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CHAPTER 3

RESEARCH METHODOLOGY

3.1 INTRODUCTION

Chapter two focused on issues that cause variation in loyalty perception of a user for

different websites. The main conclusions derived from previous chapter are: (1) relative

importance of e-loyalty antecedents varies with varying website category, (2) less emphasis

has been given to determine the relative importance across different websites in existing

studies, (3) practical strategies used by the websites to ensure e-loyalty is absent in literature,

(4) website can be categorized on the basis of user’s primary need. The purpose of this

chapter is to discuss the research methodology adopted to conduct this study to address our

research issues.

Research methodology can be understood as the science and philosophy behind the

systematic solution to a research problem (Adams et al., 2007). Jonker and Pennink (2010)

considered methodology as ‘action repertoire’ which includes preparing a repertoire based on

theoretical and practical foundations, according to which the researcher structures the logic of

research to address the research problems. It includes the study of various steps that are

usually implemented in a research for studying the research problem along with the

supportive logic (Kothari, 2004). The following sections report the research

methods/techniques adopted in this study.

3.2 RESEARCH PHILOSOPHY

Research is not ‘neutral’ but incorporates focus, aims, ambitions, devotion, values,

observation, assumptions and abilities of the researcher. Hence, it is imperative to look upon

the philosophy used in this study. Research paradigm, described by (Blaikie, 2000;

Bhattacherjee, 2012) or research philosophy, explained by (Saunders, Lewis and Thornhill,

2009) relates to the development of knowledge and the nature of that knowledge. Easterby-

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Smith, Thorpe and Lowe (2008) identified three reasons to clarify the importance of research

philosophy:

1. Help researcher to determine research methodology and overall research strategy.

2. Avoid unnecessary work and inappropriate use of different methods and methodologies

by recognizing the limitation of particular approaches.

3. Assist researcher to be innovative in selection of methods that were previously outside

his/her experience.

Two major ways of thinking about research philosophy are ontology (what is the nature

of reality) and epistemology (what can be known) which encompasses various philosophies.

Ontology concerns with the nature of reality i.e. what constitutes reality and how can we

understand existence (Saunders, Lewis and Thornhill, 2009). The belief is that reality subsists

regardless of human observers. Epistemology concerns what comprises acceptable

knowledge in the field of study i.e. what we know and how we know it (Porta and Keating,

2008). The belief can be justified using logical reasoning and experimentation. Justification

and nature of facts (nature of data and method of acquisition) are central themes. According

to Reber (1995) epistemology and ontology are within the foundation realm of philosophy

and mutually support one another while Cohen, Marion and Morrison (2007) and Hitchcock

and Hughes (1995) stated that ontological assumptions provide base to epistemological

assumptions, which leads to methodological consideration and eventually steers to

instrumentation and data collection.

Three main types of philosophies discussed in the literature are positivism, realism and

interpretivism. A positivist philosophy assumes that reality is fixed, directly measurable, and

there exists one truth, one external reality. Positivism believes that there is an objective

reality independent of human behaviour that is not a creation of the human mind (Morgan

and Smircich, 1980). Positivism is typically associated with quantitative research methods

such as experiments and surveys and emphasizes measuring and counting (Remenyi et al.,

1998). Positivists start with a theory and generate hypotheses that are subjected to empirical

examination.

Most research in social science applied an objective approach (epistemology) and used

the standardized data collection tool like surveys (Bhattacherjee, 2012). They begin with

clear sets of positivist assumptions, including hypotheses, and then proceed to test the

hypotheses (Saunders, Lewis and Thornhill, 2009). This research is also not an exception and

is positive in approach. It aims to develop hypotheses and further tests it by data collection.

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3.3 RESEARCH APPROACH

An individual can proceed deductively or inductively to identify causal relationships

that account for a particular phenomenon. Deductive approach develops a theory and

hypothesis (or hypotheses), and design research strategy to test the hypothesis while in

inductive approach, data is collected and theory is developed as a result of data analysis

(Porta and Keating, 2008).Remenyi et al. (1998) recommended deductive approach in

research in which a theory or hypothesis is built and research strategy is developed to test the

hypothesis, mostly in disciplines where agreed facts and established theories are available.

The theories and facts can be derived by analyzing the existing literature on a particular

phenomenon or subject. The researcher first collects the available literature of interest and

then synthesizes it to formulate framework and theories. Sekaran (2003) described this

approach as a hypothetico-deductive method. It starts with the theoretical framework;

hypotheses are formulated and end with a logical deduction from the results.

Deductive and inductive approach is generally followed with a quantitative and

qualitative research (Newman, 2003). Thus, research can also be dichotomized as qualitative

and quantitative (Powell, 2004; Denscombe, 2007). The qualitative research emphasizes on

processes and meanings that are not measured in terms of quantity amount, intensity or

frequency, while quantitative research methods are used within natural science, the meanings

are often derived from numbers and the aim is usually explanatory, to permit generalizations

and to enable predictions (Saunders, Lewis and Thornhill, 2009: Hair et al., 2010). Deductive

research is associated with quantitative research while inductive research is related to

qualitative research (Bryman and Bell, 2005). “Positivist methods, such as laboratory

experiments and survey research, are aimed at theory (or hypotheses) testing … … employ a

deductive approach to research, starting with a theory and testing theoretical postulates using

empirical data” (Bhattacherjee, p. 35).

In the present study, a conceptual framework and associated hypotheses are developed.

Altogether 13 hypotheses were developed on the relationship between dependent and

independent variable. The variables are e-loyalty, e-satisfaction, e-service quality, e-

perceived value, e-trust, number of members and number of peers. The data on these

constructs was collected by participants through a survey. The hypotheses were tested against

the collected data. This study’s approach is deductive, and accumulated data is quantitative

hence study is quantitative rather than qualitative.

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3.4 RESEARCH PURPOSE

Exploratory, descriptive, and explanatory are the three classifications of research

available when dealing with a research problem (Yin, 1994). An exploratory study is a means

to look out what is happening, to search for new insight, to interrogate and to assess

phenomena in a new light (Robson, 2002). Descriptive research is limited to frequency

distributions and may be qualitative or quantitative. It is normally restricted to summary

statistics such as mean (Sue and Ritter, 2012). Explanatory research establishes causal

relationships between variables, emphasizes on studying a situation or a problem to explain

the associations between variables (Saunders, Lewis and Thornhill, 2009). Sue and Ritter

(2012, p. 2) explained, “explanatory studies are characterized by research hypotheses that

specify the nature and direction of the relationships between or among variables being

studied … the data are quantitative and almost always require the use of a statistical test to

establish the validity of the relationships”. Theories or at least hypotheses are responsible

forces that affect a certain phenomenon to occur in an explanatory research (Cooper and

Schindler, 2008). This study aims to test causal relationships between e-loyalty and its

antecedents and intends to collect data for hypothesis testing. It also aims to apply rigorous

statistical tests to ascertain the reliability and validity of relationships that underlie conceptual

framework. Thus, this study is explanatory in nature.

3.5 RESEARCH STRATEGY

Yin (1994) defined five primary research strategies: experiments, surveys, archival

analysis, histories, and case studies. No research strategy is inherently inferior or superior to

any other and choice of research strategy will be guided by research question(s) and

objectives, the extent of existing knowledge, the amount of time and other available

resources, as well as philosophical underpinnings (Saunders, Lewis and Thornhill, 2009).

Aaker, Kumar and Day (2004) described that adopting survey method for research depends

on number of factors – type of population, sampling, question content, question format and

costs. The purpose of current research is to gain a better understanding of what determines

loyalty for a website and how the same user perceives different antecedents of e-loyalty for

various websites. Survey is best option to answer ‘what’ and ‘how’ questions (Yin, 1994). It

gives the opportunity to collect quantitative data that can be analyzed using inferential

statistics. Also to arrive at valid results, collection of data from substantial number of

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participants is required. Survey through questionnaires is cost-effective in such cases. Thus,

data collection through survey seems the most suitable plan for this study. For

implementation, a self-administered survey is used. The advantages are cost effectiveness and

accuracy (Aaker, Kumar and Day, 2004), privacy (Burns and Bush, 1992) and easy to

understand for respondents (Grossnickle and Raskin, 2001).

Bryman and Bell (2005) defined two types of survey – questionnaire and structured

interview. Structured interview demands the presence of the interviewer while a respondent

can fill questionnaires on its own. The choice of a survey method is based on different factors

which include type of population, sampling, response rate, costs, question format, subject

content and duration of data collection (Aaker, Kumar and Day, 2004). The proposed

conceptual model contained a number of research hypotheses that required an empirical

examination to conclude from the study. It demanded quantitative data collection and the

validity of results depend heavily on the number of responses. Thus, it is essential to reach

sufficient number of respondents. The questionnaireprovides a quantitative method of data

collection - the responses, data or information needed in numerical terms. Therefore,

questionnaire was used as a survey item for data gathering.

3.6 INSTRUMENT DEVELOPMENT

Denoting the critical significance of instrument in generating accurate survey

assessments, Straub, Gefen and Boudreau (2005) emphasized the use of previously validated

available instruments. The researchers should implement already authenticated measurement

items rather than developing a new one for efficiency reasons. Thus, the instrument items in

this study were adopted from existing literature, but with some adaptations in the context of

the present study. Altogether tenconstructs were to be measured: convenience, contact

interactivity, customization, responsiveness, e-trust, e-perceived value, e-satisfaction, number

of members, number of peers and e-loyalty. Appendix A lists the original construct items

with their corresponding literature sources.Finally, 33 items was derived to measure these ten

constructs. The measurement items and their corresponding sources are listed in table 3.1.

The scale of convenience, contact interactivity and convenience was adopted from

Srinivasan, Anderson and Ponnavolu (2002). Responsiveness items were adopted from

Semeijn et al. (2005). E-perceived value was adopted from Luarn and Lin (2003) and e-trust

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from Cyr et al. (2007). E-satisfaction items were adopted from Sheng and Liu (2010).Number

of members and number of peers items were adopted from Lin and Lu (2011).

Table 3.1: Measurement items

Construct Measurement items Adopted from

Number of members 1. I think a good number of people use this website. 2. I think most people are using this website. 3. I think there will still be many people using this website.

Lin and Lu (2011)

Number of peers

1. I think many friends around me use this website. 2. I think most of my friends are using this website. 3. I anticipate many friends will use this website in the

future.

Lin and Lu (2011)

Convenience

1. A first-time user can locate the items on this website easily.

2. This website does not take much time to meet my demands.

3. This website is a user-friendly site. 4. This website is very convenient to use.

Srinivasan, Anderson and Ponnavolu (2002)

Contact interactivity

1. I feel this is a very engaging website. 2. This is a very dynamic website. 3. My interaction with this website is clear and

understandable.

Srinivasan, Anderson and Ponnavolu (2002)

Customization

1. This website makes recommendations that match my needs.

2. The advertisements and promotions that this website sends to meare tailored to my situation.

3. This website makes me feel that I am a unique customer. 4. I believe that this websiteis customized to my needs.

Srinivasan, Anderson and Ponnavolu (2002)

Responsiveness 1. It is easy to get in touch with the website. 2. Website is always interested in feedback. 3. Website quickly responds to user request.

Semeijn et al. (2005)

E-perceivedvalue

1. I get much more than the worth of my time, effort and money.

2. Based on simultaneous considerations of what I give and what I receive, I consider this website to be valuable.

3. The choices of products and/or services offered by the website are better than its competitor.

4. After every visit, it makes me feel, it is worth using this website.

Luarn and Lin (2003)

E-trust

1. I can trust this website. 2. I trust the information presented on this website. 3. I feel this website will keep my data secure and will not

share with anyone else.

Cyr et al. (2007)

E-satisfaction

1. I feel satisfied with all myexperiences on this site. 2. I feel wise to use this site. 3. Generally speaking, I think it is an accurate decision to go

on to this particular website for my needs and requirements.

Sheng and Liu (2010)

E-loyalty 1. I prefer this website. 2. I will use the same website again. 3. Iwill recommend this website to others.

Semeijn et al. (2005)

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All the scales that have been used to measure the research construct were adapted from

well-cited journal’s article and showed high consistency (Cronbach’s alpha) in respective

studies. 5-point Likert scale was used for measurement. The scale was given to indicate the

response where each scale item has five response categories, ranging from “strongly

disagree” to “strongly agree".

3.7 QUESTIONNAIRE DEVELOPMENT

Two questionnaireswere used to accomplish the objectives of this study. Questionnaire

(A) was used to test the research hypotheses, and questionnaire (B) purpose was to ensure

consistency and to validate the obtained the results. The difference between these two

questionnaires is that questionnaire (A) demanded responses about research constructs for

any preferred website in three categories of the website. The categories are service website,

product website and social networking website. However, the questionnaire (B) is targeted

for three specificwebsites in each category. The websites are Amazon.in (product website),

Google.com (service website) and Facebook.com (social networking website).

3.7.1 Questionnaire (A)

Questionnaire A was developed to measure online web user perceptions about the

research constructs – online users are asked to indicate responses for their preferred website

in each of the three categories of the website, i.e. product website, service website and social

networking website.The final version of the questionnaire is listed in Appendix B.

The questionnaire is divided into two parts – first part contains general demographic

information, which includes gender, education, age and occupation while the second part

contains questions about e-loyalty and its antecedents. Contact details are also requested

which helped to identify other probable participants. Second part of the questionnaire

contains measurement items about e-loyalty behaviour.

Same users with at least five visits per month for a service website and a social

networking website and at least two visits per month to a product website were considered in

this study. This is done in accordance with following observations

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1. According to a report by International Market Research Bureau (IMRB) and I-Cube

(2014, cited in Prabhudesai, 2014), 97% of the ‘active’ Internet users used the Internet at

least 2 times and almost 85% went online at least 4 times a month.

2. As per ComScore (2013, cited in Mitra, 2014), an internet analytics company, the major

drivers of web behaviour are social networking and services. Further, they share, India’s

Internet population spent 23% of time on online services, 25% on social networking while

retail accounted only 3% of the total time spent.

In line with these statistics, an attempt to cater all typeof active Internet users was done

and the frequency for service website and social website was kept at least five times a month,

for product website the frequency is low, at least two times per month.

3.7.2 Questionnaire (B)

Questionnaire (B) is a slight modification of questionnaire (A). Rather than the general

service website, product website and social networking website the questionnaire specifically

targets the loyalty perception for Google.com, Amazon.in and Facebook.com. The final

version of the questionnaire is listed in Appendix C.

Google.com (Google) is the world’s and India’s leading search engine and is providing

intangible service. Amazon.in (Amazon) is the one of the leading retail website in India. In

this study, Amazon is classified as product website that sells physical products. It also sells

intangible products (e.g. e-book) but excluding the few exceptions almost all items Amazon

sells are physical products. Facebook.com (Facebook) is the India’s number one social

networking website.

For validation purpose, these three successful websites were selected. The three

‘exemplars’ were chosen because:

1. The chances are more that any random Internet user is loyal to these websites as these

websites, as per Alexa (2014) – a web analytics company, are among the most visited

sites in their respective category.

2. In agreement with our proposed model, the practical strategies used by these websites are

discussed. The existing literature lacks such discussion.

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3. Explanation of strategies will serveas guideline to other upcoming websites. As

previously noted, more than 9 out of 10 business start-ups eventually shut down within

the first 120 days (Ducker, n.d.).

4. The discussion on strategies provides theoretical validation to the outcome of the

empirical analysis.

3.8 PRE-TEST AND PILOT-TEST

To validate the measurement instrument, a pre-test and a pilot-test were done. Pretest

group include 12 respondents who have at least three years of experiences and have a

preferable attitude toward a particular website in all three categories. They are asked to make

a judgment whether the constructs and measures are appropriate and in line with the purpose

of study. A detailed discussion was done about structure, wordings, length and format of the

instrument; several items were modified to reflect the questionnaire’s purpose more clearly.

The measurement items have been amended and rephrased without changing the intent of

items. Few were removed (either they were very specific to a particular website category or

were not appropriate for all categories) keeping in mind that same individual had to fill the

questionnaire items simultaneously for three websites. Thus item needs to be framed

accordingly and with clear understandability. For example, Item- ‘this website does not have

a tool that makes product comparisons easy’ is a very specific item related to product

website, thus removed.

The size of the pilot group may lie between from 25 to 100 (Cooper and Schindler,

2008). The pilot test involved 50 students, who prefer a particular product website, service

website and social networking website having online experience of more than a year. The

pilot study was conductedat the Jaypee University of Engineering and Technology, India.

Statistical Package for Social Sciences (SPSS) was used to measure the validity and

reliability. Cronbach’s alpha for all the items were greater than 0.70 as suggested by Nunally

(1978). The items loaded on the appropriatefactors for confirmatory factor analysis with

loadings greater than 0.50 as recommended by Hair et al. (2010). Thus, instrument confirmed

content validity and reliability. The result of thepilot test is listed in Appendix D.

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3.9 SAMPLING

This section explains the sampling size, sampling frame and sampling technique

adopted in this study. Saunders, Lewis and Thornhill (2009) defined four steps in sampling

process:

1. Ascertain a suitable sampling frame

2. Decide on a suitable sample size

3. Select the most appropriate sampling technique

4. Check the sample is representative of population

3.9.1 Sampling frame

A sampling frame is a comprehensive list of all the cases in the population (Cooper and

Schindler, 2008). The main purpose of this study is to analyze the same user loyalty

behaviour for three different kinds of websites - product website, service website and social

networking website. To achieve the objective, questionnaire orients to those online users who

have online experience and have a favourable attitude to at least one website in each

category. Approximately there are 290 million Internet users in India (Internet Live Stats,

2014). In the ideal case, the population consists of all these users; however, it is not feasible

and too expensive to gather a complete list of all Internet users in India. Moreover, it would

be impractical to identify our potential respondents out of the total number of Internet users.

It is not necessary that all users prefer at least one website in each category. The population to

be able to relate to this study, the participants included were the Internet users who visit their

preferred service website and social networking website at least five times a month and their

preferred product website at least twice a month.

3.9.2 Sample size

Many views exist regarding the sample size for research. Tabachnick and Fidell (2007)

suggests minimum five cases per item, Habing (2003) recommends at least 50 and five times

of variables and according to Field (2000) at least 10-15 subjects per variable. Hair et al.

(2010) suggested that sample size larger than 100 is adequate for factor analysis. Structural

equation modelling necessitates a large sample size because the model fit assumptions are

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based on a large sample size and according to Kelloway (1998) structural equation modelling

is appropriate with a minimum of 200 observations.

Following the above recommendations we aimed to achieve a sample size of 10-15

times per variable. In the study, there were 33 variables; a sample size in the range of 330-

495 would suffice. As such, we continued to invite respondents to take part in the research

until our sample size was reached.

3.9.3 Sampling technique

As discussed previously, the objective of this study is to analyze the same user

behaviour loyalty behaviour for different category of website and to determine the relative

importance of e-loyalty antecedents. Thus, our questionnaire orients to those online users

who have online experience of three classes of websites and necessarily the respondent

should prefer at least one website in the three categories. It is almost impractical to identify

our potential respondents out of the total number of Internet users.

In such cases, the target population is elusive, and other sampling methods (non-

probability sampling methods) must be employed (Lesley 2012). Saunders, Lewis and

Thornhill (2009) identified that in business research, the case may be that study does not have

appropriate sample frame to answer research question or do not have a sample frame at all,

alternatively, limited resources or the inability to specify a sampling frame may dictate the

use of one or a number of non-probability sampling techniques. Bhattacharjee (2012) also

explained that sometimes non-probability sampling is the only way to reach hard-to-reach

populations or when no sampling frame is available. It is reasonable to use non-probability

sampling technique for our study.

According to Statista (2013), a statistics portal contains statistics from more than

18,000 sources, distribution of Internet users in India by age group are: 15-24 (34%), 25-34

(38%), 35-44 (16%), 45-54 (6%) and 55+ (3%). Thus to identify our potential respondents

and to cater these different age groups; snowball sampling method was used in this study.

Although the generalizability is limited in non-probability sampling methods, but an

attempt has been made to ensure consistency and to improve the validity of results by

examining the proposed model in two different scenarios with the help of two questionnaires.

For validation purpose, the different segment was targeted, and convenience sampling

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method was used since the purpose is to cross-examine the results and to ensure

dependability in results. All the responses were obtained from students. Students were

selectedbecause:

1. India’s young and urbanizing consumer base offer growth potential for Internet usage and

will represent the future Internet usage patterns in the population at large (McKinsey and

Company, 2011).

2. Students can represent the online consumer population. Adopting students as a survey

sample is typically considered applicable to online consumers (Chang and Chen 2008,

Njite and Parsa 2005) since online consumers are generally younger and more highly

educated than conventional users, making student samples closer to the typical online

consumer population (McKnight and Chervany, 2002).

3.10 NON-RESPONSE BIAS

Non-response is the failure to collect information from sampled respondents (Leeuw,

Hox and Dillman, 2008). In case, a majority of respondents fails to respond or not interested

in survey lead to non-response bias if there are a legitimate concern and non-response due to

a systematic reason (Bhattacherjee, 2012). Fowler (2002) outlined three important

recommendations to reduce to non-response bias: (1) organization of the questionnaire should

be clear, (2) questions should be easy to read and nicely spaced, (3) questionnaire should be

respondent-friendly.

The three measures were followed during instrument development and validation.

Measurement items were adapted from existing studies that have already validated question

items. Also, pre-test and pilot-test was performed to make the questionnaire items user-

friendly and to ensure validity.

3.11 DATA COLLECTION

The data was collected separately two times. Data was obtainedfrom the user in each

categoryfor his/her preferred website through questionnaire (A). Questionnaire (B) was used

for data collection from a user for a particularwebsite (Google, Amazon and Facebook) in

each category.

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3.11.1 Questionnaire (A)

Data collection was mainly carried out by sending emails, direct communication with

some students, and instant communication with peers, friends and relatives who further

delivered the questionnaire to their peers and friends. Although snowball sampling does not

lead to representativeness but at times it is the best method available (Hsu et al. 2012) and

studies have applied this method in their research (e.g. Tong 2009; Lin and Sun 2009; Hsu et

al. 2012).

A total of 506 responses lied in our inclusion criteria out of 518, i.e. at least five visits

per month for service website and social networking website and two visits per month to the

product website. The responses of 13 respondents were eliminated as eight of them were

partially filled and five of them have given the same rating for all the items. Finally, 493

valid questionnaires were retained for analysis. All the respondents are from India. Sample

demography is provided in table3.2.

Table 3.2: Sample demography for questionnaire (A)

Measure Item Frequency %

Gender Male 386 78.30

Female 107 21.70

Age Under 18 15 3.04

18 to 30 305 61.86

30 to 40 166 33.67

40 to 50 5 1.01

> 50 2 0.40

Education Undergraduate 142 28.80

Graduate 159 32.25

Postgraduate 192 38.94

Occupation Student 121 24.54

Office Worker 337 68.35

Self Employed 27 5.47

Home Makers 08 1.62

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Respondents were from all age groups, but majority lie in 18-30 age-group. Males

comprised 78%, and female were 22% in which 68% were office workers. 62% of the

respondents fall in the age group 18 to 30.

Juxt (2011), a market research company, conducted a survey among 2,01,839 Indian

Internet users and stated that almost 2/3rd(66.6%) users were employed. The 25-35 years

segment is the largest online age group. Male users represented 73% while female user

consisted 27% of the Internet-using population. ComScore (2014) – a web navigation

analytics company, also confirmed the demography presented by Statista in 2013. The

Internet using Indian population in different age-groups, percentage wise are: 15-24 (36%),

25-34 (39%), 35-44 (16%), 45-54 (6%) and 55+ (3%). The demography of respondents in

this study is in line with the demography presented by Juxt (2011), Statista (2013) and

ComScore (2014). Figure 3.1, figure 3.2, figure 3.3 and figure 3.4 depict the gender-wise,

education-wise, age-group and occupation-wise information of the respondents, respectively.

Figure 3.1: Respondents gender

Figure 3.3: Respondents age-group

Figure 3.2: Respondents Education

Figure 3.4: Respondents occupation

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3.11.2 Questionnaire (B)

Two universities were approached, and students were asked to participate in the study.

Thus, all respondents are students from two different universities (ITM University and Jiwaji

University) of India. The questionnaire was delivered to them in person and through e-mail.

Total 375 responses has been received which lied in our inclusion criteria out of 385. Out of

375 questionnaires, 23 were invalid. Thus, 352 usable responses were obtained. Sample

demography is provided in table 3.5. The students less than 18 years of age were 23 % while

above 18 years consisted 77% of the total respondents. Figure 3.5 and figure 3.6 depict the

age-wise and gender-wise information graphically.

75% of the India’s online populations are under the age of 35 and individuals less than

15 years of age insignificantly represent India’s online population (Statista, 2013; ComScore,

2014). The students in our sample were in the range of 16-25 age-group. Table 3.3 provides

the sample demography for questionnaire (B).

Table 3.3: Sample demography for questionnaire (B)

Measure Item Frequency %

Gender Male 237 67.33

Female 115 32.67

Age Between 16 to 25

Occupation Student 352 100

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3.12 RESEARCH ETHICS

Widely accepted tenets of ethical behaviour in research are voluntary participation,

harmlessness, anonymity and confidentially, relevance of content, avoiding over-intrusive

question, fair analysis and reporting (Cohen, Marion and Morrison 2007; Bhattacherjee,

2012).

All responses obtained were well consented as the participation in the survey was

voluntary. However, no monetary and non-monetary benefits are provided to the participants.

The survey was self-administeredhence the participants are free to withdraw their

participation at any time. Respondents were communicated regarding safety and anonymity

and their personal information. Internet users were asked about their online loyalty perception

for their preferred online product/service provider thus the questions were relevant to them.

The questions were adapted from existing literature thus there is no threat or sensitivity issue

that lead respondents to over-reporting or under-reporting. It also ensured the content validity

of questionnaire. To measure reliability and validity pre-test and pilot-test were performed.

The issue of methodological rigor is an ethical issue, and respondents have a right to

expect validity and reliability (Cohen, Marion and Morrison 2007). Thus, factor analysis and

more rigorous statistical tests were applied to obtained data to ensure validity, reliability and

authenticity. All the findings and analysis are reported fairly.

3.13 DATA ANALYSIS METHOD

Data analysis in this study followed two-step approach suggested by Anderson and

Gerbing (1998). The first step conducted the factor analysis, reliability analysis and examined

convergent and discriminant validity of the measurement model. The second step assessed

the path significances of the research hypotheses, model-fit and variance explained by the

structural model.

3.13.1 Factor analysis

Kaiser-Meyer-Olkin (KMO) statistics and Bartlett’s test of sphericity was applied to

data prior to confirmatory factor analysis, to assess whether the data fits well with factor

analysis. KMO measure of sampling adequacy varies from 0 to 1.0, and KMO should attain

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a value 0.70 or higher to proceed with factor analysis (Dziuban and Shirkey, 1974). The

Bartlett’s test compares the observed correlation matrix to the identity matrix (Field, 2000).A

significant result (Significance level < 0.05) indicates matrix is not an identity matrix, i.e. the

constructs do relate to one another enough to run a meaningful factor analysis.

Factor analysis can be either confirmatory or exploratory. Based on the objective of

data analysis, each of these approaches can be implemented. Fabrigar et al. (1999) defined, in

situations where a researcher has relatively little theoretical or empirical basis to make strong

assumptions about existing common factors or how many distinct measured variables these

common factors are likely to influence, exploratory factor analysis (EFA) is probably a more

sensible approach than confirmatory factor analysis (CFA).But, when there is sufficient

theoretical and experiential basis to specify the model or small subset of models, CFA is

likely to be a better approach.CFA is often used in dataanalysis to investigate the expected

causal relationships between the variables and used when strong theory underlies

measurement model before investigation of data (Williams, 1995). The proposed structural

model of e-loyalty in this study was developed based on strong literature support, and there

exists a sufficient base for specified model.

Lu, Chang and Yu (2012) examined the online shoppers’ perceptions of e-retailers’

ethics, cultural orientation, and loyalty and used convenience sampling; applied regression

analysis to explore the relationships between variables. However, they indicated the use of

regression analysis as a limitation, and in fact they recommended confirmatory factor

analysis to establish unidimensionality for each factor and then structural equation modelling

for path analysis for future studies.Valvi and Fragkos (2012, p. 366) done a critical analysis

of e-loyalty studies and observed that “a final methodological limitation concerns the lack of

reporting or performing confirmatory factor analysis in certain studies”, thus not assessing

the models measurement fit”.Mouakket and Al-hawari (2012) adopted convenience sampling

and used AMOS to test the model fitness by performing CFA using SEM. The present study

also implemented CFA approach which is a special case of structural equation modelling

analysis with maximum likelihood estimation. AMOS 20.0 and SPSS 20.0 software packages

were used for the assessment of the measurement model and structural model.

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3.13.2 Reliability

Reliability is the extent to which the measure of a construct is consistent or dependable

(Bhattacherjee, 2012). It predicts the internal consistency of a model. The internal

consistency of the proposed model was determined by measuring Cronbach’s alpha and

Composite Reliability (CR). Nunally (1978) recommended, the model to be internally

consistent Cronbach’s alpha should be greater than 0.7 and every construct’s composite score

should be above 0.7 (Fornell and Larcker, 1981). If all the values are in the recommended

range than measurement items for each construct are reliable and stable, ensures data internal

consistency.

3.13.3 Validity

Validity specifies the degree to which an instrument measures what it is supposed to

measure (Kothari, 2008). Face validity or content validity is the first step in ensuring that

measurement items and questions are suitable to measure the constructs that are aimed to

measure (Bryman and Bell, 2007). Cronbach (1971) explained, content validity ascertains

that construct items are representative and drawn from auniversal pool.

Construct validity comprises of convergent validity and discriminant validity. Straub

(1989) defined, convergent validity ensures that there are relatively high correlations between

the measures of the same construct while discriminant validity confirms the low correlations

between the measures of different constructs that are expected to differ.Measurement of

convergent validity used three criteria suggested by Bagozzi and Yi (1988):

1. Factor loadings of all items should exceed 0.50 (Hair et al., 2010).

2. Composite reliability should be above 0.70.

3. Average Variance Extracted (AVE) of every construct should exceed 0.5 (Fornell and

Larcker, 1981).

AVE is the average amount of variance in observed variables that a latent construct can

explain (Farrell, 2009). To achieve discriminant validity, the square root of AVE should

exceed the inter-construct correlations below and across them (Fornell and Larcker, 1981).It

should be greater than maximum shared variance (MSV) and average shared variance (ASV)

(Hair et al., 2010).

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The measurement items used in this study has strong face validity as all the construct

items have been taken (with some adaptations to present study) from previous studies.

Definitions of e-service quality, trust, perceived value, number of members, number of peers,

satisfaction exhibits strong content validity in the existing literature, thus ensures content

validity of the construct items for this study. Pre-test and pilot-test were also performed to

ensure validity of the instrument.

3.13.4 Model fit

To evaluate the model fits, chi-square with degree of freedom (CMIN/df), the goodness

of fit index (GFI), the adjusted goodness of fit index (AGFI), normal fit index (NFI),

comparative fit index (CFI) and root mean square error of approximation (RMSEA) were

calculated. CMIN gives the minimum value of discrepancy between the data and the model.

CMIN/df is Chi-square divided by degrees of freedom. The goodness of fit index (GFI)

attains a statistical value between zero and one, indicates how well the model fits the data

where one indicates perfect fit (Joreskog and Sorbom, 1989). The Adjusted Goodness of Fit

Index (AGFI) adjusts the bias occurring from model complexity (Schermelleh-Engel and

Moosbrugger, 2003). It adjusts the degrees of freedom in relation to the number of observed

variables. The Degrees of freedom is the amount by which the number of sample moments

exceeds the number of parameters to be estimated. The Number of distinct sample moments

referred to are variance and covariance and the sample moments are the sample variances.

Normal fit index (NFI) indicates where the default model lies between saturated model and

independence model. Comparative fit index (CFI) compares the performance of the model

with baseline model. Baseline model assumes zero correlations between all observed

variables. Root mean square error of approximation (RMSEA) shows a lack of fit of the

model to population data.

The acceptable value of CMIN/df is less than three as suggested by Hayduck (1987).

Scott (1991) recommended GFI value to be greater than 0.90 and AGFI should be greater

than 0.80. NFI is in the acceptable range if its value exceeds 0.90 (Bentler and Bonnet, 1980).

Bagozzi and Yi (1988) recommended CFI to be acceptable if greater than 0.90 and also

suggested the RMSEA value should be less than 0.08.

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3.13.5 Choice of statistical analysis for path models

Generally, three approaches are used for testing the structural equation models or path

models: (1) PLS-PA, (2) System of regression equations, (3) AMOS-LISREL type search

algorithms. According to Westland (2014), PLS-PA are primarily exploratory analysis tools

and not suitable for hypothesis testing. Further the author suggested that PLS path estimates

are biased and highly dispersed when computed from small samples and is a ‘limited

information approach’ in a sense that path analysis implies that each of the Ordinary Least

Square(OLS) estimators on individual pairwise paths will, in most practical circumstances,

replicate the results of PLS path analysis software.

First generation models such as regression, Logistic Regression (LOGIT), Analysis of

Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) can analyze only

one level of linkage between independent and dependent variables at a time (Gefen, Straub

and Boudreau. 2000). However, regression estimators are scaled, as recommended by Tukey

(1954) versus the un-scaled path coefficients of PLS-PA and AMOS-LISREL approaches.

The methods like multiple regressions were suitable for assessing constructs and relations

between constructs. The first purpose of regression analysis is prediction while the intent of a

correlation is to evaluate the relationship between the dependent and independent variables

(Tabachnick and Fidell, 2007). Contrary to first generation tools like regression, SEM

enables researchers to answer a set of interrelated research questions in a single, systematic

and comprehensive analysis by modelling the relationships among multiple independent and

dependent construct simultaneously (Gerbing and Anderson, 1988; Gefen, Straub and

Boudreau, 2000).

SEM using AMOS was chosen over PLS-PA and regression equations due to the

complex relationship between dependent, independent and mediating variables in proposed

model of present research. SEM permits complicated variable relationships to express

through hierarchical or non-hierarchical, recursive or non-recursive structural equations and

presents a complete picture of the entire model (Hanushek and Jackson 1977; Jan Recker,

2013). Westland (2012) suggested research is better served by a ‘full information method’

such as covariance approaches (e.g., LISREL, AMOS) or a system of equations approach.

Depending on the nature of the complex relationship between dependent, independent and

mediating variables, structural equation modelling appeared to be the most apposite methods

for addressing our research problems (Zweig and Webster, 2003).

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3.14 CONCLUSIONS

This chapter developed the conceptual framework and research methodology. Research

methodology described the stages in the research process. Most of the studies in social

sciences derived their foundation from positivist’s philosophy followed with a deductive

approach. Therefore, the current research fits well with positive philosophy with deductive

approach. For validation of conceptual framework, it is established that a quantitative

research approach would be more suitable than a qualitative one. Following this, the study

identified itself with explanatory studies; there exists a clearly organized problem

thatrequired data collection, prior to the collection of the data. Thus, for each research

construct measurement scales have been selected based on previously tested scalesthat

exhibited high consistency and validity. To measure the constructs and to address the

research issues, there is a need to identify a proper strategy for data collection. Survey

seemed the most appropriate choice. The study requires large number of responses to

generate the valid results. Thus, self-administered questionnaire appeared suitable method so

that the participant can fill the questionnaire at their convenience. A pilot study was

conducted to assess the reliability and validity of the questionnaire. After that, practical

concerns like sampling strategy and sample size were discussed. Justification for use of non-

probability sampling techniques and sample size was provided. This chapter also dealt with

non-response bias and research ethics.

An explanation of theappropriateness of factor analysis in this study and various

methods for assessment of modelfit were given. Justification for choosing CFA – a structural

equation modelling technique over other methods was provided. Criteria to assess the validity

and reliability of the obtained data were discussed. Reasons for suitability of SEM using

AMOS to test structural model and path significanceswere cited.

Chapter five presents and discusses the outcomes of hypothesis testing and determines

the relationships between dependent and independent variables.