative

interview research on two or

more cases.

4.5.1 Justification for the Selection of the Research Design in this study

The study was proposed to be done in eight phases as discussed in the previous chapter. In phase

1, the plan was to find the state of the internet in India and find factors, which drive/inhibit internet

usage. In phase 2, the plan was to evaluate the websites of banks at a single point of time. In phase

3, the plan was to understand the views of senior bank managers about internet banking and to

understand the perceptions of bank employees about internet banking. In phase 4, the plan was to

investigate financial implications and operational issues related to internet banking. In phase 5, the

plan was to understand the perception of branch employees towards internet banking. In phase 6,

the financial performance of banks who have adopted internet banking was planned to be

investigated, to determine if there is a relation with website traffic. In phase 7, the plan was to

measure the satisfaction level of internet banking users. In phase 8, the plan was to hypothesize

and test a model for user acceptance of internet banking. This model was planned to be tested for

the effects of moderating variables like age, gender, socio-economic status by forming groups. The

study gathers information only once at a single point of time and will also compare the acceptance

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of internet banking among groups. The research design chosen for this study was therefore cross-

sectional and causal comparative.

4.6 Research Method

Many methods do exist for collecting evidence to investigate a problem. Figure 4.4 illustrates the

different research methods available to solve a research problem.

Figure 4.4: The variety of research methods to solve a research problem (Adapted from

Brown, 2006)

4.6.1 Qualitative, Quantitative and Mixed methods

There are three design approaches: qualitative, quantitative and mixed methods (Bryman, 2008)

4.6.1.1 Qualitative research

Qualitative research can be construed as a research strategy that emphasizes on words rather than

quantification in the collection and analysis of data. Qualitative research is a means for exploring

and understanding the meaning individuals or groups ascribe to a social or human problem. The

process of research involves emerging questions and procedures, data typically collected in the

participant’s setting, data analysis inductively building from a particular to a general theme, and

the researcher making interpretations of the meaning of the data, (Creswell, 2009).

4.6.1.2 Quantitative research

Quantitative research can be construed as a research strategy that emphasizes quantification in the

collection and analysis of data. Quantitative research is a means for testing objective theories by

Problem to be

investigated

Questionnaire

Participant

Observation

Use of Secondary

Data

Comparitive methods

Experiments

Structured and

Unstructured

Interviews

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examining the relationship amongst variables. These variables, in turn, can be measured, typically

on instruments, so that numbered data can be analysed using statistical procedures, (Creswell,

2009).

4.6.1.3 Mixed methods research

Mixed methods research integrates quantitative and qualitative research within a single project so

that the results are mutually illuminating. The goal of mixed methods research is not to replace

either Quantitative or Qualitative approaches but rather to draw from the strengths and minimize

the weaknesses of both in a single research study and across studies, (Johnson & Onwuegbuzie,

2004). Researchers should collect multiple data using different strategies, approaches, and

methods in such a way that the resulting mixture or combination is likely to result in

complementary strengths and non-overlapping weaknesses, (Johnson & Turner, 2003)

4.6.1.4 Selection and justification for the choice of research method for the study

A comparison of Quantitative, Qualitative and Mixed methods research is shown in Table 4.2

This serves as a guideline for selection of the appropriate method.

Table 4.2: Quantitative, Qualitative and Mixed methods, (Creswell, 2009)

Quantitative Methods Mixed methods Qualitative Methods

Pre-determined

Instrument based

questions

Performance data,

attitude data,

observational data and

census data

Statistical analysis

Statistical interpretation

Both pre-determined and

emerging methods

Both open and closed-

ended questions

Multiple forms of data

drawing on all possibilities

Statistical and text analysis

Across databases

interpretation

Emerging methods

Open-ended questions

Interview data,

observation data and

audio-visual data

Text and image analysis

Themes patterns,

interpretation

(Bryman, 2006) in their article based on content analysis of 232 social sciences articles in which

quantitative and qualitative research was combined, isolated the following justification for multi-

strategy research: triangulation: convergence, corroboration, correspondence or results from

different methods. In coding triangulation, the emphasis was placed on seeking corroboration

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between quantitative and qualitative data. Complementarity: ‘seeks elaboration, enhancement,

illustration, clarification of the results from one method with the results from another’, (Greene et

al., 1989: 259). Development: ‘seeks to use the results from one method to help develop or inform

the other method, where development is broadly construed to include sampling and

implementation, as well as measurement decisions’, (Greene et al., 1989: 259). Initiation: ‘seeks

the discovery of paradox and contradiction, new perspectives of [sic] frameworks, the recasting of

questions or results from one method with questions or results from the other method’, (Greene et

al., 1989: 259). Expansion: ‘seeks to extend the breadth and range of enquiry by using different

methods for different inquiry components’, (Greene et al., 1989: 259).

In the current study, the plan was to explore relevant documents related to the internet to determine

reasons for the low penetration of the internet in India. And, to develop a model for finding factors

that drive /inhibit internet growth, by meeting bank customers and bank officials to identify the

reasons for the low usage of this channel, and reading extant literature to uncover possible

answers. Then the plan of action was to study internet banking websites based on their functional

aspect, conduct interviews to find the perception of senior bank managers on internet banking, to

conduct a survey research to find the perception and opinion of bank employees about internet

banking and finally to develop a model to explain user acceptance of internet banking. As such,

the study has eight phases and the data collection methods are different for all the four phases. In

phase 1, the plan was to study the state of the internet and identify factors that drive/ create

hindrances to the growth of the internet and develop a model to explain the same. In phase 2, the

plan was to evaluate internet-banking websites of banks operating in India by directly viewing

their websites. In phase 3, the plan was to conduct a semi-structured interview with senior bank

managers, technology service providers, employees, users and non-users of internet banking which

would have both open and closed ended questions. In phase 4, the plan was to investigate financial

implications and operational issues related to internet banking. In phase 5, the plan was to capture

the perception of bank employees about internet banking through a questionnaire, which would

have only closed ended questions. In phase 6, the plan was to study the relation between website

traffic and the financial performance of banks. In phase 7, the plan was to measure the satisfaction

level of internet banking users and the final plan was to test the proposed model developed by

using key constructs borrowed from previous theories and information systems research using an

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advanced statistical method for internet banking acceptability. The research plan had all the

characteristics of a mixed method research.

4.7 Data Collection

As the research was conducted using a mixed method approach. The data collection was done

using more than one way using documents from the Reserve Bank of India (RBI), Indian Banks

Association (IBA), Semi-structured questionnaires and Survey questionnaires. This approach of

collecting data is called triangulation. In business and management, there is a need for

triangulation in order to explore for accuracy of the data and alternate explanation. The idea is to

collect data by different means and hope there is convergence of the truth, (Adams et al, 2007).

(Stake, 1995) proposed different protocols for triangulation

Data source triangulation: The analyst asks whether or not they are likely to be reporting is

likely to be a constant at other times or circumstances

Investigator triangulation: Other researchers look at the same scene. Alternatively, findings can

be presented to other researchers to discuss alternative interpretation.

Theory triangulation: Multiple investigators agree as to the meaning of the phenomenon.

Methodical triangulation: This involves using a variety of data collection methods to build

confidence in the interpretation made.

Member triangulation: The respondent is asked to review the material for accuracy and to add

further comments that might aid description and explanation. By doing so, the actors personally

help to triangulate the researchers observation and interest.

4.7.1 Data collection method employed in this study

This study used semi-structured interviews, survey questionnaires, documents pertaining to

internet banking. Figure 4.5 shows the data collection methods used in this study.

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Figure 4.5: The data collection approach used in the study

The study was conducted in 8 phases, and the requirement of each phase captures internet banking

from a different perspective. Triangulation may also help to uncover the deviant or off-quadrant

dimension of a phenomenon. Different viewpoints are likely to produce some elements, which do

not fit a theory or model, (Jick, 1979). If the findings are corroborated across different approaches

then greater confidence can be held in the singular conclusion. If the findings conflicts, then the

researchers have greater knowledge and can modify interpretation and conclusions accordingly. In

many cases, the goal of mixing is not to search for corroboration, but rather to expand one's

understanding, (Onwuegbuzie & Leech, 2004).

In cases where a survey questionnaire was used, the questionnaire was sent along with a cover

letter informing the participant about the purpose of the research with an assurance that the

responses will be kept confidential. This is as suggested in literature to overcome non-response

bias, (Churchill, 1995, Singer, 1978).

Phase 1: The state of the internet as to the number of users in different countries. Factors, which

act as drivers and hindrance to internet growth, were identified by using secondary data from the

Internet Society (ISOC).

Phase 2: In order to evaluate the websites from the user’s viewpoint based on the functionality

dimension. The internet-banking website of banks in India was investigated by manually accessing

them. Information about the multilingual option was checked on the individual banks websites.

Here the unit of analysis is the web page.

Semi-structured interviews

Document/

website studies

Survey Questionnaire

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Phase 3: The perceptions of senior bank management, employees, users and non-users of internet

banking of were captured through semi-structured interviews and questionnaires. As it is, a known

fact that senior managers do not have the time and inclination to fill a survey questionnaire a more

pragmatic approach was taken. A road map of questions were prepared, (see Appendix B) and

posted to them wherever possible before meeting them in person for the interview. This made

them comfortable while responding. This approach is the semi-structured approach. When

customers at the bank were approached with questionnaires, the response from them was negative

and many of them refused to take part in the study. The reason for refusal to participate in most

cases was that they were in hurry to finish their banking tasks and attend to some other important

tasks. The other reasons that can be attributed was lack of trust when approached by a stranger in

the bank premises. It was found that instead of administering a formal questionnaire customers felt

easy when asked questions in an informal way, by explaining the intent of the research. The bank

customers’ degree of confidence improved when they were told that the research was for academic

purpose only, which was evident from the fact from their openness in communication.

Initially certain screening questions were asked to identify users and non-users of internet banking.

A list of guided probing questions, (see Appendix B) was used to gather opinions. The interview

was conducted without any bias to the positive and negative aspects of internet banking and the

interviewee was free to express his opinion for and against internet banking.

Phase 4: Information pertaining to internet banking was obtained by filing 13 questions under the

Right To Information Act, 2005 to all public sector banks, (see Appendix C).

Phase 5: For determining the perception of bank employees towards the internet, banking extant

literature review revealed that one such research was done in Greece, (Lymperopoulos &

Chaniotakis, 2004). The questionnaire was replicated with some modification as per the Indian

context, (see Appendix B) and was administered to bank employees.

Data was collected by visiting bank branches personally and explaining the objective of the study

to the Branch Managers and assuring them of full confidentiality of the information the employees

provide. The Branch Managers were requested to get the questionnaire filled from the employees.

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The responses were collected either on the same day after working hours or on the next day as the

Branch Managers did not want to disturb the employees during the customer working hours. A

paper-based questionnaire was used, as it was difficult to get email addresses of bank employees

and motivate them to participate in this study electronically.

Phase 6: Financial performance measures of banks in India from their yearly audited results for

the year ending March 2011 from Indian Banks Association and website traffic statistics from

Alexa, (a web traffic reporting company).

Phase 7: Data collection was done using a self-completion questionnaire, which had 13 questions

in all regarding satisfaction of internet banking. The questions were pertaining to content,

accuracy, and format, ease of use and timeliness of the website. Two methods were used for

administering the questionnaire, a Google form was created, and invitations to participate were

sent via emails to respondents who were randomly selected from a mailing list. The emails had a

covering letter with a link to the form and a paper based version of the form.

Phase 8: Data collection for testing the proposed model was done using a self–completion

questionnaire. Two ways were used for administering the questionnaire, a web form was created,

and invitations to participate were sent via emails to respondents who were randomly selected

from a mailing list. The emails had a covering letter with a link to the form and a paper based

version of the form.

Table 4.3 shows that similar variables being used during the 8 phases of the study to ensure

accuracy and confidence while drawing conclusions. The data source used in this study therefore

supports triangulation.

10

9

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110

4.8 Sampling Techniques

Sample selection in a survey based research is important, as the time constraints and the nature

of this study is dynamic and the entire population cannot be considered. Sampling techniques can

be categorized into two heads 1.] Probability Sampling 2.] Non-Probability Sampling.

Probability Sampling has an advantage, as the sampling error can be calculated. Methods of

probability sampling include random sampling, systematic sampling, and stratified sampling.

Non-probability sampling involves selecting samples such that the possibility of being selected is

known. Non-probability sampling includes convenience sampling, judgment sampling, quota

sampling, snowball sampling.

Figure 4.6 illustrates the Taxonomy of Sampling Designs (Dillon et al. Marketing Research, third

edition, pp. 233), which is useful for selecting the sampling method

Figure 4.6: Sampling Designs (Adapted from Dillon et al., 1993)

Sampling Design

Probability Sampling

Stratified Sampling

Proportionate Allocation

Optimal Allocation

Simple Random Sampling

Cluster Sampling

One-Stage Cluster

Systemic Sampling

Two-Stage Cluster

Nonprobability Sampling

Convenience Sampling

Quota Samples

Judgemental Sampling

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4.8.1 Selecting the sampling technique and justification for the choice

Phase 1: ISOC conducted a global online survey to find the opinion of individuals from different

nations about increasing government intervention to control the internet. The sampling method

adopted was convenience sampling.

Phase 3: A person at the senior management level was identified through the members of the

Thesis Advisory Committee for each of the three categories of banks. The objective of the study

was explained to the identified persons, and after interviewing them, the snowball approach was

used, in which the senior manager identified the right people in either their bank or their friends

in other banks for further interviewing. For interviews with users and non-users of the internet,

banking purposive sampling was used.

Phase 5: In this phase, the plan was to capture the perception of bank employees about internet

banking. In order to get a broader view of all the categories of banks having operations in India,

the research experts and the Thesis Advisory Committee (TAC) were of the view that Stratified

Sampling should be conducted for determining the perceptions of senior management and

employees of the banks. The selection of stratified samples was done using the following

procedure, (Dillon et al. marketing research third edition pp. 261):

Deciding on the classification variable and the number of strata to be employed.

Obtaining a list of all elements in the target population; and based on the classification variable

each bank is assigned to one stratum.

Each bank in every strata was numbered from 1 to N.

The number of bank employees to be selected from each stratum was decided.

The target audience i.e. bank senior managers, bank employees were divided into different

segments, and then samples were drawn from them. The various strata was formed on the basis

of different categories of the banks viz. nationalized, new private, old private, co-operative

scheduled and foreign banks.

Phase 7 and Phase 8

Although random sampling would have been the best method to get, a representative sample of

the population in this study, random sampling could not be employed for the following reasons

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1. Banks were reluctant to reveal details of the customers using internet banking.

2. The population size was not known

3. The objective was to understand the decision factors that led to the adoption of internet

banking and not to test the differences between adopters and non-adopters of internet banking.

This required respondents to be from a highly specific demographic profile. The respondents for

this study had to be computer literate, having internet access and using the internet-banking

channel.

Due to these reasons, convenience sampling had to be used in this phase of the study.

As per (Saunders, 2000), “… the inability to specify a sampling frame may indicate the use of

one or a number of non-probability sampling techniques.” (Saunders, 2000) also recommend use

of purposive or judgment sampling in such cases whereby the researcher chooses the cases that

will facilitate the answer for the research questions. (Creswell, 2003), argues that approaching

people for collection of data was mainly based on their convenience and availability to complete

the questionnaire.

4.9 Sample Size

(Sekaran, 2006), discusses a pragmatic approach for selection of sample size proposed by,

(Roscoe, 1969) 1.] Sample sizes larger than 30 and less than 500 are appropriate for most

research. 2.] Where samples are to be broken down into subsamples based on gender,

socioeconomic status, age then a minimum sample size of 30 for each category is necessary. 3.]

In multivariate research, the sample size should be preferably 10 times or more than the number

of variables in the study.

In phase 1, the data was obtained from an online survey conducted by the Internet Society to

discover the opinion of the internet users on various issues. Questions pertaining to the four

hypothesized constructs were identified and were used in this research. Permission to use this

data was obtained under the Creative Commons Attribution-Non-Commercial-Share Alike 3.0

Unported License. 535 responses from India were selected from 10000 responses in the dataset,

which covered 20 countries.

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In phase 2, Fifty-two banks having internet banking facility were chosen in the study, of which

26 were public sector banks, 20 were private sector banks and 6 were foreign sector banks.

Phase 3 of the research was qualitative in nature. A semi-structured interview was conducted to

gather information about the view of the bank’s senior management on internet banking. Here,

only a small number of individuals representing all the various categories of banks were chosen.

The sample size used in the semi-structured interviews was 32, and 22 of these were male and 10

females.

In phase 4, applications under the RTI were posted to public sector banks under the Act’s

purview.

Phase 5 of the research uses factor analysis. The primary purpose of choosing a sample size is to

obtain factors that are stable and should be close estimates of the population factors. Literature

on sample size required for factor analysis shows difference of opinion among researchers on

this issue. There are two schools of thought with regard to the minimum sample size for factor

analysis. One suggestion is about the total number of observations (N). (Gorsuch, 1983;

Guilford, 1954; Kline, 1979; Hutcheson & Sofroniou, 1999) suggested an absolute minimum

varying from 100 to 200 samples. The other suggestion is the subject to variable ratio (N:p).

(Nunnally, 1978; Hair et al., 1998; Baggaley, 1982; Brislin et al., 1974) recommend a ratio

varying from 2:1 to 20:1. (MacCallum et al., 1996) argue that the minimum sample size or the

minimum ratio of the sample size to the number of observed variables is not invariant across

studies and is dependent on the level of communality of the variables and the level of over

determination of the factors. 170 responses were obtained for analysis.

In phase 6, the sample comprised of 25 public sector banks, 6 new private sector banks, 10 old

private sector banks and 4 foreign banks in India, in line with the banking structure. The unit of

analysis was the bank.

Phase 7 and Phase 8 of the research uses Structural Equation Modelling (SEM). It is the general

assumption that the bigger the sample size, lesser the sampling error. The determination of

sample size when using SEM depends on the estimation method and the complexity of the

model. In this study, the Maximum Likelihood Estimation (MLE) was used. (Hair et al, 1998),

recommend a minimum sample size of 100 to 150 for using the MLE method of SEM. In MLE,

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the sensitivity to detect differences in the data increases with increase in sample size, resulting in

indication of poor fit when sample size exceeds 400. (Hoelter, 1983), suggests that a sample size

of 200 is a critical sample size and all models should be tested for this sample size. (Kline, 2005)

suggests that the choice of sample size depends on the complexity of the model and models,

which are not parsimonious, should be tested with more than 200 samples. (Kline, 2005),

suggests a sample size of 5 to 20 times of the number of parameters to be estimated.

The choice of the sample size for SEM cannot be too small nor can it be too large. Statistical

results from a very small sample size are invalid and a large sample size will result in a poor fit

model. (Schumacker & Lomax, 1996) reported that most of the studies using SEM had a sample

size between 200 and 500 subjects. The proposed model was discussed with Prof. Avinandan

Mukherjee, of Montclair University after his presentation at NMIMS University. He was of the

opinion that the sample size should be somewhere between 200 to 400 samples. In phase 7 of the

study, 387 usable responses were obtained. In phase 8 of the response, the sample size was 300.

4.10 Measurement, Scale development and Questionnaire design

4.10.1 Measurement

Measurement is the process of describing some property of a phenomenon of interest, usually by

assigning numbers in a reliable and valid way, (Zikmund & Babin, 2009). Measurement refers to

careful, deliberate observations of the real world and is the essence of empirical research,

(Bhattacherjee, 2012). In the previous chapter, various theories were discussed and a model was

proposed. The variables in the model were also conceptualized. In order to create measures, there

is a need for operationalization of the constructs.

Operationalization: Most of the theoretical constructs defined in the proposed model were

multidimensional and use multiple questions for measurement of the same construct, thereby

increasing the reliability of the measurement. These multidimensional constructs are also called

Latent Variables. The purpose of operationalization was to develop items for measuring these

constructs, which have more than one underlying dimension. The operationalization of the

theoretical constructs were required in phase 1, phase 7 and phase 8.

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Phase 1: In this phase, secondary data was used and the questions corresponding to the

hypothesized constructs were identified as in Table 4.4.

Table 4.4: Constructs and corresponding items (phase 1)

Construct Items References

Government

Support

People need to have access to better and cheaper training

opportunities.

(ISOC survey,

2012)

Governments need to place a higher priority on expanding

the internet and its benefits in my country

(ISOC survey,

2012)

Local universities and technical institutes need to offer basic

and advanced computer and internet technical training.

(ISOC survey,

2012)

Tax reductions need to be given to small and medium-sized

businesses that are using the internet to conduct business.

(ISOC survey,

2012)

Governments should consider ways to provide easier access

to cheaper computers.

(ISOC survey,

2012)

Governments should consider ways to create or encourage

competition amongst Internet Service Providers (ISP).

(ISOC survey,

2012)

Government

Control

Government control would put limits on the content I can

access.

(ISOC survey,

2012)

Government control would make me fearful that my actions

were under surveillance

(ISOC survey,

2012)

Government control would limit my freedom of expression (ISOC survey,

2012)

Government control would make the internet too controlled (ISOC survey,

2012)

Government control would inhibit the growth of the internet (ISOC survey,

2012)

Government control would make me use the internet less (ISOC survey,

2012)

Attitude

Freedom of expression is guaranteed on the internet. (ISOC survey,

2012)

The internet is essential for my access to knowledge and

education.

(ISOC survey,

2012)

The internet does more to help society than it does to hurt it. (ISOC survey,

2012)

My life has improved due to using the internet. (ISOC survey,

2012)

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Phase 7: In this phase, the five constructs used for studying end user computing satisfaction of

internet banking was operationalized using the items shown in Table 4.5.

Table 4.5: Constructs and the corresponding items (phase 7)


Content

Does internet banking provide the precise

information you need?

(Doll et al, 1994;

Pikkarainen et al, 2006)

Does information content on the internet-

banking website meet your needs?

(Doll et al, 1994;


Does the internet-banking website provide

reports that meet your need?

(Doll et al, 1994;


Does the internet-banking website provide

sufficient information?

(Doll et al, 1994;


Accuracy

Is internet banking accurate?

(Doll et al, 1994;


Are you satisfied with the accuracy of the

internet-banking website?

(Doll et al, 1994;


Format

Is the information clear from the internet

banking website?

(Doll et al, 1994;


Is the website layout and format of providing

information good?

(Doll et al, 1994;


Ease of Use

Is internet banking user friendly?

(Doll et al, 1994;


Is internet banking easy to use?

(Doll et al, 1994;


Timeliness

Do you get the information you need in time?

(Doll et al, 1994;


Does the internet-banking website provide up to

date information?

(Doll et al, 1994;


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Phase 8: in this phase, many of the latent constructs were from existing literature used in TAM,

TPB, and SCT.

Perceived Usefulness (PU)

Perceived Usefulness was operationalized using the following items:


Perceived

Usefulness

(PU)

Using internet banking enables me to

accomplish my banking tasks quickly.

(Venkatesh et al., 2003)

I find internet banking very convenient for

managing my finance.

(Ramsay & Smith,

1999)

Internet banking enables me to utilize my time

effectively by not having to stand in long

queues at the bank counter.

(Walker and Johnson,

2005; Giannakoudi,

1999)

I find that internet banking is useful in

conducting banking transactions

(Venkatesh et al., 2003)

Internet banking enables me to access banking

services at any time.

(Lockett & Littler,

1997)

Internet banking is available 24 X 7 and

therefore enables me to carry out banking

whenever I like.

(Lockett & Littler,

1997)

With internet banking, I can access my bank

account even on bank holidays.

(Lockett & Littler,

1997)

Internet Banking enables me to reduce banking

cost, such as reduced bank charges and

transportation cost.

(Ramsay & Smith,

1999)

Internet banking enables me to get correct

information unlike branch banking where I get

inappropriate response from bank staff.

(Moon & Kim, 2001)

Internet banking eliminates geographic

limitation and increases flexibility and mobility

(Evans & Wurster,

1997)

Internet banking is more useful than other

existing channels like bank branches, ATMs

and phone banking.

(Thornton & White,

2001)

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Perceived Ease of Use (PEU)

Perceived Ease of Use was operationalized using the following items:


Perceived Ease

of Use (PEU)

It is easy to use internet banking. (Davis, 1989; Adams et al.,

1992)

It is easy to learn how to use internet

banking.

(Wang et al, 2003 ; Adams

et al., 1992)

My interaction with internet banking is clear

and understandable

(Davis, 1989; Agarwal &

Prasad, 1997; Moore &

Benbasat, 1991)

It is easy for me to become skilful at using

internet banking.

Wang et al., 2003

Using internet banking does not require a

lot of mental effort.

Venkatesh & Bala, 2008

Using internet banking increases the quality

of my banking services output with minimal

efforts.

Venkatesh & Bala, 2008

Internet banking is flexible to interact with. Venkatesh & Bala, 2008

Trust (TRU)

Trust was operationalized using the following items:


Trust (TRU)

I trust that the internet banking systems

not to disclose my personal information.

(Mukherjee & Nath, 2003)

I trust the banks to keep customer data

securely.

(Mukherjee & Nath, 2003;

Ramsay & Smith, 1999)

I trust the technology of internet banking. (McKnight & Chervany,

2002)

I trust the privacy statement on the bank’s

web page to guarantee privacy of my

personal data.

(McKnight & Chervany,

2002)

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Perceived Risk (PR)

Perceived Risk was operationalized using the following items:


Perceived Risk

(PR)

I am unsure if internet banking performs

satisfactorily.

(Cunningham et al., 2005;

Featherman & Pavlou, 2003)

Overall, I feel internet banking is risky. (Ndubisi & Sinti, 2006;


I believe that money can easily be stolen

while using internet banking

(Ndubisi & Sinti, 2006;


Internet banking Self-Efficacy (IBSE)

Internet Banking Self-Efficacy was operationalized using the following items:


Internet

Banking Self-

Efficacy (IBSE)

I would be confident in using internet

banking even if there is no one around

to show me how to use it.

(Compeau & Higgins,1995)

I would be confident about using

Internet banking even if I have never

used a system like it before.


I would be confident in using Internet

banking even if I have only the online

instructions for reference.

(Compeau &

Higgins,1995;Wang et al,

2003)

I would be confident in using internet

banking, if I had seen someone else

using it before trying it out myself.


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Internet Usage Efficacy (IUE)

Internet Usage Efficacy was operationalized using the following items:


Internet Usage

Efficacy (IUE)

I feel confident to open and close the

web browser like the internet explorer.

(Torkzadeh et al, 2006;

Brown, 2006)

I feel confident in entering the website

address on the address bar of the web

browser.

Self-developed for this study

I feel confident to login to the website

using my username and password.


I feel confident in accessing and

uploading information on social

networking websites such as facebook,

orkut, g+, twitter etc.


I feel confident in using search engines

like google, bing etc. to search for

information on the web

(Brown, 2006)

I feel confident chatting with people on

messenger.


I feel comfortable to buy products

online using credit /debit cards


I feel confident booking airline/train

tickets using the internet.


I feel confident in setting my browser

window properties as per my

requirement


I feel confident searching and

comparing products and services using

the internet.

(Torkzadeh et al., 2006)

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Subjective Norm (SN)

Subjective Norm was operationalized using the following items:


Subjective

Norm (SN)

.

My decision to adopt internet banking is

influenced by my friends.

(Ajzen, 1991; Fishbein &

Ajzen, 1975; Taylor & Todd,

1995)


influenced by my family/relatives.

(Mathieson, 1991; Ajzen,

1991)


influenced by my colleagues/peers.

(Taylor & Todd, 1995;

Ajzen, 1991)

Government support (GS)

Government support was operationalized using the following items:


Government

support (GS)

Government of India supports and

promotes usage of the internet and

ecommerce.

(Teo et al., 1997; Tan & Teo,

2000)

Government of India is active in setting

up facilities such as providing adequate

telecommunication facilities, which will

be an enabler of internet banking.


2000)

Government of India has framed good

regulations and laws for Information

Technology use.


2000; Giannakoudi, 1999)

Banks Initiative (BAI)

Banks Initiative was operationalized using the following items:


Banks Initiative

(BAI)

My bank promotes internet banking. Self-developed for the study

My bank updates the information on the

internet-banking website regularly.

Self-developed for the study

My bank offers a fully transactional

website, which is user friendly.


My bank updates me regularly about the

benefits of using internet banking.


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Trialability (TRI)

Trialability was operationalized using the following items:


Trialability

(TRI)

Before deciding whether to use internet

banking, I can properly try it out.

(Moore & Benbasat, 1991)

Internet banking is available to me

adequately for trial


My bank permits me to use internet

banking on a trial basis long enough to

see what I can do.


Image (IM)

Image was operationalized using the following items:


Image (IM)

If I were to adopt internet banking, It

would give me higher status amongst my

peers.

(Karahanna, 1999;

Moore & Benbasat,

1991)

People who use internet banking have

more prestige than those who do not.

(Moore & Benbasat,

1991)

Having internet banking is trendy amongst

my peers.

(Moore & Benbasat,

1991)

People who use internet banking have a

high profile.

(Moore & Benbasat,

1991)

Attitude (ATT)

Attitude was operationalized using the following items:


Attitude (ATT) In general, I have a positive opinion about

internet banking.

(Bhattacherjee, 2001)

I like the idea of using internet banking. (Tan & Teo, 2000)

In my opinion, it is desirable to use internet

banking.

(Tan & Teo, 2000)

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Behavioural Intention (BI)

Behavioural Intention was operationalized using the following items:


Behavioural

intention (BI)

If I have the facilities required for using

Internet Banking, I intend to use it.

(Wang et al., 2003;

Wu &Wang, 2005)

I plan to experiment with or use internet

banking regularly in the next six months.

(Venkatesh et al., 2003;

Webster & Wind, 1972)

In the future, I intend to continue using

internet banking.

(Karahanna et al., 1999;

Agarwal & Prasad, 1997)

Actual Usage (AU)

Actual Usage was operationalized using the following items:


Actual Usage

AU)

(self-reported)

How long have you been using the internet

banking facilities?

(Moon & Kim, 2001)

On a weekly basis, how many times do you

use internet banking?

(Moon & Kim, 2001)

4.10.2 Measurement scale

Measurement items that are a collection of items combined into a composite score and intended

to reveal levels of theoretical variables not readily observable by direct means are often referred

to as scales, (DeVellis, 2011). Table 4.6 gives a summary of the types of measurement scales and

the operations possible on using these scales.

Table 4.6: Types of Measurement scale (adapted from Dillon et al marketing research):

Scale Basic Empirical Operations Permissible Statistics

Nominal Determination of equality Number of cases, percentages, mode

Ordinal Determination of greater or less Mode, Median, Percentages

Interval Determination of equality of

intervals or differences

Arithmetic mean, standard deviation,

product-moment correlation

Ratio Determination of equality of

ratio

Coefficient of variation.

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Because of the inherent limitations of single-item measures, respondents usually are

administered two or more measures, referred to as a scale, they are alternative indicators of the

same underlying construct, (Churchill, 1979).

4.10.2.1 Format for measurement

Composite scale

In this research, the underlying latent constructs Perceived Usefulness, Perceived Ease of Use,

Trust, Perceived Risk, Internet Banking Self-Efficacy, Internet Usage Efficacy, Subjective

Norm, Government Support, Bank’s Initiative, Trialability, Image, Attitude, Behavioural

Intention are measured using a Likert scale. The Likert scale includes many scale items. Each

item in the scale measures some aspect of the underlying construct. The respondent is given the

operational definition of the construct and views on agreement or disagreement are sought using

a five point scale ranging from strongly disagree to strongly agree. The total score for a construct

was obtained by taking the mean of the numerical score assigned by an individual to each item.

Index

To classify the respondents based on their socio-economic status. In this research, the new socio-

economic classification method developed by the Market Research Society of India (MRSI) and

Media Research Users’ Council (MRUC) based on two variables: education of the chief-earner

and number of consumer durables and the land owned by the family, (see Appendix B). The new

socio-economic classification system has 12 grades A1, A2, A3, B1, B2, C1, C2, D1, D2, E1, E2

and E3.

4.10.3 Questionnaire design

Phase 1.

In this phase, documenting studies for determining the state of the internet was done. It was

found that government regulation had a significant role in monitoring, controlling and promoting

the internet. The data for this research was obtained from an online survey conducted by the

Internet Society to find the opinion of internet users on various issues. Questions’ pertaining to

the hypothesized constructs were identified and was used in this research.

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Phase 2.

In this phase, a questionnaire was not used because it involved capturing website features of all

banks.

Phase 3.

Semi-structured interviews consisting of predetermined questions relating to domains of interest,

administered to a representative sample of respondents to confirm the study domains and identify

factors, variables and items or attributes of variables for analysis or use in a survey, (Schensul et

al., 1999).

In this phase, a semi-structured interview was conducted. The guidelines of the interview were

prepared beforehand. The questions were meant for senior management, (see Appendix B). The

basic aim was to make the respondent talk freely without any hesitation. Some facts and figures

about internet banking users were also collected. This was done by sending an email

Phase 4.

Questions were framed and sent as an application using the format prescribed under the Right To

Information Act, 2005.

Phase 5.

The instrument was developed using a two-part questionnaire. The first part captured

respondent’s characteristics and some basic information such as age, gender, education level,

work experience, hierarchy in the bank, bank’s category and size. This part of the questionnaire

used a nominal scale. The second part of the questionnaire was developed by borrowing items

from a previous study conducted in Athens, the capital of Greece, (Lymperopoulos &

Chaniotakis, 2004). The second part had items related to the perception about internet banking

and its effect on issues such as cost reduction, bank’s image, queue minimization, increase in

sales, customer alienation, decrease in employees, price competition, service differentiation,

market transparency and risk. In the second part of the questionnaire a five point Likert Scale

was used ranging from 1 –“Strongly Disagree” to 5- “Strongly Agree”.

Phase 6.

In this phase, data about the financial performance of the banks was obtained from the Indian

Banks Association website, the web rank was obtained from Alexa.com, and a questionnaire was

not required.

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Phase 7.

The first part of the questionnaire collects demographic information and the second part used

questions pertaining to content, accuracy, and format, ease of use and timeliness of the website.

A google form and paper version of the questionnaire was created. A covering letter

accompanied the questionnaire.

Phase 8.

The first part of the questionnaire collects demographic information, and the variables were age,

gender, education level. As it is a known fact that respondents are reluctant to enter correct age.

Age of the respondents was captured using categorical ranges to increase the response although

this procedure is not statistically appropriate leading to lowered variability. The gender of the

respondent was captured using a nominal variable. The educational level of the respondent was

captured using a categorical variable. The socio-economic classification index was included in

this part. The second part of the questionnaire had questions relating to the constructs used in the

proposed model for user acceptance of internet banking.

4.10.4 Pre-Test and Pilot Test

Phase 5.

For the preliminary assessment of the questionnaire, to identify problem related ambiguity in

wording, understanding and lay out and to refine the questionnaire based on the feedback of the

respondents, a pre-test was carried out. In the second phase of the study, a pre-test was

undertaken on the employees of HDFC Bank Ltd. Some of the employees found the word

alienation difficult, they also had difficulty understanding the meaning of market differentiation.

Taking this into account simple meanings and explanations were added in brackets for these

questions.

Phase 7.

The instrument was administered to part time Master of Business Administration (MBA)

students of TIMSR, JBIMS, PTVA and some individuals, selected on a random basis to

understand whether the measuring instrument captures all the constructs that were found in

previous studies. A factor analysis and reliability test on the samples obtained after the pilot

study confirmed that the instrument was without any problems.

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Phase 8.

The pre-test of this instrument was carried out by administering the survey instrument face-to-

face to 20 respondents. Respondents were encouraged to point out any adoption of internet

banking related factors that they felt was not included in the questionnaire. Even though the

respondents did not explicitly mention any new factors, that influence adoption of internet

banking, one common observation among all respondents, was that the questionnaire was very

lengthy. The questionnaire was made to look shorter by rearranging the vertical orientation of the

scale anchors and by orienting it horizontally. These modifications made the questionnaire look

short.

A pilot survey is a small-scale survey carried out prior to the final survey to confirm that there

are no possible problems, (Browne, 2006). A pilot study was done on individuals who were

computer literate, had access to the internet and used internet banking. Such individuals were

identified from the faculty, Executive MBA and part time MBA course students of NMIMS

University, Thakur Institute of Management Studies and Research, Jamnalal Bajaj Institute of

Management Studies. A reliability test revealed that the construct of government support, did not

adhere to the standards. Some of the item wordings and ordering were changed.

4.10.5 Questionnaire Administration

Paper based and Electronic survey forms were designed. The advantage of electronic survey

administration is that it eliminates the need for manually typing the response data, and data errors

are minimized. Another advantage is that the questions can be made mandatory, and the

respondent will be prevented from submitting the form if the required (mandatory) questions

have not been answered, which in turn ensures that there is no missing data. The link to the

electronic forms can be sent to selected respondents and can be embedded on a web page.

Electronic administration also eliminates the cost of photocopying and other incidental expenses

towards administration of the survey instrument.

4.11 Data screening and preparation for analysis

Real world data may contain irregularities and anomalies, (Hernandez & Stolfo, 1998). Data

abnormalities are a cause of inaccurate analysis, therefore data needs to be screened, and

necessary correction and statistical techniques need to be applied before analysis. Data screening

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for out of range values, missing data, outliers, and checks for normality was done prior to

proceeding with statistical analysis. Data screening and preparation was used only in phase 7 and

phase 8 of the study, which involved multivariate analysis.

4.11.1 Missing Data

Missing values occur when to the respondent intentionally does not answer some questions either

because of complexity of the item or the length of the questionnaire or in some cases omission

error. Missing values may also occur due to data entry errors. These types of missing values are

random in nature. The other possibility is that the respondent did not want to respond to the

question and the questionnaire did not have appropriate answer choices. The reason for not

answering can also be due to social desirability issues, if the questions were sensitive. Such

missing values are non-random.

4.11.1.1 Randomness of missing data

Depending on the pattern of the missing data, the following categories have been identified,

(Rubin, 1976). Missing Completely at Random (MCAR), Missing at Random (MAR) and Not

Missing at Random (NMAR).

The Little’s MCAR test is one of the tests to detect if data is missing completely at random. If

Little’s MCAR test shows a significant Chi-Square it indicates that the missing values are not

completely at random.

(Little & Rubin, 1987), suggested that the nature of the missing data can be diagnosed by finding

the correlation of the items having missing data. The Pearson’s correlation between the variables

after replacing all the valid data by one and missing values by zero and if found to be statistically

significant at the 0.05 level for all variable pairs it indicates that the missing variables are

completely at random. However, if the correlation coefficients are significant only for some pairs

of variables it indicates that the data is missing at random.

4.11.1.2 Steps taken to deal with missing data

(Olinsky et al., 2003), suggested that if the percentage of missing data was less than 5% then any

simple mechanism for identification and correction was acceptable.

129

(Kline, 1998), recommends three ways to manage missing data deletion; imputation with

estimates or to model the missing data as a distribution and estimate them.

The regression imputation method was used in this study for missing data imputation, as (Byrne,

2001) pointed out. This means that the imputation is based on variance and covariance and may

lead to biased standard errors in SEM. This method predicts the value of the missing value on the

basis of variables, which are not missing.

4.11.2 Test for Normality

Most of the analysis requires data to follow a normal distribution. Normality violations affect the

estimation procedures in Structural Equation Modelling (SEM) employed in phase 7 and phase 8

of this study. If the data is not multivariate normal, it may falsely increase the Chi-Square

statistics and critical values for evaluating coefficient significance, (Hair et al., 1998; West et al.,

1995). Non-normal data leads to under estimated values of fit indices like Tucker Lewis Index

(TLI), and Comparative Fit Index (CFI). Non-normal data can lead to low standard errors thus

giving false significant parameter estimates, (West et al., 1995). Skewness effect test of mean

and kurtosis effects variance and covariance. Non-normality was checked by inspecting the

Skewness and Kurtosis of the univariate distribution and the Mardia’s multivariate Kurtosis

value. Skewness greater than three and kurtosis greater than ten are potential problems, (Kline,

2005; West et al., 1995).

4.11.3 Outliers

Outliers are cases in the data set that are distinctly different. The presence of outliers can have

adverse impacts on statistical analysis can give rise to negative error variances and can affect the

model fit and the estimated parameters. Outliers can be identified by using the Mahalanobis D2

distance; the higher this distance, the case may be an extreme value and therefore an outlier.

There are two types of outliers, univariate and multivariate. In this study, univariate outliers were

not identified, as the scale used was a five point Likert Scale with anchors from strongly disagree

to strongly agree. Literature reveals different views about outliers. One view is that outliers are

valid observations and should be considered in the analysis. Another view is that the presence of

outliers will have adverse impact on analysis.

130

4.11.4 Multicollinearity

Multicollinearity was checked by using bivariate correlation between the various items. It was

found that the correlation values between variables did not exceed 0.8 recommended by (Hair et

al, 2006). If Variance Inflation Factor (VIF) for all the variables is found to be less than 5 it

indicates that multicollinearity is absent.

4.12 Validity and Reliability

Validity

Validity ensures that the concept that is intended to be measured is indeed being measured.

Validity ensures that a certain set of questions, which were meant to measure a concept, is

correctly measuring it.

Content validity

Validity is the indicator as to whether the questions actually measure the construct as it is

required to be measured. Many of the constructs used in the research were borrowed from

previous research after conducting extensive literature review. Therefore, content validity is

assumed present. The reason behind using many constructs from previous research was to avoid

possible measurement errors as establishing validity was not the primary focus of the research.

Moreover, the data obtained in this study was also obtained by means of semi-structured

interviews with senior management officials of the bank and the survey of employees and

customers. A pre-test and pilot test was also performed which ensured validity of the constructs.

Face validity

All the questionnaires used in this study were given to a panel of experts and a few senior bank

managers to ascertain face validity, and comments received from them led to minor changes in

the questionnaire, which led to greater clarity of the questions. The list of items, which were self-

developed for the study, was submitted to a panel of judges for assessing content validity. Prof.

Deb Dulal Dutta Roy of the Indian Statistical Institute, Kolkata who reviewed the questionnaire

made suggestions to modify the conceptual definition of the construct self-efficacy as per the

definitions given by Bandura and Amartya Sen. He also suggested that some items be made

simple and comprehensible and some modifications in the questionnaire design. All these

recommended changes were made to the questionnaire.

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Construct Validity

Construct validity is concerned with determining whether the instrument measures what it is

actually intended to measure, (Churchill, 1995). The measures of validity refer to developing

correct and adequate operational measures for the concept being tested, (Malhotra, 1996).

(Campbell & Fiske, 1959) proposed convergent and discriminant validity. In this part of the

study construct validity was examined by finding convergent and discriminant validity.

Convergent validity was examined to find whether the items of the same construct are correlated

and discriminant validity was used for finding whether the items of a construct do not correlate

on other constructs. The convergent and discriminant validity was established by performing

factor analysis on the data. For adequate convergent validity, it is expected that items belonging

to a common construct should exhibit factor loading of 0.60 or higher on a single factor (called

same-factor loading), while for discriminant validity, these items should have factor loading of

0.30 or less on all other factors, (cross-factor loading) (Bhattacherjee, 2012).

In phase 7 and phase 8, which used structural equation modelling, convergent validity was

established by using three criteria:

1. Factor loading

2. Average Variance Extracted (AVE)

3. Construct Reliability / Composite Reliability

Standardised factor loading of all the items were greater than the recommended value of 0.5,

(Byrne, 2001)

The software IBM SPSS AMOS 21.0.0 (Build 1178) does not have provisions to calculate

Average Variance Extracted (AVE) and Construct Reliability. The formulae and calculations for

AVE and Construct Reliability are as:

AVE = Sum of the squared factor loading / number of items

Construct Reliability = (Sum of factor loading)2 / [(Sum of factor loading)

2 +(Sum of

standardized error variance)]

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AVE should be greater than 0.5 and Construct Reliability should be greater than 0.7, (Byrne,

2001; Fornell & Larcker, 1981). Construct reliability should be greater than 0.6 and AVE should

be equal to or greater than 0.5, (Bagozzi & Yi, 1988)

Discriminant validity is established if the AVE from the construct is greater than the variance

shared between the construct and other constructs in the model, (Chin, 1998). (Hair et al., 2006)

state that if the AVE is higher than the squared inter-scale correlation of the constructs, then the

discriminant validity is supported.

Reliability

Reliability of a measure indicates the extent to which it is without bias and hence ensures

consistent measurement across time and across various items in the instrument, (Sekaran, 2006).

To ensure reliability, several methods have been suggested; test-retest reliability to ensure

stability of the measure over time, parallel form reliability, and internal consistency of measures.

In this study, item reliability and scale reliability were examined.

Item reliability

The squared multiple correlations are an indicator of the communality of the item. Communality

being a measure of the percentage of the variance of the indicator explained by its latent factor is

a measure of the item reliability. The criteria suggested by (Bollen, 1989), was that during the

confirmatory factor analysis in the measurement model, each item should have a squared

multiple correlation greater than 0.5.

Inter-item consistency reliability

Reliability as a measure is established to ensure stability and consistency of the measure. One of

the most often used measures for scale reliability is Cronbach’s alpha. The values of Cronbach’s

alpha were proposed by (George & Mallery, 2003) and shown in Table 4.7 is a commonly

accepted rule of thumb measure for internal consistency.

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Table 4.7: Rule of thumb measures for Cronbach’s alpha

Cronbach’s alpha Internal consistency

Alpha ≥ 0.9 excellent reliability

0.8≤ Alpha<0.9 good reliability

0.7≤Alpha <0.8 acceptable reliability

0.6≤Alpha <0.7 questionable reliability

0.5≤Alpha <0.6 poor reliability

Alpha<0.5 unacceptable reliability

The nearer the Alpha is to one, the greater is the internal consistency. However, a high value of

Cronbach’s alpha of more than 0.9 may indicate redundancy amongst the items in the scale,

(Streiner, 2003). Literature demonstrates that there are varied views on the values of Cronbach’s

alpha. (Hair et al., 2006), suggest that Cronbach’s alpha greater than 0.6 is should be considered

acceptable in exploratory research.

4.13 Analysis plan

In this study, data was collected using the triangulation method. The analysis of this data would

involve univariate analysis, both descriptive and inferential, bivariate analysis method using

Pearson’s correlation and multivariate method, which includes exploratory factor analysis, linear

regression, confirmatory factor analysis and structural equation modelling. In the current study,

the devised plan was to perform:

1. Descriptive Analysis

2. Model Development

In phase 1 of the research, the objective was to identify the factors that act as drivers and

inhibitors of internet usage. The two major constructs affecting attitude and the use of the

internet was identified from literature. In this phase, a three-construct model was identified and

would be tested for model fit using SEM, a second-generation statistical method. In Phase 2 of

the research, the plan was to analyse the content and characteristics of the bank’s websites. In

Phase 3 of the research, it was planned to have descriptive statistics and the principal component

factor analysis, to determine the factors about the perception of bank employees towards internet

banking. In Phase 4 of the research, it was planned to use linear regression to determine the

relation between website traffic and the performance of the bank. In phase 5, an End User

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Computing Satisfaction model was proposed and that the model fit would be tested using SEM.

Phase 6 is concerned with the analysis of the proposed model using SEM. The plan was to make

competing models and make the proposed models more parsimonious.

Tools used for Data Analysis

In phase 3 and phase 4, data collected using the questionnaires were analyzed using IBM SPSS

Statistics version 21. In phase 5, measurement models and structural models were developed

using IBM SPSS AMOS version 21, James Gaskin’s Stats Tool package in Excel.

4.14 Research Boundaries

Literature review reveals that most of the technology acceptance studies are restricted to

developed countries, so there is a dearth of literature on technology acceptance for developing

countries. Taking cognizance of this, the current study is restricted to the acceptance behaviour

of internet banking in India, which is a developing country. The samples used in the current

study are users of internet banking, users from India and employees of banks currently working

in branch offices of Indian banks.

4.15 Research Ethics

Maintaining respondent trust is important in research. Survey questionnaire and semi-structured

interviews were used for data collection for this research. Utmost care with regard to the right to

be fully informed about the research goal, right to choose whether to participate in the research,

right to privacy and confidentiality, right not to be deceived due to participation as subjects in the

research, were taken as the research involved human subjects. In the current research, the

international code on market and social research has been adopted which defines market and

social research as the systematic gathering and interpretation of information about individuals or

organizations using the statistical and analytical methods and techniques of applied social

sciences to gain insight or support decision making. The covering letter distributed along with

the questionnaires assured respondents that their identity will not be revealed to the user of the

information without explicit consent and no sales approach will be made to them as a direct

result of their having provided information.

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Chapter Summary

The Table 4.8 below summarizes the research design used in the current study.

Table 4.8: The summary of the research design used in this study

Philosophical worldview Positivism

Research approach Deductive

Time Horizon Cross-sectional

Data Collection Document analysis, Survey questionnaire and

Semi-structured interview (Triangulation)

Data analysis

Structural Equation model, Regression,

correlation, Factor analysis, reliability testing

This chapter presented the research design and methodology selected for the study. This chapter

provides the reasons behind the choice of the research design. The steps involved in designing

the questionnaire and validating it are discussed in detail. The chapter also includes discussions

and justification for the selection of samples, sample size, instrumentation, data collection, tools

and analysis methods for each phase of the study. The next chapter will discuss in detail the

analysis and the interpretation of the data to answer the research questions.

chapter 4 research design and methodology -...

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