ERASMUS SCHOOL OF ECONOMICS

MSc Economics & Business

Master Specialization Marketing

FACTORS INFLUENCING CUSTOMER LOYALTY IN

AN ONLINE ENVIRONMENT

Student: Mariana BalabanovaStudent Number: 323962

Supervisor: Dr. A.C.D. Donkers

Acknowledgments

I would like to thank the people who helped me during the writing of my Master Thesis,

without whom I might not have been able to complete it. First of all, I would like to

express my sincere gratitude and appreciation to my supervisor Dr. A.C.D. Donkers, for

his guidance, patience and constructive suggestions throughout the writing process. Then,

I would especially like to thank my partner for supporting me; being always there for me

and making me smile even in the hard moments. Finally, I would like to express my

gratefulness to my family for their unconditional love, support and confidence in me.

Abstract

Due to the great number of online stores and the minimal switching costs for consumers,

the competition between online companies has become fierce. Hence, in order online

stores to survive in the market and to gain success, they need to have loyal customers, as

this is a major determinant for long-term financial performance of firms. In particular, the

onetime visitor must be transformed into a loyal customer, who will return to the e-store,

purchase repeatedly from the product or service offerings, and most likely increase the

customer base by becoming a referral of the company. However, when consumers buy a

product or service in an online store, they are affected by different aspects which

influence their willingness to come back and re-purchase. Therefore, finding how to

retain existing customers is important for e-tailers and they can achieve this by

identifying the critical factors that determine loyalty. The purpose of this thesis is to gain

better understanding of the antecedents affecting customer loyalty. Hence, customer

satisfaction, service quality, perceived value and trust are examined in an online

business-to-consumer context, in order to investigate their relation among each other and

their influence on e-loyalty. The results revealed that e-satisfaction is the main predictor

of online consumer loyalty. What is more, e-trust, e-perceived value and e-service quality

have been also identified as antecedents of loyalty, influencing it directly and as well

indirectly, through e-satisfaction. The results pose important implications for e-managers.

Keywords: e-service quality; e-trust; e-perceived value; e-satisfaction; e-loyalty, e-consumer

TABLE OF CONTENT

LIST OF TABLES vi

LIST OF FIGURES vii

1. INTRODUCTION 1

1.1. Introduction 1

1.2. Research Problem and Research Objectives 3

1.3. Thesis Contribution 5

2. THEORETICAL BACKGROUND 6

2.1. Literature Review 6

2.2. Online Customer Loyalty 11

2.2.1. Definition of Online Loyalty 11

2.2.2. Dimensions of Online Loyalty 13

2.2.3. Benefits of Customer loyalty 17

2.3. Factors influencing Online Loyalty 19

2.3.1. Online Service Quality 19

2.3.2. Online Trust 24

2.3.3. Online Perceived Value 31

2.3.4. Online Satisfaction 38

3. HYPOTHESES 43

3.1. Online Service Quality 43

3.2. Online Trust 48

3.3. Online Perceived Value 52

3.4. Online Satisfaction 54

3.4.1. Mediation effect of Online Satisfaction 56

3.5. Theoretical Framework & Summary of Hypotheses59

4. METHODOLOGY61

4.1. Survey Design 61

4.2. Questionnaire Design 62

5. ANALYSES & RESULTS 64

5.1. Descriptive Statistics... 64

5.2. Scales and Reliability... 64

5.2.1. Factor Analysis 65

5.3. Regression Analysis... 70

5.3.1. Regression Analysis for e-Loyalty 70

5.3.2. Regression Analysis for e-Satisfaction 71

5.3.2. Regression Analysis for e-Loyalty including a Mediator 72

5.3.4. Regression Analysis for e-Trust 74

5.3.5. Regression Analysis for e-Perceived Value 75

5.4. Further Analyses... 76

5.4.1. Regression Analysis for the effect of e-Service quality on

e-Satisfaction 76

5.5. Summary of Hypotheses & Results... 77

6. CONCLUSION 79

6.1. Conclusion 79

6.2. Managerial Implications 84

6.3. Limitations & Suggestions for Future Research 88

7. REFERENCES 90

8. APENDIX I 104

9. APENDIX II 110

LIST OF TABLES

Table 1: Definitions of Online Loyalty 12

Table 2: Relationship between behavioural and attitudinal loyalty 16

Table 3: Definitions of Online Service Quality 21

Table 4: Summary of main measurement scales for online service quality 23

Table 5: Definitions of Online Trust 27

Table 6: Definitions of Online Perceived value 33

Table 7: Components of the cost-benefit conceptualisation of perceived value 35

Table 8: Definitions of Online Satisfaction 41

Table 9: Summary of proposed Hypotheses 60

Table 10: Details of the scales used in the model 65

Table 11: KMO and Bartlett's Test 66

Table 12: Total Variance Explained 68

Table 13: Pattern Matrix (a) 69

Table 14: Component Correlation Matrix 69

Table 15: Coefficients (a) Independent variables vs e-Loyalty 71

Table 16: Coefficients (a) mediator (e-satisfaction) as dependent variable 72

Table 17: Model Summary(c) all predictors including mediator inserted 73

Table 18: Coefficients (a) all predictors including mediator inserted 74

Table 19: Coefficients e-Service Quality vs. e-Trust 75

Table 20: Coefficients (a) e-Service Quality vs. e-Perceived value 75

Table 21: Coefficients (a) e-Service quality vs. e-Satisfaction 76

Table 22: Summary of Hypotheses & Results 77

LIST OF FIGURES

Figure 1: Theoretical Framework 59

Figure 2: Scree Plot 67

1. INTRODUCTION

1.1. Introduction

Since the advent of Internet the number of online users across the world grows

exponentially. Global Internet access exceeded 2 billion people in 2011 - an increase of

480% since year 2000 - which constitutes more than 30% of the 6 billion world

population (Internet World Stats, 2011). Joines et al. (2003) stated that using the Web for

making purchases is increasingly becoming one of the primary reasons people are using

the Internet. Moreover, results from a Global research pointed out that more than 85

percent of the world’s online users have used the Internet to make purchases (Nielsen

Research, 2008). For today’s online shoppers, there are no more restrictions in terms of

store location or opening hours; they can shop products or services conveniently at any

time, in any online store without even leaving their homes.

As a consequence, the growth in Internet usage has changed the way business is done by

bringing a new era of electronic retailing (e-tailing) (Ponirin et al., 2009). Access to wide

number of potential customers, increasing profitability, gaining market share and

delivering high e-service quality that attract, satisfy and retain customers, are some of the

many performance gains for an e-company possible with an e-commerce (Watson et al.,

2000; Carlson & O'Cass, 2010). What is more, unlike in brick-and-mortar business,

settling an Internet company requires very low entry costs and a cheap maintenance.

Therefore, being attracted by the tremendous potential afforded by an online presence, an

increasing number of retailers have built websites, as a channel for profit, processing

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business transactions, communication and dissemination (Chen et al., 2008). As a result,

the e-shoppers have the ability to compare prices and attributes of competing products or

services among abundant number of e-stores and eventually choosing to purchase, or re-

purchase, from the e-tailer who can best satisfy their needs, wants and expectations (Li &

Zhang, 2002).

Consequently, due to the great number of e-stores and the minimal switching costs for

consumers, the competition between online companies has become fiercer and fiercer

(Yang et al., 2003). Therefore, in order to sustain their success, e-vendors have to

continually develop and keep loyal customer base, because, as Reichheld and Schefter

(2000) stated, e-tailers can build a very good online store but if the consumers visit only

once, this will lead to a loss.

According to Rigby (2000) loyal online customers are more profitable than onetime

shoppers, because they spend more, refer to more people and are more willing to expand

their purchasing into new categories. The authors Anderson and Srinivasan (2003)

further stated that a customer who is loyal to a company may be worth up to 10 times as

much as its average customer. In particular, the consequences of enhanced customer e-

loyalty in online firms are increased revenue, reduced customer acquisition costs and

lower costs of serving repeat purchasers, leading to long-term profitability (Yang &

Peterson, 2004). In addition, Reichheld and Sasser (1990) have found in their study that

if firms retain just 5 percent more of the customers, their profits increase by 25 percent to

125 percent. This statement is supported and by Lovelock and Wright (1999) who

proposed that on average it costs a company five to six times as much to attract a new

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customer, as it does to implement retention strategies to hold an existing one. Hence,

facing the competition, most business players shift their strategy towards retaining

existing customers and turning them into loyal ones, as this is a key factor contributing to

company’s success.

Therefore, considering that competing businesses are only a mouse click away in e-

commerce settings, it is critical that companies understand how to bring the customers

back to their websites (Anderson & Srinivasan, 2003). In order an online store to survive

in the market, the onetime visitor must be transformed into a loyal customer who will

return and purchase repeatedly from the e-vendor’s offerings. However, in order to build

an online loyal customer base, there is a prerequisite which the e-tailers have to fulfil.

Specifically, they have to learn their customer’s needs and expectations, which are

constantly changing and rather unpredictable. In order to achieve this and to implement

better customer retention strategies, online marketers and e-tailers must conduct ongoing

research and find the factors that influence customer loyalty.

1.2. Research Problem and Research Objectives

As already stated, in order to make customers loyal to a specific online store, marketers

need to deepen their understanding of the antecedents of e-loyalty. Such an

understanding can help e-retailers gain a competitive advantage by devising strategies to

increase online loyalty (Srinivasan et al., 2002). Extensive review of the marketing

literature shows that e-service quality, e-perceived value, e-trust and e-satisfaction have

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been each researched as separate a antecedent of e-loyalty, but their simultaneous impact

on it and their relationship has been rarely researched in one study.

Therefore, the main research problem of this thesis is to integrate satisfaction, trust,

perceived value and service quality together in the context of business-to-consumer

online environment, in order to investigate the interrelation among each other and their

influence on online customer loyalty.

This thesis will try to solve the main research problem by reaching the following

objectives:

To gain better understanding of the definitions, measures and dimensions of e-

satisfaction, e-service quality, e-trust, e-perceived value and e-loyalty;

To develop a theoretical framework which demonstrates that e-loyalty is a

consequence of e-satisfaction, e-service quality, e-trust and e-perceived value;

To assess the effects of e-service quality, e-trust, e-perceived value and e-

satisfaction on e-loyalty;

To assess the interrelation between e-service quality, e-trust, e-perceived value

and e-satisfaction;

To investigate the mediating effects of online satisfaction on the relationship

between e-service quality, e-trust, e-perceived value and e-loyalty.

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1.3. Thesis Contribution

This study may contribute to the marketing literature in several ways. First, it integrates

in one theoretical framework satisfaction, service quality, trust and perceived value, in

order to investigate their influence on loyalty towards an online vendor. Second, it

examines the interrelationship between the separate antecedents of e-loyalty. Third, it

studies the mediating effects of online satisfaction on the relationship between e-service

quality, e-trust, e-perceived value and e-loyalty. Fourth, in order to deepen the

understanding of loyalty’s determinants, the study provides a brief theoretical

background of each one of them. Finally, it extends the knowledge of customer

behaviour in online environment, by conducting the research in the context of an e-

commerce company.

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2. THEORETICAL BACKGROUND

In this Chapter the literature regarding the topic is presented. Further, the concept of

online loyalty and its benefits are discussed, followed by a brief explanation of the

factors that might influence online loyalty.

2.1. Literature review

Both marketing academics and practitioners have tried to discover factors which

influence online loyalty. A review of the marketing literature below presents the most

prominent antecedents of customer loyalty in an online environment.

One of the main drivers of e-loyalty has been customer satisfaction. Prior research

regarding this construct have mostly been undertaken in the offline environment (Cyr,

2008) and just in recent time’s satisfaction in online settings has become the focus of

interests to practitioners and academics. The reason is that increased customer

satisfaction with a website leads to a higher customer retention rate and positive referrals

to friends and family, it increases customer repurchase behaviour and ultimately drives

higher firm profitability (Carlson & O'Cass, 2010; Ojo, 2010; Collier & Bienstock, 2006;

Cristobal et al., 2007). Various researchers have reported that e-satisfaction is an

immediate and important factor affecting e-loyalty in different contexts, such as online

business-to-business e-services, online banking and online retail stores (Lee & Overby,

2004; Shun & Yunjie, 2006; Bhattacherjee, 2001; Park & Kim, 2003; Riel et al., 2001;

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Wolfinbarger & Gilly, 2003; Lantieri, 2008; Taylor & Hunter, 2003; Yang & Peterson,

2004). In the same vein, Li et al. (2007) concluded that satisfaction was the most

important predictor in distinguishing between “switchers” from “stayers” in online

consumer markets, i.e. customers who stay with the e-vendor and those who change it.

Devaraj et al. (2002) have claimed that repeated e-satisfaction with purchases eventually

leads to customer e-loyalty. Whereas, this relationship has been challenged by various

researchers, arguing that in an online environment satisfaction not always leads to loyalty

(Cao et al., 2004; Lin, 2003; Huang et al., 2009). Several studies have found that half of

the satisfied customers defect eventually (Reichheld, 2001; Storbacka & Lentinen, 2001;

Jones & Sasser, 1995) and McDougall & Levesque (2000) added that customer

satisfaction is a necessary, but not a sufficient condition for future intentions.

The relationship between e-satisfaction and e-loyalty has been investigated in various

studies. It has been combined with factors such as trust, perceived value, commitment

(Luarn & Lin, 2003); trust and perceived value (Anderson & Srinivasan, 2003); e-service

quality and perceived value (Chang & Wang, 2011; Chang et al., 2009); e-service quality

and e-trust (Kassim & Abdullah, 2008,2010); perceived value and switching costs (Yang

& Peterson, 2004); trust and usability (Flavian et al., 2006); only e-service quality (Ojo,

2010; Carlson & O'Cass, 2010); only perceived value (Gill et al., 2007).

Next to e-satisfaction, online trust has been regarded as an inevitable condition for e-

loyalty development process. A review of the marketing literature points out that the role

of trust is even more important in e-commerce settings since there is no physical store; no

physical interaction between the seller and the buyer; lack of physical presence of the

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product and the consumer finds it risky to provide sensitive information, such as credit

card numbers or personal details, in order to complete the transaction (Yoon, 2002;

Warrington et al., 2000; Ribbink et al., 2004; Papadopoulou et al., 2001; Grabner-Krauter

& Kaluscha, 2003). Lantieri (2008), just as Li et al. (2007), has suggested that if a

consumer does not experience a positive outcome associated with an online store, his or

her trust will be negatively impacted and may result in a termination of any further

interactions with that website. Furthermore, prior research reported that especially the

lack of trust in online companies is a primary reason why many web users do not shop

online (Koufaris & Hampton-Sosa, 2004; Yang et al., 2009; Reichheld & Schefter, 2000)

and this, unavoidably, leads to the economic failure of the e-vendor (Lantieri, 2008;

Torkzadeh & Dhillon, 2002; Constantinides, 2004; Corbitt et al., 2003).

According to Reichheld and Schefter (2000) achieving customer loyalty depends to a

large extent on the vendor’s ability to build and maintain customer trust. Gefen et al.

(2003) commented that trust provides a measure of subjective guarantee that the e-vendor

can make good on its side of the deal, behave as promised and genuinely care. Many

authors share the view that trust is a critical factor of loyalty and suggest that the higher

the degree of consumers' e-trust, the higher the degree of e-loyalty, and the easier it is for

companies to retain consumers by developing long-term customer relationships (Gefen &

Straub, 2004; Chen & Barnes, 2007; Pavlou, 2003; Papadopoulou et al., 2001; Li et al.,

2007; Reichheld et al., 2000; Sirdeshmukh et al., 2002; Pitta et al., 2006; Gefen, 2002).

In order to investigate the possible relationship with online loyalty, e-trust has been

researched together with factors such as e-satisfaction, perceived value, commitment

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(Luarn & Lin, 2003); satisfaction and perceived value (Anderson & Srinivasan, 2003);

satisfaction and e-service quality (Kassim & Abdullah, 2008/2010; Wolfinbarger &

Gilly, 2003); satisfaction and usability (Flavian et al., 2006); satisfaction, quality and

corporate image (Garcia & Caro, 2008); e-service quality (Huang, 2008).

Another construct, regarded as a very important factor influencing online customer

loyalty is perceived value. Being a source of competitive advantage, the construct has

received an enduring interest among marketing researchers in both academia and industry

(Sánchez-Fernández & Iniesta-Bonillo, 2007; Mizik & Jacobson, 2003; Wang et al.,

2004; Ulaga & Eggert, 2006; Khalifa, 2004). The classical view conceives perceived

value as the net benefits, which a customer obtains from a product/service or a web-store

(Zhan & Alan, 2003; Chang et al., 2009; Cravens & Piercy, 2003). McDougall and

Levesque (2000) built on that by stating that in making the decision to return to the e-

vendor, the customer will consider whether or not he/she received “value for money”. In

keeping with earlier research, Yang and Peterson (2004) added that customers will only

stay loyal to an e-vendor if they feel that they are receiving greater value than they would

from the competitors.

In this sense, previous studies suggest that perceived value strengthens electronic loyalty

by reducing consumer’s need to seek alternative e-vendors (Chang et al., 2009; Anderson

& Srinivasan, 2003). Furthermore, Chang et al. (2009) contributed with his finding that

customers with higher perceived value have higher degree of e-loyalty. Past research has

found that customer perceived value is a crucial predictor of online loyalty and this

relationship has been studied in contexts, among many, such as online banking portals,

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online retail stores, travelling websites and online financial services (Bauer et al., 2005;

Kim & Niehm, 2009; Luarn & Lin, 2003; Yang & Peterson, 2004; Wenying & Sun,

2010; Sun et al., 2009; Chang et al., 2009; Cristobal et al., 2007).

In addition, the review of prior research indicates that the influence perceived value has

on online loyalty has been researched in various studies together, among many, with

factors such as satisfaction (Gill et al., 2007; Yang & Peterson, 2004); trust, customer

satisfaction and commitment (Luarn & Lin, 2003); trust and satisfaction (Anderson &

Srinivasan, 2003); e-service quality and satisfaction (Chang & Wang, 2011; Chang et al.,

2009); switching costs and satisfaction (Lam et al., 2004); e-service quality (Cristóbal et

al., 2010).

Furthermore, the review of extant research positions online service quality also as an

important driver of loyalty in an online environment. The construct has been studied for

more than two decades in the traditional brick-and-mortar stores, but just recently its

impact over online ventures’ success and their long-term relationship with customers has

attracted researchers’ interest. Some scholars claim that quality service is something that

customers typically want and value (Gefen, 2002; Zhou et al., 2009), even more in an

online environment, where the online shoppers expect equal or higher levels of service

quality than traditional channel customers (Lee & Lin, 2005). Kuan et al. (2008)

suggested that if a company manages to enhance its e-service quality, the consumers’

willingness to come back and do more business with the online vendor will increase,

which will result in long-term relationships with the consumers, greater customer

retention rate, loyalty and profitability. Nevertheless, according to Kim et al. (2006)

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some e-vendors fail to build online loyal customer base because of poor service quality,

which negatively affects the consumers, such that over 60 percent of online shoppers exit

prior to completion of the transaction and do not come back to the website.

Furthermore, the impact of e-service quality on e-loyalty has been researched in various

studies together, among many, with factors such as satisfaction (Yi & Gong, 2008);

website design, trust and satisfaction (Zhou et al., 2009); satisfaction and perceived value

(Chang & Wang, 2011); trust, perceived risk and switching costs (Gefen, 2002); trust and

satisfaction (Kim et al., 2006); core quality, relational quality, perceived value,

satisfaction (McDougall & Levesque, 2000).

Based on the literature review it can be stated that there is very limited research, if any,

combining in one study e-satisfaction, e-trust, e-perceived value and e-service quality

together, in order to examine their influence on loyalty.

2.2. Online Customer Loyalty

2.2.1. Definition of Online Loyalty

For the last two decades, loyalty has received much attention from marketing researchers

in both academia and industry. As a consequence of the exponential increases in an

online shopping and the unprecedented rate of growth in the number of retailers selling

online (Szymanski & Hise, 2000), there has been a need to extend loyalty to the online

environment by creating the term “e-loyalty”, which is referred to the consumer’s

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loyalty for an online store. Many researchers have tried to define online loyalty, but there

is still no generally accepted definition regarding it. Typically, consumers’ behaviour or

attitude regarding products, services or companies signal the degree of intention to return

to the e-vendor, hence it shows their loyalty.

In the table below various definitions from different authors are gathered, which will give

better understanding of e-loyalty.

Table 1: Definitions of Online Loyalty

Definition Author(s)

Online loyalty is an enduring psychological attachment by a

customer to a particular online vendor or service provider.

Butcher et al.

(2001)

Online loyalty is the customers’ intention to revisit the Internet

stores again based on their prior experience and expectation of the

future.

Lee et al. (2000)

Online loyalty is the perceived intention to visit or use a website in

the future and to consider purchasing from it in the future.

Cyr et al. (2008)

Online loyalty is favourable attitude toward an electronic business

resulting in repeat buying behaviour.

Anderson and

Srinivasan (2003)

Online loyalty is the intention of a consumer to repurchase

products/services through a particular e-service vendor

Luarn and Lin

(2003)

As can be seen from the definitions above, consumers’ intention to revisit repeatedly a

webstore is an important component of e-loyalty and crucial for the success of the web

vendor. If customers show high preference and mental attachment to the specific Internet

store, they can be loyal (Sohn & Lee, 2005).

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For this thesis e-loyalty is defined as customer’s favourable attitude and commitment

towards the online retailer that results in repeat purchase behaviour, based on the study

of Srinivasan et al. (2002).

2.2.2. Dimensions of Online Loyalty

Together with the importance of defining loyalty, the marketing literature is concerned as

well with identifying its dimensions. Understanding them is a critical tool for marketers

to develop their marketing strategies. Moreover, since e-loyalty is an extension of the

traditional loyalty in an online environment (Reicheld & Schefter, 2000), the traditional

loyalty dimensions appear to apply to e-loyalty as well with small differences.

A review of the marketing literature suggests that consumer loyalty has a few

dimensions. Since its very first definitions there is a debate about identifying whether

loyalty is based on behavioural or attitudinal approach (Jacoby & Kyner, 1973; Dick &

Basu, 1994), depending on the relative emphasis on respectively, the purchasing or the

cognitive component (Mellens et al., 1996). Loyalty by strong preference can be

comparable to attitudinal loyalty and loyalty by high repeat patronage can be comparable

to behavioural loyalty (Sohn & Lee, 2005; Gao, 2005).

Some authors claimed that customer loyalty is based solely on behavioural concepts

(Cunningham, 1956; Dekimpe et al., 1997; Jacoby & Chestnut 1978; Pritchard, 1991).

By its nature behavioural loyalty can be considered as the actual purchases over a certain

period of time (Mellens et al., 1996) or as the amount of purchases for a particular brand

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(Javalgi & Moberg, 1997). Bowen and Chen (2001) consider this construct as a

consistent, repetitious purchase behaviour which is an indicator of loyalty. In contrast

with traditional store, in electronic commerce have to be taken into consideration the

repeat website visits without purchases and the time spent at the e-store (site stickiness)

(Gommans et al., 2001). In addition, when behavioural loyalty is extended to online

environment, it is expected that e-loyalty will result in positive word-of-mouth

(Srinivasan et al., 2002).

According to Mellens et al. (1996), the advantages of behavioural loyalty are that it is

based on actual purchases; it is not likely to be accidental as it is usually based on

behaviour over a period of time and it is relatively easier to collect than attitudinal data.

However, Day et al. (1979) suggested that behavioural loyalty has also disadvantages and

one of them is unfortunately that it cannot predict consumer behaviour, as it can be a

result of situational factors such as high switching costs or available alternatives (Dick &

Basu, 1994). In an online context the disadvantage even grows bigger, since the online

consumer has access to large amount of information over a product, service or e-vendor

for a short amount of time (Gommans et al., 2001). Furthermore, according to Day

(1969), the bigger disadvantage of behavioural loyalty is that it does not make difference

between brand loyalty and repeat buying.

As a result researchers have proposed the attitudinal dimension in order to measure

loyalty. Attitudinal loyalty in contrast, compared to behavioural one, can make a

separation between the brand loyalty and the repeat buying (Mellens et al., 1996). In

general the attitudinal measurements are concerned with the sense of loyalty, engagement

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and allegiance (Bowen & Chen, 2001). Therefore it can be stated that the emotional

attachment toward a product, service or organization is the root of attitudinal loyalty. The

degree of this attachment defines the (purely cognitive) degree of consumer’s loyalty

(Hallowell, 1996), which in turn is based on stated preferences, commitment or purchase

intentions of the individual (Mellens et al., 1996).

Furthermore, the attitudinal measures of loyalty include trust, emotional attachment or

commitment, and switching cost (Baloglu, 2002). Attitudinal loyalty can be divided into

three stages – cognitive, affective and conative. According to Dick and Basu (1994) the

cognitive component is associated with the informational determinants (e.g. brand beliefs

that one brand is preferable than another); affective loyalty is related with feelings and

reflects a favourable attitude toward a brand and finally, conative loyalty is related to the

behavioural intentions towards the brand, containing commitment to repurchase.

According to Gommans et al. (2001), in order to strengthen the cognitive dimension in an

online environment, it is needed to offer customised information, where for the affective

dimension more focus on the roles of trust, privacy, and security is required. However, a

weak point of attitudinal loyalty is that it cannot accurately reflect the reality, as it is not

based on actual purchases and it is harder to collect attitudinal data (Mellens et al., 1996).

In addition to the understanding that loyalty can be based only on behavioural or

attitudinal approach, Day (1969) suggested for the first time that loyalty should be seen

as a construct which entails both behavioural and altitudinal dimensions. Later Jacoby

(1971) defined this bi-dimensional concept as composite loyalty, which according to

Jacoby and Chestnut (1978), should always represent favourable attitudes, intentions and

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repeat-purchase. Pritchard and Howard (1997) proposed that the new concept should

measure loyalty much better using product preferences of the consumer, tendency of

brand-switching, frequency, recency and total amount of purchase (Pritchard & Howard,

1997; Hunter, 1998; Wong et al., 1999). The combination of behavioural and attitudinal

loyalty results in four categories, where both relative attitude and repeated patronage can

be either ‘high’ or ‘low’. The four types can be seen in the table below.

Table 2: Relationship between behavioural and attitudinal loyalty (Adapted from Dick & Basu, 1994)

True loyalty is characterized by a strong attitudinal attachment and high repeat

patronage; it exists when buyers make high percentages of purchases from the

preferred brand, while being least vulnerable to competitive offerings (Heiensa &

Pleshko, 1996; Baloglu, 2002);

No loyalty occurs when a consumer has no preferences and there is little or no

purchasing from a brand (Heiensa & Pleshko, 1996); when there is no loyalty,

customers would visit another e-vendor due to cost or price promotion;

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Spurious loyalty emerges when the customers perceives little difference between

alternatives, i.e. there is no emotional attachment, but purchase one brand more

consistently than others (Weiwei, 2007). According to Javalgi and Moberg

(1997), it could occur if there were no alternatives in a category. The high

patronage levels of spuriously loyal customers can be explained by factors such

as habitual buying, financial incentives, convenience, and lack of alternatives, as

well as factors relating to the individual customer’s situation (Baloglu, 2002);

Customers with latent loyalty exhibit low patronage levels, although they hold a

strong attitudinal commitment to the company (Baloglu, 2002). Latent loyalty is

evident when a buyer has a favourite brand, but does not purchase it very often

(Heiensa & Pleshko, 1996). This might be due to not sufficient resources to

increase the patronage or because the company’s price, accessibility, or

distribution strategy may not encourage the customers to repeat the purchase

(Baloglu, 2002).

2.2.3. Benefits of Customer loyalty

The positive consequences of having a true loyal customer base are numerous, that is

why firms are constantly trying to find ways to keep current customers and at the same

time to attract new ones (Heiensa & Pleshko, 1996; Yang & Peterson, 2004).

Butcher et al. (2001) proposed that loyal customers are active ambassadors of the e-

vendor/service/product and that the possible outcomes of this are providing positive

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word-of-mouth, recommending the service/product to others, encouraging others to use it

and defending the vendor’s virtues. Word of mouth is seen as the most effective and

economical factor for online vendors to grow their websites (Rigby et al., 2000). Loyal

customers will refer the product or service to others and as a result the e-vendor will have

an increased customer base and will save costs, which otherwise had to be spent on a

marketing activity.

Further, acquiring new customer in an online environment is five times more expensive

than to retain an existing one. At the same time, in order an online apparel retailer or

grocery e-vendor to break even, they have to retain the customer accordingly for 12 and

18 months (Rigby et al., 2000; Gefen, 2002). Therefore, many researchers see the

customer resistance to switch the e-vendor as an important outcome of loyalty. Switching

costs, defined as the “onetime costs that customers associate with the process of

switching from one provider to another” (Burnham et al., 2003), can be seen as a reason

why loyal customers tend not to change from one vendor to another.

Switching cost encompasses learning costs, which refer to the time “sunk” into becoming

familiar with a website and the navigation through it; artificial cost (barriers created by

the vendor such as frequent buyer programs or contracts) and the time, money or effort

required to change e-stores (Thatcher & George, 2004). According to Shankar et al.

(2003), if loyal customers switch, they will lose their loyalty benefits (e.g., emotional

loss, rewards program) or face a potentially unfamiliar service encounter and, as a result,

they may further perceive the loss to be higher than the short-term gain of moving to a

new e-vendor. Moreover, loyalty leads as well to less price sensitivity. Even if a

18

competitor offers cheaper alternatives, the loyal consumer is often willing to buy from

the e-vendor which he already trusts and spend more money for the product/service he

knows already.

There are researchers who suggest that loyal customers are beneficial to a company,

because they are willing to purchase a greater variety from the e-store’s products or

services, possibly at a higher price, which leads to a higher revenue for the seller (Griffin,

2002; Tsoukatos & Rand, 2006; Rigby et al., 2000).

In addition, Griffin (2002) stated that loyal customers bring to a company the benefit of

reducing failure costs. This was further explained by Bove and Johnson (2009) who

stated that loyal customers require a significant incentive to defect; hence they are more

tolerant of minor mistakes or inconsistencies when they occur. They understand better

when something goes wrong (Gefen, 2002).

2.3. Factors influencing Online Loyalty

2.3.1. Online Service Quality

More than a decade after the advent of the Internet, the online channel for distribution of

goods and services is very important and more powerful than ever. It stays a critical

channel for selling. Therefore in order to maintain success and to keep the customer

satisfied, the e-retailers need to offer an excellent electronic service quality, or e-service

quality as it has become more commonly known. E-service quality is recognised as one

19

of the key determinants for success or failure in online business (Carlson & O’Cass,

2010; Barnes & Vidgen, 2002; Kim et al., 2009). As DeLone and McLean (2003, 2004)

noted, service quality in the online environment is more important than previously

thought. Initially it was considered that it is enough only to have a web presence and low

prices in order to have a successful business (Parasuraman, 2005). Nowadays, e-service

quality is recognised as a crucial factor used by e-marketers in their marketing strategy,

in order to differentiate their services from the competitors. The service managers have to

ensure that they offer high service quality, which meets or exceeds the expected by

consumers service level (Ladhari, 2009).

Collier and Bienstock (2006) suggested that an online service encounter starts with a

customer making a behavioural choice visiting a particular website. The website is the

first experience that the consumer has with an e-retailer. Further, in their article Carlson

and O'Cass (2010) noted that customers form their initial opinion en assessment of e-

service quality based on the particular dimensions of the website interface, because of the

limited human interaction with the e-retailer in the delivery of products or services.

In the same direction, Collier and Bienstock (2006) stated that the e-retailers need to

understand that the online website of a company is just as a traditional brick-and-mortar

store. They give an example that if a consumer goes to a shop and it is hard to find the

desired product, the prices are not matching and the atmosphere is not welcoming, most

likely the customers will be dissatisfied and will not return to the store. The authors

suggested that the same principle applies to online web-store (Collier & Bienstock,

2006). If the customers find that a company’s website is hard to use, with inadequate and

20

wrong information, then the dissatisfaction of the experience will affect future behaviour

to repurchase from the website and most likely the consumers will visit another

electronic store in order to shop online.

Service quality has been researched and studied in services literature for more than two

decades, but it has been just recently referred to the online environment (Fisk et al., 1993;

Pitt et al., 1995; Parasuraman and Zeithaml, 2002, 2005; Fassnacht & Koese, 2006; Yi &

Gong, 2008). There is no generally accepted definition of e-service quality, but the most

common ones can be seen in the table below, which will give a better overview of its

concept.

Table 3: Definitions of Online Service Quality

Definition Author(s)

Service quality on the Internet is the extent to which a

website facilitates efficient and effective shopping,

purchasing, and delivery of products and services.

Zeithaml, Parasuraman, and

Malhotra (2000)

Online service quality is defined as a consumer’s

overall evaluation and judgement of the quality of the e-

service delivery in the internet marketplace

Santos (2003)

Online service quality relates to customers’ perceptions

of the outcome of the service along with recovery

perceptions if a problem should occur.

Collier & Bienstock (2006)

On the basis of the table above, it can be stated that e-service quality is broadly defined

and in general it includes both pre- and post- website service aspects (Chang et al., 2009).

21

Together with defining, measuring and monitoring e-service quality are as well very

important for the service sector. Traditionally, “SERVQUAL” scale, created by

Parasuraman et al. (1985, 1988), is probably the most commonly used measure for

service quality of a variety of traditional offline services. Researchers have tried to apply

this method to the online service context, but the dimensions of “SERVQUAL” do not fit

the data adequately (Ladhari, 2009). The reason is that online services have different

characteristics, which the offline services do not have (Collier & Bienstock, 2006).

Therefore, with the accelerating use of online services, the aim is to create a

measurement scale of service quality, which can be applied to the virtual world. Different

studies have been conducted in order to find out adequate dimensions for assessing e-

service quality. The most commonly proposed ones by authors are communication,

customer service, ease of use, information, reliability, responsiveness, security, trust,

website design, etc. Mostly the researchers propose them after analyzing the consumers’

comments about their online experience while shopping.

However, there is an inconsistency between the dimensions proposed from different

researches. Various established scales exist and the most researched ones are WebQual

(Lociacono et al., 2000), SiteQual (Yoo & Donthu, 2001), eTailQ (Wolfinbarger & Gilly,

2003) and NetQual (Bressolles, 2006). Their corresponding dimensions of website

service quality are summarised and can be seen in Table 4 below.

22

Table 4: Summary of main measurement scales for online service quality

In the same direction, Parasuraman et al. (2005) developed two scales: (1) basic E-S-

QUAL scale, relevant for the entire customer base of a website, the purpose of which is

purely to measure the service quality of websites and (2) E-RecS-QUAL, which is

relevant only to customers who had non-routine encounters with the websites.

E-S-QUAL scale consists of 22 items integrated in the following 4 dimensions:

Efficiency - The ease and speed of accessing and using the site.

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Fulfilment - The extent to which the site’s promises about order delivery and item

availability are fulfilled.

System availability - The correct technical functioning of the site.

Privacy - The degree to which the site is safe and protects customer information.

According to Rowley (2006), E-S-QUAL scale presents the primary dimensions of e-

service quality, based on costumer’s experience and evaluation perspective, which can be

accepted also as the antecedents to the adoption of e-service quality. Additionally,

Boshoff (2007) and Connolly (2007) concluded that the developed scale is a useful tool,

which can be used in a variety of situations.

For the purpose of this thesis the E-S-QUAL scale, developed by Parasuraman et al.

(2005), will be used as a measurement tool in order to assess e-service quality.

2.3.2. Online Trust

How can online buyers be sure that the information on the website in regard to services

or products is correct and that their order will be delivered on time? When they fill in

their personal information, how do they know that it will not be used for a fraud? How

can they be sure that their credit card details will be protected? Unfortunately, the answer

mostly is that they do not know.

Keeping this in mind, e-marketers and online ventures must find ways how to win and

keep consumers’ trust and build positive relationships with them, in order to reduce the

uncertainty.

24

Trust has always been a key element in successful marketing (Urban et al., 2000) and

moreover, it plays a crucial role in developing customer loyalty. According to Gefen

(2002), loyalty is a main goal of any profit-oriented company and it is all about earning

consumer’s trust. He suggested that a buyer who trusts the vendor will engage in a

business transaction and will return for additional purchases, whereas without trust in the

relationship the buyer will stay away from the seller. By implementing trust-based

strategies, companies can build long-term relationships with consumers, which lead to

increased sells and profits. Reichheld and Schefter (2000) emphasized that role by stating

that ‘‘to gain the loyalty of customers, you must first gain their trust. That’s always been

the case but on the web it’s truer than ever’’ (p.107).

Since the use of Internet and particularly the number of the online commercial

transactions has increased, the trust between buyer and seller (online website) has indeed

surfaced as a major component of an e-commerce. It has become a key characteristic

correlated with the success or failure of many online ventures (Urban et al., 2000). For

Internet companies it is much harder to promote trust than their brick-and-mortar

counterparts (Jarvenpaa & Grazioli, 1999), because of the high uncertainty existing in

commerce with such vendors (Gefen, 2002). Nohria and Eccles (1992) suggested that

due to the following factors, it is hard to build trust online: (1) absence of simultaneous

existence in time and space, (2) absence of human network attributes (i.e., audio, visual,

and sensual), and (3) absence of feedback and learning capability.

In keeping with earlier research, Warrington et al. (2000) proposed that lack of physical

presence of the product, the inability to examine its quality, and the physical distance

25

between buyer and seller are the conditions which form consumers’ perceptions of

uncertainty and risk. In the same direction, the absence of salespeople (Yoon, 2002), the

corresponding incapability of consumers to assess the trustworthiness of the salesperson

through body language (Gefen, 2002), and the anonymity of the Internet are as well main

factors in increasing consumers’ anxiety and risk perceptions (Constantinides, 2004).

Similarly, Dellarocas (2001) stated, “…the more the two sides of a transaction are

separated in time and space, the greater the risks”.

Electronic exchanges contain multiple risks to customers (Grabner-Krauter & Kalusha,

2003). Online buyers are worried about various things, such as their private information,

including name, address, credit card details; about the product quality, availability of

returns, credit card frauds, and product delivery (Warrington et al., 2000). The consumers

do not know how the seller will use their personal information and, therefore, they want

to make purchases only from online vendor who is honest and will not violate the

relationship by using unfair pricing, misleading information, unauthorized use of credit

card information or distribution of personal data (Warrington et al., 2000; Gefen, 2002;

Grabner-Krauter & Kalusha, 2003). Internet users mostly refrain from shopping online

namely due to security reasons. Hence, customer trust in the online store is crucial,

because of the little guarantee in an electronic environment (Gefen, 2002).

Trust is a complex construct, which has been widely studied across different disciplines.

Though there are many definitions in the literature regarding it, it is hard to define it due

to its multi faceted nature. It has been mostly defined in terms of intensions and beliefs.

26

The most common definitions used by authors can be seen in the table below, which will

give an overview and better understanding of what trust is.

Table 5: Definitions of Online Trust

Definition Author(s)

The belief that a party's word or promise is reliable and

a party will fulfil his/her obligations in an exchange

relationship.

Blau (1964) and Rotter (1967)

The willingness of a party to be vulnerable to the

actions of another party based on the expectations that

the other will perform a particular action important to

the trustor, irrespective of the ability to monitor or

control that other party.

Mayer et al. (1995)

Trust is defined as the willingness to make oneself

vulnerable to actions taken by the trusted party based on

the feeling of confidence or assurance.

Gefen (2002)

The degree of confidence customers have in online

exchanges, or in the online exchange channel.

Ribbink, van Riel &

Streukens (2004)

An attitude of confident expectation in an online

situation of risk that one's vulnerabilities will not be

exploited.

Corritore, Kracher &

Wiedenbeck (2003)

For this study, the definition of Ribbink, van Riel & Streukens (2004) is applied: “Trust

is the degree of confidence customers have in online exchanges, or in the online

exchange channel.”

27

Further, despite the similarities between offline and online trust, there are some

distinctions. In order to differentiate them, the study of Hassanein and Head (2004) is

used in this thesis. According to the authors online trust has the following characteristics:

The parties involved may interact across different times and locations, and the

rules and regulations may vary across these zones;

There is less data control during and following its transfer;

There are lower barriers to entry and exit for online businesses;

Physical trust cues (such as investments in physical buildings, facilities and

personnel) are not visible in the online environment;

The physical evaluation of products is difficult in an online setting, as consumers

can only rely on the senses of vision and sound;

Electronic transactions are generally more impersonal, anonymous and automated

than person-to-person offline transactions.

In order to deepen further the understanding of the complexity and multi-dimensional

construct of trust, it is worth noting its dimensions.

At the heart of various studies, trust has been identified as trusting beliefs or as trusting

intentions (Gefen et al., 2003; Jarvenpaa et al., 2000; Lim et al., 2001; McKnight &

Chervany, 2002; Gefen, 2000; Gefen & Silver, 1999).

In the online environment and the corresponding e-commerce transactions, trusting

beliefs include the online consumers’ beliefs and perceptions about particular trust-

28

related attributes of the e-vendor (Grabner-Krauter & Kalusha, 2003; Luarn & Lin,

2003). Mayer et al. (1995) identified it as “trustworthiness”.

Furthermore, in their research McKnight et al. (2002) defined trusting intentions as the

extent the truster (i.e. online consumer) is securely willing to depend, or intends to

depend, on the trustee (online vendor) in a given situation, despite the incapability of

influencing or controlling the seller. According to Luarn and Lin (2003), such intentions

are frequently discussed in the literature regarding to online sharing of personal

information, making a one time or repeating e-purchase, or acting on information

provided by an e-vendor. Many authors have revealed that trusting beliefs positively

influence consumer’s purchase intentions (McKnight et al., 2002; Kim & Benbasat,

2003; Verhagen et al., 2004; Gefen et al., 2003) and, therefore, it is of a great interest as

well for the researcher to understand, which are the factors influencing trusting beliefs

(Connolly & Bannister, 2007).

In addition, as it has been stated earlier in this thesis, trusting beliefs formed by

customers are based on competence, benevolence and integrity of the e-vendor (Gefen,

2002; Gummerus et al., 2004). These findings are extended by Chen and Dhillon (2003)

and after an extensive literature review they have identified competence, benevolence

and integrity as the three main dimensions of trust. The authors explained each one of

them as follows in terms of company trust:

Competence includes a company’s ability to fulfil its promises communicated to

consumers;

29

Benevolence is the probability a company holds consumers’ interests ahead of its

own self-interest and indicates sincere concern for the welfare of the customers;

Integrity suggests that a company acts in a consistent, reliable, and honest manner

when fulfilling its promises.

In keeping with earlier research, Connolly and Bannister (2007) supported the view that

the competence of a website can be regarded to the design of the website; its ease of use

and reliability; the speed of the transaction; the correct and timely delivery of the

product, considered as the fulfilment of the transaction; and the presence of security

features. They suggest that the second component of trust, benevolence, is widely

recognised as influencing trustworthiness and implies a perception of positive intent and

good motives. The authors further adopt in their research the description of integrity as

the trusting party’s perception that the trusted party will be honest and adhere to an

acceptable set of principles. Consequently, Connolly and Bannister (2007) conclude that

if an individual (an online venture) has all three characteristics, will be accepted as very

trustworthy.

According to Ribbink et al. (2004), the lack of trust is a frequent reason for the online

consumer to not make online transactions at an e-vendor or to return for additional

purchase. Consequently, e-buyers want to purchase only from trusted sellers with trusted

web-stores (Huang, 2008). As a result these websites have higher rates of customer

conversion, retention (Urban et al., 2000) and liquidity (Sultan et al., 2001), in

comparison with sites that do not generate loyalty. But in order to build a loyal customer

30

base, the online vendor must first win and keep consumers’ trust, which plays a crucial

role in an e-business.

2.3.3. Online Perceived Value

Value creation has gained much attention in the literature and it has been considered as a

main part of every organization’s mission and statement (Sweeney & Soutar, 2001). In

particular, perceived value is seen by many marketers and practitioners as an imperative

for a strategic management, prerequisite for a long-term success and a source of

sustainable competitive advantage (Khalifa, 2004; Spiteri & Dion, 2004; Sweeney &

Soutar, 2001). Indeed, Slater (1997) suggested that ‘... the creation of customer value

must be the reason for the firm’s existence and certainly for its success’.

However, perceived value has been mostly researched in offline environment, while its

role in an online setting is even more important, considering that the search costs are

reduced and this brings more competitive prices for the e-consumer. Consequently, the

lower search costs on the Internet and the multiple search facilities allow the consumers

to find quickly products or services and to compare their features and prices (Anderson &

Srinivasan, 2003). Moreover, online customer value is highly context-dependent,

meaning that in an online environment, the product/service, web-store and the Internet

channel all together bring value to customers (Zhan & Alan, 2003).

In the same direction, perceived value contributes to the loyalty of an e-business by

reducing an individual’s need to seek alternative service providers (Anderson &

Srinivasan, 2003; Yang & Peterson 2004). This matter is further elaborated by Anderson

31

and Srinivasan (2003), suggesting that when the perceived value is low, e-customers will

be more inclined to switch to competing businesses in order to increase perceived value,

thus contributing to a decline in loyalty. The authors claimed that even satisfied

customers are unlikely to repurchase on the same website, if they do not feel like they are

getting the best value and instead, they will seek out other websites in an ongoing effort

to find better value (Anderson & Srinivasan, 2003). Therefore, in order to generate

highly loyal customers, the e-vendors have to make sure that they provide good

satisfaction and high perceived value.

Accordingly, perceived value is of a high strategic relevance to organization and being

relatively new component in marketing literature, the service managers have to gain

greater in-depth understanding of it. However, extensive literature review indicates

ambiguity with respect to the definition, dimensions, and measurement of perceived

value (Fernández & Bonillo, 2007).

Khalifa (2004), just as other authors (Roig et al., 2006; Woodruff, 1997; Fernández &

Bonillo, 2007), has suggested that the concept has been one of the most misused ones

from the marketing researchers in both academia and industry, being as well

interchangeably named in the literature value, shopping value, consumption value,

relationship value, product value, service value, expected value, consumer value,

customer value, perceived value and received value, while all these concept differ from

each other.After conducting an extensive review of the academic literature, Chang et al.

(2009) concluded that the main attributes of different authors’ definitions concerning

customer perceived value is that:

32

Perceived value for a consumer is related to his expertise or knowledge, of buying

and using of a product;

Perceived value for a consumer is related to the perception of a consumer and

cannot be objectively defined by an organisation;

The customer perceived value presents a trade-off between benefits and sacrifices

perceived by customers in a supplier’s offering.

Various definitions of the term have been proposed and can be seen in the table below,

which will give an overview and better understanding of what perceived value is.

Table 6: Definitions of Online Perceived value

Definition Author(s)

Perceived value is the difference between the prospective

customer’s evaluation of all the benefits and all the costs

of an offering and the perceived alternatives.

Kotler and Keller (2006)

Perceived value is created when the benefits a consumer

gets from a product are greater than the long term costs a

consumer is expected to pay with a product.

Slater and Narver (2000)

Perceived value equals a perceived quality relative to the

price.

Hallowell (2000)

Perceived value is a trade-off between desirable attributes

compared with sacrifice attributes.

Woodruff and Gardial

(1996)

Perceived value is the consumer’s overall assessment of

the net benefits gained from shopping at a store through

successfully obtaining quality products and shopping

enjoyment.

Zhan and Alan (2003)

33

However, one of the most commonly cited and accepted definition, as well the most used

as basis for studies into this concept, is given by Zeithaml (1988, p. 13), which stated that

perceived value is “the consumer’s overall assessment of the utility of a product (or

service) based on perceptions of what is received and what is given”.

Sweeney and Soutar (2001) elaborated on this definition that the two components price

and quality have different effects on ‘perceived value’ and adopted the view of Zeithaml

(1988) who stated that some consumers perceive value when there is a low price, while

others perceive value when there is a balance between quality and price. Hence, for

different consumers, the components of perceived value might be differentially weighted.

Fernández and Bonillo (2007) claimed that the definition of Zeithaml (1988) considers

‘perceived value’ as a uni-dimensional construct that can be measured simply by asking

respondents to rate the value that they received in making their purchases.

On the basis of her definition, Zeithaml (1988, p. 13) identified four potential meanings

of value:

Value is low price;

Value is whatever one wants in a product;

Value is the quality that the consumer receives for the price paid;

Value is what consumers get for what they give.

A review of existing literature in a service marketing context reveals that most of the

academic research has been focused on the forth description. Perceived value has its root

in equity theory and a common view shared by many scholars is that perceived value

34

represents a trade-off between the consumers’ evaluation of benefits (what consumers

get) and the sacrifices (what consumers give) from the purchase and use of a

product/service (Parasuraman & Grewal, 2000; Grewal et al., 1998; Kotler, 2000;

Cravens & Piercy, 2003). Most of the researchers view product (service) as the get

component and the price as the give component. However, extended to the online

environment the ‘get’ and ‘give’ trade-off involve slightly different aspects. According to

Luarn and Lin (2003), the online consumer gives time, cognition and effort during an

interaction with an e-tailer’s website, and gets an experience related to easy search of

needed/wanted products, quick checkout and receiving confirmation about all of the

important aspects of the purchase, such as order-confirmation and delivery-tracking.

In the same direction, Sanchez et al. (2006) suggested a number of benefits and sacrifices

which can be seen in Table 7 below.

Table 7: Components of the cost-benefit conceptualisation of perceived value(Adapted from Sanchez et al., 2006)

Benefit components Sacrifice componentsEconomic benefits Price sacrifices

Emotional benefits Time sacrifices

Social benefits Effort sacrifices

Relationship benefits Risk

Inconvenience

According to the authors (Sanchez et al., 2006), economic benefits are connected with the

monetary savings of the consumer when they purchase product or services (e.g., buying

35

product/service at a lower price). They see emotional benefits as the positive feelings the

consumers gain when they buy product or service (e.g., buying vacation packages) and

social benefits is received from service’s/products’ characteristic to enhance social self-

concept (e.g., social status). Further, the authors explain that relationship benefits are

received when a company can frequently deliver high value to the consumer (Sanchez et

al., 2006).

Then Sanchez et al. (2006) described price sacrifice as the monetary cost perceived by

consumers (e.g., if the expected price of a product or service is higher, the consumer has

to decide whether to purchase it). According to them time sacrifices are the amount of

time consumers are spending on searching and buying products or services and the effort

sacrifices involve the physical energy spent by consumers to search, find and buy

product or a service. Further, the authors suggested that risk is the chance that there

would be negative consequences of purchasing or consuming the product or service and

that inconvenience is the case when consumers are having a bad experience while

consuming product/service (Sanchez et al., 2006).

However, other researchers do not agree with the conceptualization of perceived value

seen only as trade-off between benefits and sacrifices. An extensive review of the

academic literature reveals that perceived value has been approached in many different

ways and it is difficult for the academic practitioners to reach an agreement and

consistency in regard to its dimensions and measurements.

These findings of perceived value are extended, suggesting that it is a dynamic construct

consisting of four dimensions: acquisition value; transaction value, in-use value and

36

redemption value (Grewal et al., 1998; Parasuraman & Grewal, 2000; Krishnan et al.,

1999). Parasuraman and Grewal (2000) give the following explanation:

Acquisition value is the benefit the buyer’s belief they are getting when obtaining

a product/service;

Transaction value is the difference between the consumers’ internal reference

price and the price offered within the context of a special deal;

In-use value is the residual benefit which is extracted from using the product or

the service;

Redemption value according to the authors is the benefit at the time of trade-in or

end of life (for products) or termination (for services).

Furthermore, Sheth et al. (1991b) have divided perceived value into five dimensions:

social, emotional, functional, epistemic and conditional value, while Woodall (2003)

proposed fire other notions; namely net value, marketing value, derived value, sale value,

and rational value. In addition, the study of Sweeney and Soutar (2001) revealed four

aspects of perceived value: emotional value, social value, two functional values in terms

of price/value and performance/quality. Similarly, Babin et al. (1994) divided perceived

value into utilitarian and hedonic value. Utilitarian view refers to consumers’ concerns

with purchasing quality products in an efficient and timely manner with a minimum of

irritation (Childers et al., 2001); while hedonic value is an overall assessment of

experiential benefits and sacrifices, such as entertainment or emotive aspects of shopping

experiences (Overby & Lee, 2006). As can be seen there is a complexity inherent in this

area of research and it is difficult to quantify perceived value (Petrick, 2002).

37

In addition, perceived value can be measured using both uni-dimensional (Cronin et al.,

2000; Eggert & Ulaga, 2002) and multi-dimensional approach (Sweeney & Soutar, 2001;

Sheth et al., 1991) depending on the desired result. Alves (2011) and Lin et al. (2005)

have suggested that when seeking to understand the effects of perceived value in other

constructs, such as satisfaction and loyalty, uni-dimensional approach should be utilised.

As perceived value has been linked earlier in this study with satisfaction, loyalty and

(re)purchase intentions, and considering that one of the purposes of this thesis is to find

the particular effect perceived value has on satisfaction and loyalty; it can be stated that

the uni-dimensional approach will be applied.

2.3.4. Online Satisfaction

An important factor determining loyalty is consumer satisfaction. It has been recognized

as a differentiating mechanism for what the company has to offer (Chang et al., 2009), an

important construct affecting participants’ motivation to stay with the channel (Devaraj et

al., 2002) and a main component for the long-term profitability of the company (Wirtz,

2003). Zairi (2000) views customer satisfaction as one of the most discussed challenges

of organisations and it represents every company’s sole purpose, it is at the heart of every

mission statement, and it is the ultimate goal of any strategies put in place.

However, recent studies show that differences in building and evaluating consumer

satisfaction exist between online and offline environments (Chen, 2008; Wu & Padgett,

2004). It is difficult to attract new customers and generate loyalty in non-traditional

38

business contexts, such as the Internet; therefore satisfaction with the e-vendors and their

services/products is more important online than offline (Gommans et al., 2001; Ribbink,

2004; Reichheld & Schefter, 2000). Building on that, Wu and Padgett (2004) stated that

virtual markets provide information about alternative product/services, especially for

functional ones, in a way that facilitates direct comparisons of attributes. A competing

offer is just a few clicks away on the Internet (Venkatesh et al., 2003) and that is why

evaluating customer satisfaction in online environment is of a great importance for every

manager. In their quest to develop a loyal customer base, most companies try their best to

continually satisfy the needs and wants of their customers and to create long-term

relationships with them (Anderson & Srinivasan, 2003).

According to Zairi (2000), the customers’ overall satisfaction with a vendor is being

generated during each stage of the purchase experience: from choosing the product

(service), through purchasing it, to any subsequent interaction with the firm after the

purchase. Additionally, the satisfaction is related as well to the product’s perceived

performance relative to consumers ‘expectations. The difference between the

expectations and the perceived quality or performance is called disconfirmation. As a

consequence there are few possible outcomes, which are postulates of the widely

accepted paradigm in the literature - expectancy disconfirmation model. According to it,

if the performance is above expectations the consumer will be satisfied (positive

disconfirmation); if the expectations are matched the consumer will have neutral feeling

(confirmation); if the perceived performance is below customer’s expectations, he/she

will be dissatisfied (negative disconfirmation).

39

Being a foundation for consumer satisfaction studies, expectancy disconfirmation model

suggests that consumers judge satisfaction with a product in comparison with their

expectations about the product performance (Yi, 1993; Oliver, 1980). McMullan and

O'Neill (2010) agreed with this statement and added that this model conceptualises

satisfaction as the difference between what a consumer expects to receive and his or her

perceptions of actual delivery.

Satisfied customers are more likely to return to the vendor for future purchases, to

recommend the website to five or six people and eventually to form emotional

attachment with the website (Gummerus, 2004; Carlson & O'Cass, 2010). In contrast,

consumers who are dissatisfied with their experience, most probably will neither visit

again the same website for repeated transactions, nor will develop close relationship. It is

more likely that they will switch to another e-vendor; even complain and say negative

things to more than ten people (Hoyer & MacInnis, 2001; Ojo, 2010).

Accordingly, satisfaction is an important predictor of consumer behaviour and, therefore,

service managers and marketers have to gain better comprehension of what is meant by

customer satisfaction, its nature and dimensions.

However, so far there is no consensus in the marketing literature in regard to the

definition of customer e-satisfaction. Most of the definitions are grounded in the

expectancy disconfirmation theory and the majority of the researchers agree that

satisfaction is an attitude or evaluation, formed by the customer comparing their pre-

purchase expectations of what they would receive from the product, to their subjective

perceptions of the performance they actually did receive (Oliver, 1980; Khurana, 2009).

40

In Table 8 below are presented definitions of customer satisfaction, which will give

better idea about this concept.

Table 8: Definitions of Online Satisfaction

Definition Author(s)

Satisfaction is the perception of pleasurable fulfilment in the

customers’ transaction experiences

Oliver (1997)

Satisfaction is the sum of a customer’s overall feelings and

attitudes toward a purchase situation

Shun and Yunjie

(2006)

Satisfaction is the outcome of a comparison between expected

and perceived performance throughout the customer relationship.

Wangenheim

(2003)

Satisfaction in online environment can be seen as a preferable

attitude towards interaction with the website

Ou (2003)

Satisfaction is the consumers’ judgment of their Internet retail

experience as compared to their experiences with traditional

retail stores.

Szymanski and

Hise (2000)

For this thesis, the definition of Anderson and Srinivasan (2003) is adopted: “Satisfaction

is the contentment of the customer with respect to his or her prior purchasing experience

with a given e-commerce firm”.

Furthermore, in the marketing literature two main conceptualizations of satisfaction exist:

transaction-specific satisfaction and overall or cumulative satisfaction (Shankar et al.

2003; Jones & Suh, 2000; Yi & La, 2004). Chang et al. (2009) described transaction-

specific satisfaction as an emotional response to performance of specific attributes of a

service encounter and Parasuraman et al. (1994) stated that it is the customer’s

evaluations of service quality, product quality and price.

41

In contrast, overall satisfaction refers to the customers’ satisfaction or dissatisfaction

with a vendor, based on all their encounters and experiences with the firm over a period

of time (Jones & Suh, 2000; Bitner & Hubbert, 1994). It can be seen as a function of all

previous transaction-specific satisfactions and it may be based on many transactions or

just a few, depending on the number of times the consumer has used a particular vendor

(Jones & Suh, 2000; Parasuraman et al., 1994).

Yang & Tsai (2007) and Yang & Peterson (2004) stated that overall satisfaction is an

aggregation of customer’s cumulative impression of a vendor’s performance. In the same

direction, Jones and Suh (2000) gave an explanation that low transaction-specific

satisfaction reduce the likelihood of a customer returning to a given company, but only

when the customer’s overall satisfaction with that vendor is low. They further explained

that when overall satisfaction with a company is high, even if there is some

disappointment the customers will give the vendor another chance, therefore overall

satisfaction is a better driver of repurchase intentions than transaction-specific

satisfaction. For that reason, for the purpose of this thesis the focus will be on overall

satisfaction and in the study it is referred as to customer satisfaction.

42

3. HYPOTHESES

This Chapter presents the hypotheses, which are proposed in regard to the factors

influencing customer loyalty and their interrelation. In addition, the theoretical

framework of this thesis is introduced.

3.1. Online Service Quality

One set of literature promotes the idea that there is a relationship between e-service

quality and e-satisfaction. What is more, various studies have revealed that e-service

quality is one of the most important antecedents of online satisfaction (Petersen, 2001;

Urban et al., 2000; Wingfield & Rose, 2001). It is widely stated and accepted that in

order an e-vendor to have satisfied customers which will return to the website, superior e-

service quality is needed. Caruana (2002) supported this argument by adding that as a

process in time, service quality takes place before, and leads to overall customer

satisfaction. According to Devaraj et al. (2002) and Carr (2002), e-satisfaction and e-

service quality are strongly related and Yang (2007) stated also that e-service quality has

positive strong effects on online satisfaction. Likewise, Ribbink et al. (2004) concluded

that in an online retail store e-satisfaction is directly affected by e-service quality.

Furthermore, Carlson and O'Cass (2010) suggested that the levels of customer

satisfaction (or dissatisfaction), which can be influenced from quality attributes of a

website, will most likely cause positive (or negative) customer attitude towards the

website, and eventually will induce positive (or negative) behavioural intentions. The

satisfaction will be influenced by the quality of the website and its characteristics,

considering that the website is the only interaction with the company (Anderson &

43

Srinivasan, 2003; Bansal et al., 2004). If the delivered e-service quality is assessed as a

high quality e-service, then e-satisfaction should appear as a consequence.

By contrast, other researchers do not agree with the opinion that there is a relationship

between e-service quality and e-satisfaction. Wu & Ding (2007) found in their research

that e-service quality has no direct effect on customers’ e-satisfaction, but even a weak

direct negative effect. In their research Falk et al. (2010) stated that many e-tailers

assume the relationship between e-service quality and e-satisfaction is linear and,

therefore, they add frequently new attributes to their websites in order to enhance their

business presence. However, the authors suggested that the mentioned relationship may

be nonlinear and, therefore, quality improvements may have an asymmetric effect on

satisfaction (Falk et al., 2010). These findings are in contradiction with the notion the e-

service quality is an antecedent of e-satisfaction (Wu & Ding, 2007).

This thesis will support the findings of the researchers who insist that there is a

relationship between e-service quality and e-satisfaction. Therefore, based on the

literature review above the following hypothesis is developed:

H1: An increase in e-service quality leads to an increase in the customer e-

satisfaction with a website.

Further, in the electronic commerce content consumers would not return to an e-vendor if

they do not find his website not only easy-to-use, but trustworthy and secure as well.

There are number of studies supporting the notion that e-service quality is an antecedent

44

of e-trust (Sultan & Mooraj, 2001; Hennig-Thurau & Klee, 1997). However, some

scholars do not fully support this argument. Harris and Goode (2004) investigated the

effect of e-service quality on e-trust by conducting two studies in two different service

contexts – online purchase of books and online flight purchasing. The results revealed

that the hypothesis that there is a positive and direct relationship between e-service

quality and e-trust was just partially supported. Similarly, Chen et al. (2002) found as

well that e-service quality is not a strong determinant of e-trust.

On the other hand, Ribbink et al. (2004) found in their research that the confidence

customers have in online exchanges, will be positively affected by the quality of their

online experiences. The result of their research was that e-service quality directly and

positively influences e-trust.

Moreover, prior research shows that trust is based on beliefs such as competence,

benevolence and integrity of the e-vendor and one of the dimensions, competence, is

embedded in e-service quality’s dimensions. Kim et al. (2009) suggested that trust

concerns the notion of competence that includes fulfilling the promised service in a

reliable and honest manner. Gummerus et al. (2004) stated that the quality of the e-

vendor’s service is based on the ease of using the website and its technical functionality,

the correct and timely delivery and its safety, where Kassim and Abdullah (2010) built on

that by adding that these dimensions also reflect the e-vendor’s competence and,

therefore, induce trust. Gummerus et al. (2004) concluded that the service quality of an e-

vendor is expected to affect trust directly, since it provides physical evidence of the

service provider’s competence, as well as facilitating effortless use of the service.

45

Considering the previous arguments, this thesis will support the findings of the

researchers who argue that there is a positive relationship between e-service quality and

e-trust. Thus, based on the literature review above, the following hypothesis is

developed:

H2: An increase in e-service quality leads to an increase in the customer trust

towards a website

In addition, many researchers have found direct positive relationship between service

quality and perceived value (Cronin et al., 2000; Teas & Agarwal, 2000; Chi, Yeh &

Jang, 2008; Hu et al., 2009; Yu & Fang, 2009). Cronin et al. (2000) found in their

research that value is largely defined by perceptions of service quality and according to

them, service consumers place greater importance on the quality of a service, than they

do on the costs associated with its acquisition, i.e. service quality is an important

decision-making criterion for service consumers. According to Parasuraman and Grewal

(2000), service quality is a logical driver of perceived value and even in instances where

the buyer-seller exchange involves a physical product, superior pre-sale and post-sale

service rendered by the seller can add to the perceived value. A recent research of Chang

and Wang (2011) indicated as well that e-service quality has an important role in the

development of perceived value in the online environment. According to the authors

(Chang & Wang, 2011), e-consumers expect equal or higher levels of service quality,

than customers in traditional brick-and-mortar store and as a result when online shoppers

perceive high e-service quality, they will exhibit high customer perceived value and

become a satisfied and loyal customer.

46

On the basis of the preceding, the following hypothesis is proposed:

H3: An increase in e-service quality leads to an increase in the customer’s

perceived value towards a website.

Furthermore, at the heart of various studies is the notion that service quality has a direct

impact and on customer loyalty (Gefen, 2002; Cronin et al., 2000; Kim et al., 2006; Kuan

et al., 2008; Chang & Wang, 2011; Cristóbal al., 2010). Bei and Chiao (2006) made a

research in 3 industries (banks, auto repair shops, and gasoline filling station) and the

results revealed the positive direct relationship between service quality and customer

loyalty.

Although service quality, in general, is important in creating loyal customers across

industries, it is especially important in the case of online stores, because of the ease with

which customers can switch from one online store to another (Gefen & Devine, 2001).

Oliveira (2007) studied the link between service quality and loyalty in online banking

services and the findings provided strong empirical evidence that there is a positive direct

relationship between them. In addition, Huang (2008) found that e-service quality affects

positively e-loyalty in an online travel store and the author stated, that e-service quality is

a key factor to an e-vendor’s ability to differentiate itself from its competitors and to gain

customer’s loyalty. One way of increasing customer loyalty, suggested by Gefen (2002),

is through superior service quality and the author further explains that service quality is

something that customers typically want and value and therefore, providing high service

quality will increase their willingness to come back and do more business with the

47

vendor. However, in a research in online retail store, Chang and Wang (2011) found that

e-service quality does not significantly affect customer loyalty, but it influences it

through e-satisfaction.

There is very little academic, rigorous research addressing the service quality-loyalty link

in an online environment (Oliveira, 2007). Therefore, having this in mind and taking into

consideration the preceding literature overview, the following hypothesis is proposed:

H4: An increase in e-service quality will lead to an increase in e-loyalty towards

website.

3.2. Online trust

It has been claimed that loyalty to a website is satisfaction driven, while trust is the main

antecedent of satisfaction (Gummerus et al. 2004). For online e-tailers it is of a great

importance to identify the main factors that determine customer satisfaction and loyalty.

As a result, there has been considerable discussion in the literature regarding satisfaction,

trust and loyalty, namely whether trust influences customers’ satisfaction, or the other

way around and whether the effect of trust on loyalty is a direct one, or mediated by

satisfaction.

In prior research, trust is found to be a strong antecedent of satisfaction. This relationship

has been researched in many various contexts, including residential energy market,

online B2B environment, online retail stores and online banking services (Doney &

48

Cannon, 1997; Singh & Sirdeshmukh, 2000; Harris & Goode, 2004; Taylor & Hunter,

2003). For example, Gummerus et al. (2004) found in their research that trust is the

strongest predictor of customer satisfaction in an online health care service. According to

them, when consumers trust an e-vendor, this means that the consumers believe they will

receive the promised product/service and will experience a reduced level of risk

(Gummerus et al., 2004). In the same vein, Singh and Sirdeshmukh (2000) stated that the

customers will be satisfied and place an order in Internet only when they fill comfortable

doing this, i.e. trusting the e-vendor. Furthermore, Balasubramanian et al. (2003) found

that trust in an online broker is directly related to the online investor’s satisfaction.

Regarding to an early study of Geyskens et al. (1998), where satisfaction is seen as a

consequence of trust, it is suggested that when channel members (online buyers) trust its

partner (online seller), they will feel secure by way of an implicit belief that the actions

of the partner (online seller) will result in positive outcomes and not in negative

outcomes and the authors stated that this evaluation will lead to a high satisfaction. It can

be added that in any buyer–seller relationship, consumers’ trust evaluations before a

specific exchange episode are having a direct influence on their post purchase

satisfaction (Singh and Sirdeshmukh, 2000; Kim et al., 2009).

By contrast, other authors support the finding that satisfaction is an antecedent of trust in

an online context (Garbarino & Johnson, 1999; Ribbink et al., 2004). In particular, a

research conducted by Pavlou (2003) revealed the positive effect of e-satisfaction on e-

trust in an e-retail store. Ribbink et al. (2004) commented on this study that customers’

satisfactory experiences with a specific e-tailer are expected to increase their willingness

49

to make more online purchases from that e-tailer, as well as their trust in the online

medium as such. Satisfaction with a specific e-tailer increases confidence in the system

as a whole, which leads to trusting the e-tailer (Ribbink et al., 2004). In addition, Kassim

and Abdullah (2010) studied also the relationships between customer e-satisfaction and

e-trust. They found that satisfaction is an important factor in building trust among the

online users. Moreover, they suggested that satisfied customers are more disposed to trust

the online service provider than dissatisfied customers.

Considering the previous arguments, this thesis will support the findings of the

researchers who argue that trust should be present, in order for the customers to be

satisfied with the e-vendor. Thus, the following hypothesis is developed based on the

literature review above:

H5: An increase in e-trust leads to an increase in the customer e-satisfaction

with a website.

Furthermore, previous studies suggest that e-trust is an important antecedent of e-loyalty

(Reichheld et al., 2000; Flavian, 2006; Kim & Moon, 2000). Ou and Sia (2003) stated

that trust reduces the perceived uncertainty and risk in online transactions and that the

reliable feeling towards the trusted party makes the online customers more willing to

involve in the trading relationship, due to the perception of assurance or confidence.

According to Gefen (2002), customers who do not trust an online vendor will be less

inclined to do business with him and, in addition, Anderson and Srinivasan (2003)

claimed that if customers do not trust an e-business, they will not be loyal to it, even

50

though they are generally satisfied. Similarly, in the study of Lantieri (2008) it was found

that trust is a strong predictor of e-loyalty in an online retail store and the author

suggested that lack of trust in a website, or in e-commerce, is a strong barrier to online

transactions.

For their part, Luarn and Lin (2003) conducted a research in an online service

environment, such as travelling services and video on demand, by integrating trust-

related constructs within the broad framework of the Theory of Reasoned Action (TRA).

Luarn and Lin (2003) found that e-trust has a strong positive influence on e-loyalty and

the authors further stated that, in order to attract more consumers to repurchase

products/services from a specific e-vendor, it is not enough only to develop customer

satisfaction, but it is of a significant importance to develop e-trust. On the other hand,

Ribbink et al. (2004) investigated the trust in online stores for books and CDs and the

result revealed that, indeed, e-trust is directly affecting loyalty, but much less than

satisfaction, which may imply that trust is not the anticipated major contributor to loyalty

in an online environment.

It can be concluded that little research is conducted in regard to the trust-loyalty link in

an online context (Stewart, 2003). Therefore, having this in mind and based on the

preceding literature review, the following hypothesis is developed:

H6: An increase in e-trust will lead to an increase in e-loyalty towards a

website.

51

3.3. Online Perceived Value

A review of the service marketing literature reveals perceived value as a contributing

factor to consumer’s satisfaction (Yang & Peterson, 2004; Liu et al., 2003; Cronin et al.,

2000; Spiteri & Dion, 2004; McDougall & Levesque, 2000). In particular, customer

satisfaction is viewed as a post consumption evaluation made by the consumer, regarding

the bought product or service (Graf & Maas, 2008; Sanchez et al., 2006), and it is

contended that value has a direct impact on how satisfied customers are with a vendor

(Anderson et al., 1994). The consumers cannot be fully satisfied with the product or

service delivered, if they do not feel they got their “money’s worth”. Yang et al. (2004),

just as Oliver (1993), suggested that customer value can be considered as a cognition-

based construct, capturing any benefit-sacrifice discrepancy, whereas customer

satisfaction is primarily an affective and evaluative response. Yang & Peterson (2004)

also found in their study that perceived value is an antecedent of customer satisfaction

and they suggested that to enhance satisfaction, a service provider can spend its effort on

improving the value perceived by customers. In addition, researchers claim that if

perceived value is not included in studies aiming at identifying the antecedents of

consumer’s satisfaction, the shortcoming of this approach would be obvious (Anderson et

al., 1994; Heskett et al., 1997).

On the basis of the preceding, the following hypothesis is proposed:

H7: An increase in perceived value will lead to increase in customer e-

satisfaction with a website.

52

Furthermore, a view that is shared by several researchers (Chiu et al., 2005; Ulaga &

Eggert, 2006; Gill et al., 2007) regards perceived value as a reliable predictor of

(re)purchase intention of the e-customers and according to Khalifa (2004), loyalty and

profits are strongly linked to the value that is created for them. The consumers invest

their loyalty in a retailer until it can deliver superior value, relative to the offerings of the

competitors (Khalifa, 2004; Reichheld, 1996). In addition, in making the decision to

return to the e-vendor, the e-shoppers will consider if `value for money' was received or

not (McDougall & Levesque, 2000). Al-Sabbahy et al. (2004) generalized that perceived

value is not only influential at the pre-purchase phase, but it also affects customer

satisfaction, intention to recommend and return behaviour at the post-purchase phase.

Prior research indicates, that this positive relationship between perceived value and

loyalty has been found in many contexts - online banking (Yang & Peterson, 2004),

apparel shopping websites (Kim & Niehm, 2009) and in e-services, such as online

travelling and video on demand (Luarn & Lin, 2003).

In keeping with earlier research, Caruana and Ewing (2010) made an investigation in two

online stores – one for books and the other one for online share trade, respectively with

1165 and 692 usable replies and the results from the both studies indicated that the effect

of perceived value on online loyalty is significant. Such arguments are supported and by

the empirical findings of Chang and Wang (2011), who found as well that perceived

value has a direct effect on loyalty in an online shopping environment and that the higher

the level of perceived value, the higher the customer loyalty is.

53

However, according to Chang et al. (2009) little empirical research has been conducted in

regard to relationships between customer perceived value, satisfaction and loyalty in an

online environment, therefore, taking this into account and considering the

aforementioned arguments, the following hypothesis is proposed:

H8: An increase in perceived value will lead to increase in customer e-loyalty

towards a website.

3.4. Online Satisfaction

There has been considerable discussion in the literature regarding consumer satisfaction

and its influence on behavioural intentions.

One part of researchers argues that satisfaction is a crucial determinant of customer

loyalty (Lee & Overby, 2004; Lantieri, 2008; Cronin et al., 2000; Devaraj et al., 2002;

Anderson & Srinivasan, 2003; Taylor & Hunter, 2003), where Shankar et al. (2003)

added that the effect of satisfaction on loyalty is even stronger in an online environment

than in a traditional brick-and-mortar store. The possible reasons would be that

alternative offers are just a few clicks away and the cost of searching are low. According

to Cristobal (2007), the economic results of increasing consumer satisfaction are

demonstrated in the long term and have a direct effect on e-loyalty. Chiou and Shen

(2006), just as other authors (Collier & Bienstock, 2006; Wolfinbarger & Gilly, 2003),

supported this argument by explaining that satisfied customers are more likely to revisit

the website, reuse or repurchase the same product/service in the future, recommend it to

54

others and to resist offers from competitors. Some scholars state that customers are loyal,

because they are satisfied and, therefore, they want to continue the relationship (Cai &

Xu, 2006; Andreassen & Lindenstad, 1998; Fornell et al., 1996). Devaraj et al. (2002)

built on that by stating that customer satisfaction eventually leads to customer loyalty.

In keeping with earlier research, Kim et al. (2009) investigated the satisfaction-loyalty

link between 182 respondents in an online retail store and found that the e-satisfaction

has a positive effect on e-loyalty, i.e. the more the consumers are satisfied with a website,

the more loyal they will be to this website. Yang & Peterson (2004) and Taylor & Hunter

(2002) found as well in their studies that e-satisfaction is a strong determinant of e-

loyalty, having made the research accordingly in an online banking context and an online

B2B environment.

However, there are some researchers who have challenged this positive relationship and

counterarguments arise that customer satisfaction does not necessarily lead to loyalty

(Cao et al., 2004; Koivumaki, 2001; van Riel et al., 2001). According to Hellier et al.

(2003), the customer is influenced by a mixture of positive and negative bonds, where the

negative bonds (e.g. consumer inertia, brand promotion, customer information processing

limitations, supplier monopoly) tie the customer to the vendor, even though customer

satisfaction with the company may not be particularly high. Shankar et al. (2003) built on

that by stating that it is possible for a customer to be loyal without being highly satisfied

(e.g., when there are few other choices) and to be highly satisfied and yet not be loyal

(e.g., when many alternatives are available).

55

As a result, the findings on the linkage between consumer satisfaction and loyalty are

unclear and more research is needed. This thesis will support the arguments of the

researchers who state that there is a positive relationship between e-satisfaction and e-

loyalty. Therefore, the following hypothesis is developed:

H9: E-Satisfaction will positively affect customer loyalty.

3.4.1. Mediation effect of Online Satisfaction

Recently, researchers were attracted to study the mediating role that consumer

satisfaction has on the effect of service quality on loyalty in an online environment (Yang

& Lin, 2006; Yang, 2007; Bei & Chiao, 2006). Online consumer loyalty is difficult and

costly to gain and, therefore, it requires a quality service that satisfies the consumer

(Cristobal, 2007). Weathers and Makienko (2006) stated that online service quality may

impact company profitability through customer’s satisfaction. In addition, Cronin, Brady,

and Hult (2000) could support empirically the indirect impact of service quality on

loyalty in six studies, conducted in different industries.

Further, Caruana (2002) made a research among bank users and the results revealed that

customer satisfaction does play a mediating role in the effect of service quality on

loyalty. At the same time, Yang and Tsai (2007) found that in an online environment e-

service quality also influences e-loyalty via customer e-satisfaction and, moreover, when

checking the loadings of its dimensions, the authors found that the significance of all of

them is above 0.75, indicating that all dimensions of e-service quality are important

56

factors influencing online satisfaction and loyalty. Gronroos et al. (2000) stated that e-

service quality can retain customers using levels of customer e-satisfaction and Yoon &

Uysal (2005) suggested that examining the mediating role of e-satisfaction should be

accepted as a primary dimension, in order to evaluate the performance of services and

products.

On the basis of the preceding, the following hypothesis is proposed:

H10: Online satisfaction will mediate the effect of e-service quality on e-loyalty.

Furthermore, earlier in this thesis it was hypothesised that an increase in e-trust will lead

directly to an increase in e-loyalty. Nevertheless, a review of existing literature in a

service marketing context suggests that together with the argument that the relationship

between e-trust and e-loyalty is direct, e-trust as well influences e-loyalty indirectly

through e-satisfaction. Haris and Gooede (2004) performed two studies, namely in online

stores for books and for flight purchasing, founding that e-trust plays a pivotal role in

service dynamics and, in particular, in indirectly driving e-loyalty through e-satisfaction.

Kim et al. (2009) found as well in their study among online shoppers that satisfaction

mediates the effect of e-trust on e-loyalty. Similarly, Jin and Park (2006) suggested that

the online vendors have to gain consumer trust first, so it can be transferred to

satisfaction and eventually to loyalty, in order to attract customers to online store. In the

same direction, Anderson and Srinivasan (2003) pointed out that e-satisfaction is likely to

result in stronger e-loyalty when customers have a higher level of trust in the e-business.

On the basis of the preceding, the following hypothesis is proposed:

57

H11: Online satisfaction will mediate the effect of e-trust on e-loyalty.

Finally, as it has been proposed earlier in this thesis, perceived value has direct effect on

customers’ satisfaction and loyalty. However, researchers have argued that perceived

value impacts loyalty as well indirectly through its relationship with consumer’s

satisfaction. In their research, Lam et al. (2004) examined the mediating role of customer

satisfaction in the impact of customer value on customer loyalty in B2B service setting.

The authors (Lam et al., 2004) have extended prior research by incorporating the

attitudinal framework cognition -> affect -> behavioural intent or behaviour, by

regarding perceived value as cognition variable, customer satisfaction as an affect

variable and customer loyalty as behaviour (or a disposition to behave favourably toward

a service provider). The results of the study revealed that perceived value is a direct

antecedent of customer’s satisfaction and that perceived value influences indirectly

loyalty through satisfaction (Lam et al., 2004).

Furthermore, the indirect effect of perceived value on loyalty has been investigated in

business marketing content; in services, such as dentist, auto service, restaurant, and

hairstylist; in online banking services; in online retail stores and in online service context

(Eggert & Ulaga, 2002; McDougall & Levesque, 2000; Wang & Xu, 2008; Yang &

Peterson, 2004; Chang & Wang, 2011). The findings from all of the studies indicated that

perceived value leads to satisfaction which, in turn, leads to positive behavioural

intentions, revealing the mediating role of satisfaction in the perceived value-loyalty

relationship. Nevertheless, there are some researchers who have found only partial

mediating role of satisfaction on the effect of perceived value on e-loyalty (Cronin et al.,

58

2000; Caruana & Fenech, 2005). As a result, Gill et al. (2007) suggested that by testing

satisfaction as a mediator will provide an enhanced understanding of the impact of

perceived value on behavioural intentions.

On the basis of the preceding, the following hypothesis is proposed:

H12: Online satisfaction will mediate the effect of perceived value on online

loyalty.

3.5. Theoretical Framework & Summary of Hypotheses

The theoretical framework guiding this study is presented below in Figure 1, illustrating

the direct and indirect (punctuated lines) links between the variables. In addition, a

summary of the Hypotheses proposed earlier, can be seen in Table 8.

Figure 1: Theoretical Framework

Table 9: Summary of proposed Hypotheses

59

Hypotheses

H1: An increase in e-service quality leads to an increase in the e-satisfaction with a

website.

H2: An increase in e-service quality leads to an increase in e-trust towards a website.

H3: An increase in e-service quality leads to an increase in perceived value towards a

website.

H4: An increase in e-service quality leads to an increase in e-loyalty towards a website.

H5: An increase in e-trust leads to an increase in e-satisfaction with a website.

H6: An increase in e-trust leads to an increase in e-loyalty towards a website.

H7: An increase in perceived value leads to an increase in e-satisfaction with a website.

H8: An increase in perceived value leads to an increase in e-loyalty towards a website.

H9: An increase in e-satisfaction leads to an increase in e-loyalty towards a website.

H12: E-satisfaction will mediate the effect of e-service quality on e-loyalty.

H11: E-satisfaction will mediate the effect of trust on e-loyalty.

H10: E-satisfaction will mediate the effect of perceived value on e-loyalty.

4. METHODOLOGY

60

This chapter will present how the research has been carried out. Discussion about survey

design and its distribution is included, followed by explanation in regard to the

questionnaire and its content validity in order to collect the data.

4.1. Survey Design

The data, needed to test the hypotheses in this thesis, was collected through a survey,

which was designed using web based software, specialised in conducting online surveys

(www.qualtrics.com). This method allowed the data to be gathered in an easy, quick,

costless and accurate manner. For a period of 10 days the survey was online on

www.qualtrics.com and it was distributed by posting a hyperlink (URL) in two Facebook

accounts, which together had more than 500 people listed as their friend, increasing the

chance of collecting more and useful data. Invitation messages to participate were sent.

The respondents were kindly asked to click on the link and then automatically were

transferred to the survey page, where they had to click on an answer of their choice and

after a completion of the survey; it was stored on a server. The data files could be

accessed at any time and downloaded to a computer in Microsoft Excel format, SPSS file

(.sav format) or HTML file.

4.2. Questionnaire Design

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The Questionnaire (see Appendix II) used for this thesis was self-administered. In order

to gather valuable data, there was a condition in place in order to participate in the

questionnaire, namely the responded had to have an experience with a website for hotel

reservations. Therefore, a so cold “skip logic question” was included in the first page. If

the answer to the question “Have you ever reserved a hotel using the Internet?” was

“No”, the survey came to its end, differentiating in this way the respondents with online

reservation experience, being valuable for obtaining data; from the ones without any

experience. In case the participants clicked on “Yes”, the survey continued to the

introduction part. Further, on the next page the respondents were kindly asked before

proceeding to the survey questions, to think about the last time they have made a

reservation for a hotel through the Internet and, if possible, to indicate the name of the

website. The purpose of this was to fix their thoughts only to one website in order to give

valuable answers and as well, for informative purpose for the researcher. In addition, it

was chosen that the participants have to evaluate websites for hotel reservation, because

it is one of the most popular products, bought online (Nielsen Research, 2008) and

therefore ensuring that more data would be gathered.

The next five parts of the questionnaire consisted 33 items, used to measure the variables

included in the theoretical framework. Based on the respondents’ past experience with

the website for hotel reservations they had in mind, they were requested to indicate the

level of agreement with the statements, using 5-point Likert-type scale which ranges

from (1) strongly disagree to (5) strongly agree.

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Further, to ensure the content validity of the scales, the items selected must represent the

concept about which generalizations are to be made (Luarn & Lin, 2003). Therefore, a

review on the prior research is conducted in order to guarantee the content validity.

The part for online loyalty consisted of four items, which were adapted from Anderson &

Srinivasan (2003) and Ribbink et al. (2004), capturing both attitudinal and behavioural

aspects. Online satisfaction, consisting four items, was assessed by adapting the scale

developed by Kim et al. (2009); Collier & Bienstock (2006) and Chang et al. (2009). In

the part measuring perceived value, four items were based on studies from Harris &

Goode (2004); Chang et al. (2009) and Parasuraman et al. (2005). Further, five items

were used to measures online trust and they were determined by scales used by Anderson

& Srinivasan (2003); Wolfinbarger & Gilly (2003) and Flavian et al. (2006). The final

part measuring e-service quality consisted of fifteen items, which covered the four

dimensions Fulfilment, and Privacy (both adapted from Wolfinbarger & Gilly, 2003;

Park et al., 2007) and System Availability and Efficiency (both adapted from Parasuraman

et al., 2005).

In the final section of the questionnaire, four items regarding the demographic

characteristic of the respondents were asked, such as gender, age, income and education.

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5. ANALYSES & RESULTS

This Chapter reports the results of the conducted survey. First, the demographic

characteristics of the sample are presented. Second, reliability analysis, including factor

analysis, is performed. Then, the conducted regression analyses are described, followed

by explanation of the results. Finally, a summary of the tested hypotheses is included.

5.1. Descriptive Statistics

From the survey were collected 223 responses. From the characteristics of the sample

(see Appendix I), it can be concluded that the sample is not balanced in terms of gender -

80,4% of the respondents were male and 19,6% of them were female. Almost half of the

respondents have a college degree education; 40% have completed high school and most

of the rest have a university degree or another type of education. Further, around 40% of

the respondents have a monthly net income of less than 1000 €, one in three between

1000 and 1999 € and the rest have a net monthly income higher than 2000 €. In addition,

the respondents have an average age of 28 years old, varying from 18 to 62 years old (SD

10.2). Finally, the most popular website where the respondents have made their online

reservations is www.booking.com.

5.2. Scales and reliability

In order to calculate the reliability Cronbach’s Alpha method is applied. According to

Nunnally (1978) Cronbach’s Alpha of 0.7 and above is acceptable. As a result the

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reliability analysis shows that all scales used in this research are reliable; all Cronbach’s

alpha’s are higher than 0.70. Key statistics of the scales (including correlations between

scales) are given in Table 10 below. More detailed tables of the reliability analysis of

scales can be found in Appendix I.

Table 10: Details of the scales used in the model

Alpha

Mean

SD E-trust E-service

Perceived value

E-satisfaction E-loyalty

E-trust 0.83 3.89 0.66 1 0.80 0.63 0.63 0.49E-service 0.95 3.99 0.61 0.80 1 0.63 0.64 0.49Perceived value

0.84 3.81 0.64 0.63 0.63 1 0.71 0.55

E-satisfaction 0.83 3.88 0.59 0.63 0.64 0.71 1 0.65E-loyalty 0.87 3.66 0.65 0.49 0.49 0.55 0.65 1

5.2.1. Factor Analysis

For the purpose of this thesis the E-S-QUAL scale, developed by Parasuraman et al.

(2005), has been chosen as a measurement tool in order to assess e-service quality. As it

has been already mentioned, it can be theoretically divided into four subscales –

Efficiency, Fulfilment, System availability and Privacy (in total 15 items). The reliability

analysis indicated a strong internal consistency of this 15-item scale (alpha=.95). In

addition, factor analysis was executed in order to confirm this underlying pattern.

Further, Principal Component Analysis (PCA) was used; being a commonly used

technique to reduce a complex data set to a lower dimension to reveal the sometimes

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hidden, simplified structure that often underlies it. The first principal component

accounts for as much of the variability in the data as possible, and each succeeding

component accounts for as much of the remaining variability as possible. The selected

rotation method is oblimin, because the subscales are likely to be highly correlated.

Further, Kaiser-Meyer-Olkin statistic (KMO) and Bartlett’s Test were performed, in

order to assess whether there appears to be some underlying (latent) structure in the data.

This is also referred to as Sampling Adequacy, or lack of Sphericity. The KMO statistic

varies between 0 and 1 and a value close to 1 indicates that patterns of correlations are

relatively compact and so factor analysis should yield distinct and reliable factors (Field,

2005). The bare minimum for KMO should be .5 or greater (Kaizer, 1974) and Bartlett’s

Test should be significant (p<.05). In this study the KMO value is .927, which according

to Kaizer’s recommendations is a superb value, and Bartlett’s Test is highly significant

(p<.001) (Table 11). These assumptions are both not violated; therefore it is appropriate

to perform factor analysis.

Table 11: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .927

Bartlett's Test of Sphericity Approx. Chi-Square 2429.872

df 105

Sig. .000

In addition, there are a few options to decide how many components should be extracted:

(1) to use all components with an eigenvalue > 1; (2) to use all components left of the

elbow in the scree plot, or (3) to choose the number based on the theoretical viewpoint.

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Because four subscales are assumed, the number of factors to extract is four. The scree

plot (Figure 2) shows one factor on the left of the elbow, which confirms that the 15

items can be summarized into one quality scale.

Figure 2: Scree Plot

Table 12 below demonstrates, too, that the first factor is the most important in terms of

the percentage variance explained (57%). The second factor barely exceeds the

eigenvalue > 1 criterion and the eigenvalues of factors 3 and 4 are even lower.

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Table 12: Total Variance Explained

ComponentInitial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadingsa

Total% of

VarianceCumulative

% Total% of

VarianceCumulative

% Total

dimension0

1 8.602 57.345 57.345 8.602 57.345 57.345 5.8162 1.153 7.688 65.034 1.153 7.688 65.034 5.9113 .949 6.329 71.363 .949 6.329 71.363 5.4104 .768 5.117 76.480 .768 5.117 76.480 5.8775 .568 3.785 80.265

6 .459 3.057 83.322

7 .402 2.679 86.000

8 .388 2.588 88.589

9 .357 2.380 90.969

10 .321 2.143 93.112

11 .281 1.873 94.984

12 .229 1.526 96.511

13 .220 1.467 97.978

14 .179 1.191 99.169

15 .125 .831 100.000

Extraction Method: Principal Component Analysis.a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.

In order to find out whether the four subscales are indeed the underlying structure of e-

service quality, the pattern matrix is used. In Table 13 below can be seen that the highest

loadings per factor correspond with the subscales q9=Fulfilment, q10=Privacy,

q11=System Availability and q12=Efficiency. Because of the negative factor loadings of

the items on factors 2 and 4, the factor names can be best chosen as the opposite of the

scale (for example factor 2 = ‘lack of efficiency’ and for factor 4 = ‘lack of system

availability’).

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Table 13: Pattern Matrix(a)

Component

1 2 3 4

q9_1a .763 .150 .175 -.100

q9_2a .787 .001 .088 -.105

q9_3a .756 -.348 -.124 .112

q9_4a .578 .029 .170 -.244

q10_1a .123 -.055 .777 -.036

q10_2a -.044 -.168 .718 -.067

q10_3a .050 .035 .902 .021

q11_1a .194 -.213 .171 -.486

q11_2a -.008 -.087 .000 -.904

q11_3a .054 -.005 .019 -.899

q12_1a .034 -.725 .065 -.172

q12_2a .027 -.831 .019 -.007

q12_3a -.027 -.691 .320 .054

q12_4a .221 -.624 .053 -.105

q12_5a .051 -.648 -.036 -.316

Extraction Method: Principal Component Analysis.

Rotation Method: Oblimin with Kaiser Normalization.

a. Rotation converged in 11 iterations.

The correlations between the four components are shown below in Table 14. The

negative signs are explained by the ‘opposite of the scale’ interpretation (see above).

Table 14: Component Correlation Matrix

Component 1 2 3 4

dimension0

1 1.000 -.489 .500 -.556

2 -.489 1.000 -.454 .502

3 .500 -.454 1.000 -.523

4 -.556 .502 -.523 1.000

Extraction Method: Principal Component Analysis.

Rotation Method: Oblimin with Kaiser Normalization.

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For the purpose of this study the four dimensions fulfilment, privacy, system availability

and efficiency have been integrated into one concept of e-service quality.

5.3. Regression Analysis

The analysis chosen to test the hypotheses is the regression analysis, which allows fitting

a predictive model to a dataset and then uses the model to predict values of the dependent

variable from one or more independent variables (Field, 2005). The theoretical

framework of the study is tested by means of a series of simple linear regression

analyses.

The model was first tested for multivariate outliers. A multivariate outlier is a case with

an unusual combination of scores for a number of variables, which differs substantially

from the rest of the data for some reason (Schinka & Velicer, 2004). For this reason,

Cook’s distances were saved to detect influential multivariate outliers, where Cook’s

distance (CD’s) is a measure of the overall influence of a case on the model and values

greater than 1 might be a reason for concern (Field, 2005). Only one respondent had

Cook’s distances >1 (in the 1st, 2nd and 5th regression analysis) and was therefore

excluded from the analysis. Appendix I includes CD’s for the outlier in the 5 regression

analyses.

5.3.1. Regression Analysis for e-Loyalty

The first regression analysis aims to test how the three predictor variables influence the

outcome variable. In particular, it will be tested whether there is a relationship between

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the independent variables e-service quality, e-trust and e-perceived value and the

dependent variable e-loyalty, testing H4, H6 and H8 respectively.

From the model summary (see Appendix I) can be seen that the three predictors – e-

service quality, e-perceived value and e-trust, account for 38% (R2=.380) of the

variability in loyalty, F (3,214) = 43.7, p<.001.

Table 15 below shows that all three independent variables – e-service quality, e-

perceived value and e-trust, are significant predictors of e-loyalty. The B-weight for e-

perceived value is (.20, p<.05); for e-trust (.23, p<.05) and for e-service quality (.29,

p<.01). Therefore, hypotheses H4, H6 and H8 were supported.

Table 15: Coefficients (a) Independent variables vs. e-Loyalty

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .826 .251 3.284 .001

e-Perceived Value .201 .084 .195 2.380 .018

e-Trust .229 .094 .222 2.424 .016

e-Service Quality .291 .106 .259 2.755 .006

a. Dependent Variable: e-Loyalty

5.3.2. Regression Analysis for e-Satisfaction

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The second regression analysis aims to test whether there is a relationship between the

predictors and the mediator. In other words, it is tested whether the independent variables

e-service quality; e-trust and e-perceived value are significant predictors of the outcome

variable e-satisfaction, testing respectively H1, H5 and H7.

In the model summary (see Appendix I) can be seen that the independent variables

account for 62% (R2=.615) of the variability in the mediator (e-satisfaction),

F(3,214)=114.2, p<.001. The significance of the F-Statistic=0 and the R2=0.615 indicate

a strong positive relationship in the correlation between the observed values of the

dependent variables and indicate a relatively good proportion of variation in the

dependent variable explained by the model.

Table 16 below indicates that all three independent variables e-service quality, e-trust and

e-perceived value have a significant effect on e-satisfaction. The B-weights are (.27,

p<.001) for perceived value, for e-trust (.22, p<.01), and for e-service quality (.35,

p<.001). Therefore, the three tested hypotheses H1, H5 and H7 are supported.

Table 16: Coefficients(a) mediator (e-Satisfaction) as dependent variable

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .607 .180 3.377 .001

e-Perceived value .265 .060 .284 4.393 .000

e-Trust .219 .067 .235 3.252 .001

e-Service Quality .350 .076 .343 4.631 .000

a. Dependent Variable: e-Satisfaction

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5.3.3. Regression Analysis for e-Loyalty including a Mediator

It was hypothesised earlier in this thesis that an independent variable influences the

mediator and the mediator influences the depended one. In order to support of reject the

hypotheses H10, H11 and H12, all variables are inserted into the model to test whether

there is an effect of the mediator (e-satisfaction) on the relationship between the

independent variables e-service quality, e-trust and e-perceive value and the dependent

variable e-loyalty. In case there is an effect of the mediator, the significance of the

independent variables should decline.

In Table 17 below can be seen that in the first step where only the independent variables

e-service quality, e-trust and e-perceive value are entered, the model explains 38%

(R2=.380) of the variability’s of e-loyalty, F(3,214)=43.7, p<.001. However, in the

second step of the regression analysis, where all the independent variables (e-service

quality, e-trust and e-perceive value) and the mediator (e-satisfaction) are entered, it can

be seen that 44% (R2=.440) of the variability of e-loyalty is explained by the model,

therefore e-satisfaction has a significant added value in predicting e-loyalty, Fch

(1,213)=23.0, p<.001.

Table 17: Model Summary(c) all predictors including mediator inserted

ModelR

R Square

Adjusted R Square Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1 df2

Sig. F Change

1.616a .380 .371 .51379 .380 43.704 3 214 .000

2.664b .440 .430 .48926 .060 23.000 1 213 .000

a. Predictors: (Constant), e- Service Quality, e-Perceived Value, e-Trust

b. Predictors: (Constant), e-Service Quality, e-Perceived Value, e-Trust, e-Satisfaction

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ModelR

R Square

Adjusted R Square Std. Error of the Estimate

Change Statistics

R Square Change

F Change

df1 df2

Sig. F Change

1.616a .380 .371 .51379 .380 43.704 3 214 .000

2.664b .440 .430 .48926 .060 23.000 1 213 .000

a. Predictors: (Constant), e- Service Quality, e-Perceived Value, e-Trust

c. Dependent Variable: e-Loyalty

In Table 18 below can be seen that e-satisfaction is a significant predictor of e-loyalty

with a positive B-coefficient (.44, p<.001). A positive B-coefficient means that an

increase in the independent variable leads to an increase in the dependent variable.

Further, in Table 16 and Table 18 can be seen that there is indeed a mediator effect of e-

satisfaction between all independent variables and e-loyalty. The variables e-service

quality, e-trust and e-perceived value, change from significant (p<0.05) predictors (see

Table 16) to insignificant predictors of e-loyalty once e-satisfaction is entered (Table 18).

Therefore H10, H11 and H12 are supported.

Table 18: Coefficients(a) all predictors including mediator inserted

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Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .561 .246 2.282 .023

e-Perceived value .085 .084 .083 1.014 .312

e-Trust .133 .092 .129 1.444 .150

e-Service Quality .139 .106 .123 1.311 .191

e-Satisfaction .437 .091 .396 4.796 .000

a. Dependent Variable: e-Loyalty

5.3.4. Regression Analysis for e-Trust

In addition to the regression analyses presented above, additional analysis is conducted in

order to test the relationship between e-service quality and e-trust, thus testing H2.

Therefore, e-trust was the dependent variable and e-service quality was the independent

one. E-service quality is a significant positive predictor of e-trust and explains 62%

(R2=.616) of the variability in e-trust, F(1,216) =346.9, p<.001 (see Appendix I). In Table

19 below can be seen that the B-weight of e-service quality is positive (.86), therefore an

increase in e-service quality is associated with an increase in e-trust. Therefore, H2 is

supported.

Table 19: Coefficients e-Service quality vs. e-Trust

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Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .465 .186 2.495 .013

e-Service Quality .857 .046 .785 18.626 .000

a. Dependent Variable: e-Trust

5.3.5. Regression Analysis for e-Perceived Value

The last fifth regression analysis analyzes the relationship between e-service quality and

e-perceived value, hence testing H3. Here e-perceived value is the dependent variable

and e-service quality the independent one. E-service quality is a significant positive

predictor of e-perceived value and accounts for 52% (R2=.522) of the variability in e-

perceived value, F(1,216)=235.6, p<.001 (see Appendix I). In Table 20 below can be

seen that the B-weight is positive (.79), therefore an increase in e-service quality is

associated with an increase in e-perceived value. Therefore, H3 is supported.

Table 20: Coefficients(a) e-Service quality vs. e-Perceived value

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .630 .209 3.021 .003

e-Service Quality .791 .052 .722 15.348 .000

a. Dependent Variable: e-Perceived Value

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5.4. Further Analyses

Based on the statistical research above, there were additional interesting findings to be

gained, possibly contributing to the explanation of the relationships between the studied

variables. In this study it has been found already that e-service quality influences directly

e-trust and e-perceived value, which in turn, affect e-satisfaction. Therefore, it is likely

that e-perceived value and e-trust (partially) mediate the effect of e-service quality on e-

satisfaction. In order to investigate this (partially) mediated effect, additional regression

has been conducted.

5.4.1. Regression Analysis for the effect of e-Service quality on e-Satisfaction

It has been seen that e-service quality, e-perceived value and e-trust all have their own

significant effect on e-satisfaction (see Table 16). Further, if the effect of e-service

quality on e-satisfaction is mediated by e-perceived value and e-trust, then the B-weight

of e-service quality should be higher without e-perceived value and e-trust, so higher

than .35 (Table 16).

It is demonstrated below in Table 21, that the B-weight for e-service quality alone is .75.

This is much higher than .35, so it is plausible that the effect of e-service quality on e-

satisfaction is partially mediated by e-perceived value and e-trust.

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Table 21: Coefficients(a) e-Service quality vs. e-Satisfaction

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) .875 .191 4.582 .000

e-Service Quality .747 .047 .733 15.841 .000

a. Dependent Variable: e-Satisfaction

5.5. Summary of Hypotheses & Results

The results of the analyses were used to answer the hypotheses formulated for this

research. Below can be seen the summary of the hypotheses and the results.

Table 22: Summary of Hypotheses & Results

Hypotheses Result

H1: An increase in e-service quality leads to an increase in the e-satisfaction with a website.

Table 16 shows a significant positive B-coefficient for e-service quality (.35, p<.001). Therefore, an increase in e-service quality will lead to an increase in e-satisfaction.

Supported

H2: An increase in e-service quality leads to an increase in e-trust towards a website.

Table 19 shows that variable e-service quality is significant at the p<0.001 level (sig. 0.000) with the coefficient B being .86, indicating that e-service quality is a significant positive predictor of e-trust. Therefore, an increase in e-service quality will lead to an increase in e-trust.

Supported

H3: An increase in e-service quality leads to an increase in e-perceived value towards a website.

Table 20 shows that e-service quality is a significant positive predictor of e-perceived value (.79, p<.001). Therefore, an increase in e-service quality will lead to an increase in e-perceived value.

Supported

H4: An increase in e-service quality leads to an increase in e-loyalty towards a website.

Supported

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Table 15 shows that the variable e-service quality has a B-coefficient of .29 and was significant at the p<0.01 level with a significance of 0.006. Therefore an increase in e-service quality will lead to an increase in e-loyalty.

H5: An increase in e-trust leads to an increase in e-satisfaction with a website.

Table 16 shows a significant positive B-coefficient for e-trust (.22, p<.01). Therefore an increase in e-trust will lead to an increase in e-satisfaction.

Supported

H6: An increase in e-trust leads to an increase in e-loyalty towards a website.

In Table 15 can be seen that the variable e-trust has a B-weight of .23 and is significant at the p<.05 level with a significance of 0.016. Therefore an increase in e-trust will lead to an increase in e-loyalty.

Supported

H7: An increase in perceived value leads to an increase in e-satisfaction with a website.

In Table 16 can be seen that perceived value is a significant predictor of e-satisfaction with a positive B-coefficient (.27, p<.001). Therefore an increase in e-perceived value will lead to an increase in e-satisfaction.

Supported

H8: An increase in perceived value leads to an increase in e-loyalty towards a website.

Table 15 shows a significant positive B-coefficient for e-perceived value (.20, p<.05). Therefore an increase in e-perceived value will lead to an increase in e-loyalty.

Supported

H9: An increase in e-satisfaction leads to an increase in e-loyalty towards a website.

In Table 18 can be seen that e-satisfaction is a significant predictor of e-loyalty with a positive B=.44 and a being significant at the p<.001. Therefore an increase in e-satisfaction leads to an increase in e-loyalty.

Supported

H10: E-satisfaction will mediate the effect of e-service quality on e-loyalty

It can be seen that the significant B-weight of e-service quality (.29, p<.01) (Table 15) changes to insignificant (.14, p>.05) when e-satisfaction is added to the model (Table 18). Therefore e-satisfaction will mediate the effect of e-service quality on e-loyalty.

Supported

H11: E-satisfaction will mediate the effect of trust on e-loyalty.

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It can be seen that the significant B-weight of trust (.23, p<.05) (Table 15) changes to insignificant (.13, p>.05) when e-satisfaction is added to the model (Table 18). Therefore e-satisfaction will mediate the effect of e-trust on e-loyalty.

Supported

H12: E-satisfaction will mediate the effect of e-perceived value on e-loyalty.

The significant B-weight of e-perceived value (.20, p<.05) (Table 15) changes to insignificant (.09, p>.05) when e-satisfaction is added to the model (Table 18). Therefore e-satisfaction will mediate the effect of e-perceived value on e-loyalty.

Supported

6. CONCLUSION

In this Chapter the conclusions regarding the hypotheses will be presented, followed by

the implications for managers. Finally, the limitations of the study will be discussed and

recommendations for future research will be suggested.

6.1. Conclusion

The objective of this thesis was to increase the understanding of the relationship between

customer satisfaction, service quality, trust and perceived value in an online environment

and their influence on online loyalty.

The results provided support for the theoretical model of this thesis. In particular, the

study identified that satisfaction, trust; service quality and perceived value are all factors

having direct influence on customer loyalty in an online environment. It was found,

however, that satisfaction is the strongest predictor of customer e-loyalty. It can be

concluded that the more satisfied are the customers with an e-vendor, the more loyal they

will be to the same e-vendor. The results are matching the findings of Kim et al. (2009)

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who, based on their study, concluded that e-satisfaction affects e-loyalty of the e-

consumer.

Further, the hypothesis proposing that perceived value is directly affecting loyalty was

supported, but much less than e-satisfaction, which may imply that perceived value is not

the major contributor to loyalty in an online environment. However, the results revealed

that perceived value has not only a direct impact on loyalty, but also indirect one through

e-satisfaction, which revealed the mediating role of e-satisfaction in the perceived value-

loyalty relationship. This finding confirmed that although customer perceived value is

not the most influential factor of e-loyalty, it has a critical role in explaining customer

loyalty in an online setting (Chang & Wang, 2011).

The findings of the study supported and the proposition that e-trust has a direct influence

on e-loyalty. This can be explained with the role trust has in reducing the perceived

uncertainty and risk in online transactions and once the e-consumers find an e-store

trustworthy, they will be willing to (re)engage in trading relationship with the vendor.

The results are in agreement with previous research, conducted by Lantieri (2008) and

Luarn & Lin (2003), who found that e-trust has a strong positive influence on e-loyalty.

Moreover, the hypothesis proposing that e-satisfaction mediates the effect of e-trust on e-

loyalty was supported. Hence, e-trust will influences e-loyalty indirectly through e-

satisfaction and this relationship emphasises the essential role which trust plays in

determining loyalty in an online context.

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Another factor, found to be affecting loyalty directly is e-service quality. In particular,

the hypothesis stating that an increase in e-service quality leads to an increase in e-

loyalty towards a website was supported. This is in line with the findings of Oliveira

(2007) and Huang (2008) who concluded that e-service quality has a positive relationship

with e-loyalty. A possible explanation is that online stores with superior service quality

can make the experience on the website more pleasant for the consumers and the

transactions easier and safer. As a result, a better service quality is something that all the

customers want and value and by providing it, the e-shoppers will be willing to return

and repurchase from the same e-vendors. What is more, the hypothesis suggesting that e-

satisfaction will mediate the effect of e-service quality on e-loyalty was supported as

well. This result is matching the finding of Caruana (2002) and Yang & Tsai (2007) who

contended that online service quality may impact loyalty through customer satisfaction.

Therefore, the effect of e-service quality on e-loyalty may be indirect as well. Having

both direct and indirect effects on loyalty, e-service quality is a very important factor

determining customer’s loyalty in an online setting.

Furthermore, considering that customer satisfaction is a crucial factor influencing

customer loyalty in an online context, it was important to find its possible determinants.

In particular, the results supported the premise that service quality, is an important

antecedent of customer satisfaction in an online environment. This is consistent with the

findings of Yang (2007) and Ribbink et al. (2004) who concluded that e-service quality

has positive strong effect on online satisfaction. This can be explained by the fact that the

website is the only interaction with the company and in order the customers to be

satisfied and eventually to return to the e-vendor, superior e-service quality is needed.

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The proposition that perceived value is a determinant of e-satisfaction was supported as

well. The customer perceived value determines how satisfied the online shoppers are

with an e-store. The consumer cannot be fully satisfied with the product or service

delivered, if they do not feel they got their “money’s worth”. These results are in line

with the study of Yang and Peterson (2004) who found that perceived value has a

significant and positive influence on e-satisfaction.

Further, the results obtained in the present research revealed that trust is a strong

predictor of customer satisfaction in a B2C e-commerce market. This finding supports

the study of Gummerus et al. (2004) who found that trust influences customer

satisfaction. A possible explanation of the results is that the online consumer will make a

purchase from an e-vendor only when they feel convinced and secure that the promised

service/product will be receive with reduced level of risk. If the e-shopper evaluates the

e-seller as a trustworthy, this will lead to a high customer satisfaction.

In addition, the results indicated that e-service quality is a significant positive predictor

of e-trust. As a result an increase in e-service quality will lead to an increase in e-trust.

When a high service quality is offered, customer’s uncertainty about the privacy and

safety of their personal information will be diminished, therefore the confidence the e-

shoppers have in the e-vendor will be affected positively. The findings of this thesis are

in line with the study of Ribbink et al. (2004) who found that service quality directly and

positively influences the customer’s trust in an online context.

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Furthermore, the hypothesis that e-service quality is a significant positive predictor of

perceived value was supported as well. Therefore, an increase in e-service quality will

lead to an increase in customer perceived value. This is in agreement with the findings of

Chang & Wang (2011) and Parasuraman & Grewal (2000) who concluded that e-service

quality is a factor with an important role in the development of e-perceived value. The e-

shoppers expect to experience higher service quality than in traditional stores, which will

result in efficient online shopping, and once they receive it, they will demonstrate higher

perceived value.

In addition, the extra analyses conducted in this thesis revealed that the effect of e-service

quality on e-satisfaction was partially mediated by e-perceived value and e-trust.

Consequently, e-service quality influences e-perceived value and e-trust, which, in turn,

affect e-satisfaction. This finding is in line with studies conducted by Gummerus et al.

(2004) and Yunus et al. (2009), who concluded as well that e-satisfaction can be

influenced and indirectly by e-service quality. This result strengthens even more the role

e-service quality has in establishing e-satisfaction, which in turn, impacts customer

loyalty towards a website. Therefore, having both direct and indirect effect on e-

satisfaction, e-service quality is an important factor to be considered in the B2C online

setting.

All the hypotheses proposed in this study were supported by the collected data. It can be

concluded that e-satisfaction, e-trust, e-perceived value and e-service quality are all

antecedent of online loyalty, affecting it directly or indirectly through customer e-

satisfaction. In addition, satisfaction appeared to be the strongest predictor of online

loyalty and is influenced positively by e-service quality, e-perceived value and e-trust.

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Therefore, by increasing consumers’ satisfaction and trust towards an e-vendor and by

offering high value and superior service quality, e-vendors can generate a loyal customer

base.

6.2. Managerial Implications

When consumers buy a product or service in an online store, they are affected by

different aspects which influence their willingness to come back and re-purchase. The

main influencing factors, revealed in this study, are satisfaction, e-trust, e-service quality

and customer e-perceived value, which directly or indirectly affect consumer’s loyalty

towards a web-store.

Service managers and practitioners should direct their efforts in increasing customer e-

satisfaction, which in this study appeared to be the most critical driver of customer

loyalty and, hence, major component for the success of an online company. If the level of

satisfaction with the experience in the web-store is high, the consumers will become

loyal to it. Therefore, it is of a great importance for e-managers to create satisfied

customers, as they are more likely to remain the relationship with a specific e-vendor by

purchasing repeatedly his products or services; to neglect competitors’ offers and to

increase the customer base of the company by spreading positive word-of mouth to their

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friends and family. It is important to note that e-satisfaction is directly influenced by

consumers’ perceived value, the service quality of the e-store and the consumers’ trust

towards the e-vendor. Therefore, enhancing each one of these factors will lead to

customer e-satisfaction and, in turn, to e-loyalty, which is the goal of every business unit.

In particular, it can be suggested that perceived value leads to e-satisfaction which, in

turn, leads to positive behavioural intentions. Once the customers perceive higher value

from the online retailer, they will feel immediate satisfaction with the purchase

experience and then they will be more loyal, hence, will return to repurchase. In addition,

perceived value can influence loyalty not only indirectly through customer satisfaction,

but as well directly, which confirms its strong role in achieving customer loyalty. The e-

tailers can generate a loyal customer base by offering superior store value in comparison

to the competition.

Managers have to understand that perceived value is all about consumers’ evaluations of

benefits and sacrifices (costs). In particular, from the moment of visiting the e-store to

purchasing a product/service, customer will analyse what they have given (time and

effort during navigation through the website) and what have they received (positive

experience in terms of easily searching and finding products/services, quick transaction,

order-confirmations and delivery-tracking, etc). Therefore, one way for service manager

to offer high perceived value is by increasing consumers’ economic, emotional, social or

relationship benefits obtained from products/services and websites, such as reducing the

price of goods or services; enhancing the performance or quality of the products/services

offered; offering specific services, etc. Another suggestion is by reducing consumers’

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sacrifices such as price, time, efforts, risk and inconvenience while searching and buying

products or services in an e-vendor. By applying these implications in real, managers

have the chance to offer the higher value perceived by the customers and as a

consequence to generate loyal customer base.

Furthermore, the results revealed that the higher the consumer’ trust towards a web-store,

the higher the customer satisfaction and through satisfaction it indirectly influences also

positively and loyalty. When the e-shoppers trust the e-seller, they will believe that the

interaction will be with positive outcome and this evaluation will positively influence the

degree of customer satisfaction. As a result, satisfied customers are more likely to

become loyal. In particular, when the e-vendors create a protected website with secure

transactions and, what is more, they communicate to their customers how they guarantee

the safety of their personal information, it is more likely to increase customers’

satisfaction, which in turn will influence positively their loyalty. Therefore, e-tailers can

use trust to enhance the satisfaction of the customers and consequently their loyalty.

The results indicated further, that loyalty is also influenced directly by trust, which means

that the greater the trust in an online vendor, the greater the consumer’s loyalty towards

him. Because of the lack of human interaction in an online store, e-consumers are

worried about the privacy of their personal and transactional details and a possible fraud;

about the delivery of the product/service ordered; return policies, etc. If the e-shoppers

perceived that the risk of buying from an e-tailer is high, they will not trust him and,

consequently, they will not (re)purchase from him. Therefore, a possible suggestion for

raising consumer’s trust is by improving company’s reputation as a result of thorough

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quality control. Furthermore, the service managers need to address all the risks that the

online consumer perceives while interacting with a website. This can be done by creating

an online store which is safe, reassuring and reliable. An implication for e-tailers is that

they have to provide adequate security features in the webstore, in order to protect

consumer’s personal information and credit card details. Service managers can achieve

this by implementing digital certificates, secure servers, placing the Company’s Privacy

Policy or the well-known VeriSign label on the website. Only when customers perceive

an online store as trustworthy, they will (re)purchase from the offered products or

services.

Finally, the study confirmed that e-satisfaction; e-perceived value, e-trust and e-loyalty

are all affected directly by e-service quality and, moreover, are largely defined by it. In

order to increase their level, the online service quality offered to the e-shopper has to be

excellent, placing it as an important factor for the success of the e-company. Since in the

Cyberspace it is very easy for the consumers to switch e-stores, higher service quality

offered by the e-vendor is needed, in order to differentiate themselves from the

competing businesses. Therefore, e-managers have to attract the e-shoppers with superior

service quality, which will lead to higher trust with the e-tailer, to an increase in the

degree of satisfaction and perceived value, and to customer e-loyalty.

Important implication for managers is that the website, which is the only interaction with

a company, has to be with a friendly interface; easy to use and to navigate through. This

is so, because if the consumer finds that it is hard to navigate through the website and it

takes too much time, they may abort the search and leave the e-store. Furthermore, the

88

product or service information, such as description, price, delivery, warranty, refund

conditions; has to be rich, updated, well organised and easily accessible. In addition, if

the assortment and variety of products/services offered in the online store is wide; the

chances that customers’ needs will be met are higher. Another suggestion is that the

delivery of the products/services has to be correct and fulfilled as promised. Finally, the

website has to be secure and safe for online transactions, since security is a one of the

main factors for evaluation in online purchasing. As a result, if the consumers are

satisfied with the purchase experience; if they perceived the website as useful and

valuable and if they trust the e-vendor, they will become loyal to it without seeking

alternative e-vendors.

Thus, the e-service managers need to implement e-service quality, e-trust, e-perceived

value and e-satisfaction as a central part of their strategy in order to instil loyalty in their

customer’s mind. What is more, being a major antecedent of e-loyalty, satisfaction has to

be enhanced by offering superior e-service quality, high e-perceived value and e-trust in

order to generate a loyal customer base.

6.3. Limitations & Suggestions for Future Research

There are several limitations in this thesis, which can be taken into consideration in

future research.

First, one of the limitations of this study is that the collected data was in regard to hotel

reservation websites and, therefore, the findings might not be suitable for making

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generalisation about different e-tailers due to specific industry characteristics. Future

research might consider examining more and different online industries. A comparison

between various e-tailers would be another recommendation for further research, in order

to deepen the understanding of consumer behaviour in different online environments.

Moreover, the product by nature was a hedonic one and, therefore, further research might

be conducted with regards to the different types of products, i.e. hedonic vs. utilitarian

goods.

Second, perceived value was assessed using uni-dimensional approach, after taking into

consideration the suggestions made by other researchers (Alves, 2011; Lin et al., 2005)

that the uni-dimensional method should be utilised when seeking to understand the

effects of perceived value in other constructs, such as satisfaction and loyalty. It would

be therefore interesting to conduct further research applying multi-dimensional measure,

which might yield different results. Further, this study employed the E-S-QUAL scale for

measuring e-service quality and a recommendation for further research is to apply other

scales of e-service quality with different dimensions, including web design, customer

service, entertainment, responsiveness.

Third, the study examined the relationships only among e-service quality, e-satisfaction,

e-trust, e-perceived value and e-loyalty. Possible future research might deepen the

knowledge of the online factors influencing customer loyalty, by extending the

theoretical framework and adding additional variables, such as switching costs,

commitment, brand, reputation, perceived risk.

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Fourth, the respondents of the survey had the freedom of choosing a website, which

might have biased the data. A suggestion for future research is to collect data from one or

two websites, because then the respondents would have more consistent experience.

Finally, the study was limited only to respondents coming from The Netherlands and that

is why the results cannot be generalised for other countries. Therefore, it might be

interesting to conduct future research investigating samples from different countries and

compare the consumer behaviour and decision making process in regard to loyalty across

different cultures.

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104

8. APPENDIX I

Sample Characteristics

105

Reliability of scale analyses e-Loyalty

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized

Items N of Items.865 .865 5

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deletedq5_1a 14.59 6.765 .729 .553 .825q5_2a 14.83 6.860 .718 .579 .828q5_3a 14.83 6.553 .726 .591 .827q5_4a 14.53 7.547 .641 .521 .848q5_5a 14.41 7.693 .623 .512 .852

Reliability of scale e-Satisfaction

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized

Items N of Items.834 .833 4

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deletedq6_1a 11.65 3.346 .650 .458 .795q6_2a 11.65 3.283 .680 .483 .782q6_3a 11.73 3.189 .698 .502 .773q6_4a 11.54 3.466 .623 .426 .807

Reliability of scale analyses e-Perceived value

106

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized

Items N of Items.836 .840 4

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deletedq7_1a 11.38 3.908 .689 .478 .783q7_2a 11.58 3.736 .589 .359 .833q7_3a 11.46 3.795 .713 .534 .771q7_4a 11.31 3.905 .690 .525 .782

Reliability of scale analyses e-Trust

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized

Items N of Items.825 .834 5

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deletedq8_1a 15.73 7.460 .441 .256 .850q8_2a 15.33 6.880 .755 .615 .753q8_3a 15.42 7.240 .684 .543 .774q8_4a 15.71 7.265 .575 .462 .804q8_5a 15.54 7.145 .705 .566 .768

Reliability of scale analyses e-Service quality

107

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based

on Standardized

Items N of Items.946 .946 15

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if

Item Deleted

Corrected Item-Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha if Item

Deletedq9_1a 55.70 73.834 .656 .600 .944q9_2a 55.76 72.973 .744 .702 .941q9_3a 55.95 73.402 .650 .533 .944q9_4a 55.76 72.937 .724 .603 .942q10_1a 56.07 73.279 .726 .664 .942q10_2a 56.11 73.854 .655 .517 .944q10_3a 56.05 73.897 .637 .585 .944q11_1a 55.78 73.046 .815 .708 .940q11_2a 55.74 72.801 .747 .779 .941q11_3a 55.79 73.522 .743 .764 .941q12_1a 56.04 71.980 .756 .690 .941q12_2a 56.03 73.761 .659 .614 .943q12_3a 56.04 74.118 .688 .576 .943q12_4a 56.00 72.674 .763 .664 .941q12_5a 55.91 73.135 .746 .668 .941

Cook’s distances case summaries

Cook’s Distance

1 2.05938

2 3.39517

3 .82876

4 .11130

5 3.03039

108

Regression Analysis for e-Loyalty

Model Summary(b) Independent variables vs. e-Loyalty

Mode

l R

R

Square

Adjusted R

Square Std. Error of the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .616a .380 .371 .51379 .380 43.704 3 214 .000

a. Predictors: (Constant), e-Service Quality, e-Perceived Value, e-Trust

b. Dependent Variable: e-Loyalty

Regression Analysis for e-Satisfaction

Model Summary(b) mediator (e-Satisfaction) as dependent variable

Model R

R

Square

Adjusted R

Square Std. Error of the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .785a .615 .610 .36717 .615 114.174 3 214 .000

a. Predictors: (Constant), e-Service Quality, e-Perceived Value, e-Trust

b. Dependent Variable: e-Satisfaction

Regression Analysis for e-Trust

Model Summary(b) e-Service quality vs e-Trust

109

Mode

l R

R

Square Adjusted R Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df

1 df2

Sig. F

Change

1 .785a .616 .615 .39091 .616 346.919 1 216 .000

a. Predictors: (Constant), e-Service Quality

b. Dependent Variable: e-Trust

Regression Analysis for e-Perceived Value

Model Summary(b) e-Service quality vs. e-Perceived value

Mode

l R

R

Square Adjusted R Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change

df

1 df2

Sig. F

Change

1 .722a .522 .519 .43775 .522 235.572 1 216 .000

a. Predictors: (Constant), e-Service Quality

b. Dependent Variable: e-Perceived Value

Regression Analysis for the effect of e-Service quality on e-Satisfaction

Model Summary(b) e-Service quality vs. e-Satisfaction

Model

R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

dimension0

1 .733a .537 .535 .40085 .537 250.924 1 216 .000

a. Predictors: (Constant), e-Service Quality

b. Dependent Variable: e-Satisfaction

110

9. APPENDIX II

Survey Questions

111

112

113

114