1

KATHOLIEKE UNIVERSITEIT LEUVEN UNIVERSITEIT GENT UNIVERSITEIT HASSELT

VRIJE UNIVERSITEIT BRUSSEL KATHOLIEKE HOGESCHOOL MECHELEN KATHOLIEKE

HOGESCHOOL BRUGGE-OOSTENDE ERASMUSHOGESCHOOL BRUSSEL HOGESCHOOL WEST-

VLAANDEREN XIOS HOGESCHOOL LIMBURG PLANTIJN HOGESCHOOL ANTWERPEN

Academic Year 2011-2012

Social Media and Customer

Loyalty in the Travel Trade:

A Relational Benefits Perspective

Sociale Media en Loyaliteit in de Reissector: een ‘Relational Benefits’ Perspectief

Master’s thesis submitted to obtain the

degree of

Promotor

Prof. Dr. Robert Govers Master of Science in Tourism

by: Astrid Senders

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Abstract

The aim of this study is to create an understanding of how social media affect customer

loyalty to tour operators, by investigating the complex online relationships they have with

their clients. The relational benefits approach was used to investigate several relational

benefits and their influence on customer loyalty from an online customer perspective. The

sampling frame includes customers having a relationship with tour operators on Facebook.

Structural Equation Modeling was used to analyze the data, a method which is able to test

complex theoretical models. Findings show that customer loyalty is only directly affected by

social and functional benefits. Indirect effects have been found of confidence and hedonic

benefits. Special treatment benefits showed no significant effects at all. The theoretical

contribution of this study is the application of CRM research in relation to the tourism

industry, in a rather new context of social media. The practical contribution is that the travel

trade gains insight in online factors that drive their customers to become loyal.

Key words: customer loyalty, social media, relational benefits approach

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In het kort: Het doel van dit onderzoek is het leren begrijpen van hoe sociale media

klantenbinding beïnvloed bij reisorganisaties, door de complexe online relaties te

onderzoeken tussen reisorganisaties en hun klanten. Hiervoor werd gebruik gemaakt van de

“relational benefits” benadering om verschillende relationele voordelen te onderzoeken en

het effect ervan op klantenbinding vanuit een online klantenperspectief. Het steekproefkader

bestond uit klanten met een relatie met reisorganisaties op Facebook. Om de verkregen data

te analyseren, is de methode “Structural Equation Modeling” gebruikt, een methode welke in

staat is complexe modellen te testen. Resultaten tonen aan dat klantenbinding alleen direct

beïnvloed wordt door “social” en “functional benefits”. Indirecte effecten zijn gevonden voor

“confidence” en “hedonic benefits”. “Special treatment benefits” bleken helemaal geen effect

te hebben. Dit onderzoek draagt bij aan de huidige literatuur, omdat CRM onderzoek nog

maar weinig is toegepast op het gebied van toerisme en ten tweede ook in een vrij nieuwe

context van sociale media. Het onderzoek is daarnaast ook van praktisch belang voor de

professionele sector, doordat het marketeers inzicht geeft in online factoren die een rol

spelen bij klantenbinding.

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Table of contents

List of figures ................................................................................................................. 6

List of tables .................................................................................................................. 7

List of appendices .......................................................................................................... 8

1. Introduction ................................................................................................................ 9

1.1 Research objective and question ...............................................................................12

1.2 Demarcation ..............................................................................................................13

1.3 Definitions ..................................................................................................................13

1.4 Structure of the thesis ................................................................................................14

2. Conceptual and Theoretical Foundation ..................................................................... 15

2.1 The concept of Customer Loyalty ...............................................................................15

2.2 The concept of Customer Satisfaction ........................................................................17

2.3 The Relational Benefits Approach ..............................................................................18

2.3.1 Social Benefits .....................................................................................................19

2.3.2 Confidence Benefits .............................................................................................19

2.3.3 Functional Benefits ..............................................................................................20

2.3.4 Special Treatment Benefits ..................................................................................20

2.3.5 Hedonic Benefits ..................................................................................................21

2.3.6 Application of the Relational Benefits Approach in the context of Social Media ....21

2.4 Relationship Commitment .........................................................................................22

2.5 Word of Mouth ..........................................................................................................23

3. The Construction of the Proposed Model .................................................................... 24

3.1 Consequences of Relational Benefits ........................................................................24

3.2 The influence of Customer Satisfaction and Relationship Commitment on Customer

Loyalty ......................................................................................................................26

3.3 The influence of Relationship Commitment and Customer Satisfaction on Word of

Mouth ........................................................................................................................29

3.4 A graphical illustration of the proposed conceptual model .........................................30

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4. Methodology ............................................................................................................ 31

4.1 Research design .......................................................................................................31

4.2 Data collection ..........................................................................................................31

4.2 Measurement ............................................................................................................33

4.3 Data analysis ............................................................................................................33

5. Results .................................................................................................................... 35

5.1 Checking assumptions ...............................................................................................36

5.2 Exploratory factor analysis .........................................................................................39

5.3 Confirmatory factor analysis .......................................................................................40

5.4 Structural Equation Modeling .....................................................................................43

5.5 Nested Structural Models ...........................................................................................44

5.6 Hypotheses testing ....................................................................................................47

6. Conclusion ............................................................................................................... 53

Limitations and Suggestions for Further Research .......................................................... 56

Theoretical and Managerial Implications......................................................................... 58

Acknowledgements ...................................................................................................... 60

References .................................................................................................................. 61

Appendices ................................................................................................................. 67

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List of figures

Figure 1: Evolution in active Facebook users (2004-2011) ...................................................11

Figure 2: Relative attitude behavior relationship ...................................................................16

Figure 3: The proposed conceptual model ............................................................................30

Figure 4: Final model ............................................................................................................47

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List of tables

Table 1: General sample details ...........................................................................................35

Table 2: Fit indices and their acceptable threshold levels .....................................................41

Table 3: Interpretation of BCC values ...................................................................................45

Table 4: Interpretation of BIC values ....................................................................................45

Table 5: Significant direct effects found by the final model ...................................................48

Table 6: Overview of consulted studies for the operationalization of constructs ....................67

Table 7: Studies consulted with respect to "Social Benefits" .................................................69

Table 8: Consulted studies with respect to "Confidence Benefits" ........................................70

Table 9: Consulted studies with respect to "Functional Benefits" ..........................................71

Table 10: Consulted studies with respect to "Special Treatment Benefits" ............................72

Table 11: Consulted studies with respect to "Hedonic Benefits" ...........................................73

Table 12: Consulted studies with respect to "Customer Satisfaction"....................................74

Table 13: Consulted studies with respect to "Customer Loyalty"...........................................75

Table 14: Consulted studies with respect to "Relationship Commitment" ..............................76

Table 15: Consulted studies with respect to "Word of Mouth" ...............................................76

Table 16: Measurement Items included in questionnaire (Likert scales 1-7) .........................77

Table 17: Regression weights in the measurement model .................................................. 121

Table 18: Significant paths found with the proposed model ................................................ 128

Table 19: Significant indirect effects found by the final model ............................................. 131

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List of appendices

Appendix 1: Measurement scales reviewed for operationalization of constructs…………….67

Appendix 2: Output used to check assumptions…………………………………………………81

Appendix 3: Output used during exploratory factor analysis……………………….………….100

Appendix 4: Output used during confirmatory factor analysis………………………………...121

Appendix 5: Output used during structural equation modeling……………………….……….128

Appendix 6: Output used during nested structural models testing ……………….………….129

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1. Introduction

The Internet has changed our daily lives completely. From a supplier’s point of view, the

Internet enables companies to attract new customers. For customers, the Internet created a

greater choice in products and services, value and pricing flexibility, due to access to new

and more products. This increased competition and therefore companies are challenged to

remain attractive to customers and to make them loyal to their brand (O’Reilly & Paper,

2009). Keeping existing customers by fostering customer loyalty is less expensive than to

acquire new customers; it takes high investments to obtain information about new customers

and to earn their trust (Conze et al., 2010; Hennig-Thurau, 2002; Gwinner et al. 1998;

O’Reilly & Paper, 2009). Long-term relationships with customers are essential for companies

operating in highly competitive environments. As service goods, tourism products are non-

transparent and therefore a chance exists that customers change suppliers. The non-

transparent aspect is due to the distance between the place of purchase and the place of

consumption (Conze et al., 2010). However, Yen & Gwinner (2003) pointed out that in the

literature the focus on the benefits of long-term relationships for companies is replaced by

the focus on the benefits for customers:

“Today it is crucial to know the desires of the customer and to understand what

services generate benefits for the customers” (Conze et al., 2010).

To remain competitive, companies work on their relationships with their customers which is

also well known as relationship marketing. Berry (1983) defined relationship marketing as

“attracting, maintaining and enhancing customer relationships”. Sheth (1996, cited in Hennig-

Thurau, 2002, p.231) states that customer loyalty is a primary goal of relationship marketing

and sometimes seen as equal to the concept of relationship marketing. According to Vogt

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(2011), travel and tourism organizations have been one of the early adopters of Customer

Relationship Management. Conze et al. (2010) confirm this by stating that the travel industry

was a pioneer to introduce loyalty programs like frequency flyer programs or hotel loyalty

cards.

Before, marketing practices were one way, but this has changed over the years with the

advent of social media. Social media is different from traditional media, since the

communication runs both ways instead of one-way. The two-way aspect makes it possible to

start conversations between multiple parties (Miller, 2011). Hanna et al. (2011) concluded

that social media must be used in addition to traditional media in their marketing activities.

But the emergence of Web 2.0 requires a different approach of marketers who try to connect

with their customers (Meadows-Klue, 2008). Meadows-Klue (2008) argues:

“Relationship marketing for the Facebook generation demands both thinking

and acting differently”.

Social media have gained popularity the last few years. Developments in information

technology have led to new possibilities for communication in the travel industry. Social

networking sites are increasingly used by online travelers who like to communicate with

others regarding travel information and by those who search for travel-related information

(Sung-Bum & Dae-Young, 2010). Therefore social media are becoming more and more

important in the online tourism domain (Xiang & Gretzel, 2011). Social media are used by

people sharing their experiences and opinions with others and consist of social networks

(e.g. Facebook, MySpace, Linkedin), blogs, micro blogs (e.g. Twitter), social bookmarking

and news services (e.g. NUjij), media sharing sites (e.g. YouTube) and virtual communities

(e.g. Second Life) (Miller, 2011). Social networking sites are, presumably, the most popular

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social media. Social networking site “Friendster” was the pioneer in social media as known

nowadays. In 2003, it introduced the concept of making friends online. Friendster was

popular at that time, but soon MySpace came along which became the most popular SNS in

2006. Other SNS were launched in 2003 and 2004, respectively Linkedin for business

purposes and Facebook, initially, for college students. Today, SNS are being used by all

kinds of people. Since Facebook allowed users of all ages, it became one of the most

popular SNS as well as for people as for marketing management. As figure 1 shows,

Facebook has over 800 million users that make active use of the social medium. According

to Zarella & Zarella (2011), half of them logs in each day. Beside a huge amount of users,

Facebook has the most general audience which makes it interesting for all kinds of

businesses. All the information resulting from 800 million profiles make Facebook a great

source for marketers. Therefore, social media marketing is being more and more applied

(Miller, 2011).

Figure 1: Evolution in active Facebook users (2004-2011)

Source: Facebook (2011)

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In professional journals regarding tourism like the Belgian Travel Magazine, it can be read

that, also in the travel trade, social media are more and more included in the marketing mix

(2011a). Social media are becoming more and more important and experts in the

professional sector are recommending the travel trade to actively anticipate to it (Travel

Magazine, 2011b; Travel Magazine, 2011c). The added value of the use of social media by

players in the travel trade is questioned. What is the impact of the use of social media by the

travel trade? Several benefits of social media for the travel trade are suggested, like

enthusing customers for certain destinations, creating brand awareness and creating

customer loyalty (Travel Magazine, 2011a). Kasavana et al. (2010) support this, saying that

social networking can assist in improving customer loyalty and satisfaction. To achieve goals

like these, the use of social media need to respond to customer’s needs (Hekkert, 2011).

This idea connects to the shift in the literature to focus more on the customer’s point of view.

The discussion in the professional sector about the impact of social media on customer

loyalty may solicit for a more rigorous approach and academic research into the

phenomenon.

1.1 Research objective and question

Based on the previous discussion, the objective of this research is to create an

understanding of how social media affect customer loyalty to tour operators, by investigating

the complex online relationships they have with their clients. To meet this objective, this

study aims to identify important drivers of customer loyalty in an online context. The focus

lies on customers and their relationships with tour operators through social media. From a

customer perspective, several relational benefits and their influence on customer loyalty are

investigated. Based on the research objective, the research question is as follows:

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In what way and to what extent do relational benefits of social media have an impact on

customer loyalty toward a specific tour operator?

1.2 Demarcation

As will be explained in further detail in chapter 4, customers of tour operators on Facebook

were approached to participate in an online survey in order to be able to answer the research

question. Despite of the fact that travel agents are also recommended to use social media,

only tour operators were included in this research because it is expected that online bookers

are more and more purchasing their holidays directly with tour operators. Furthermore,

Facebook was chosen as the sampling frame because its potential for marketing purposes is

being recognized more and more, plus it is relatively easy to gain access to tour operators’

customers on Facebook. The sampling frame was categorized by several different tour

operators having a Facebook page, Belgian as well as Dutch ones, attempting to guarantee

representative results for the tour operator industry.

1.3 Definitions

The most important concepts mentioned in the research objective and research question are

briefly explained below. A more detailed explanation will follow in the next chapter.

As stated earlier, customer loyalty is the primary goal of customer relationship management.

Customer loyalty can be defined as “an enduring desire to maintain a valued relationship”

(Hennig-Thurau et al., 2002). According to the relational benefits approach used in this study,

some sort of relational benefits for the customers must be created in order to make

customers value the relationship with a company, more specifically a tour operator, in the

long run. Relational benefits can be described as “those benefits customers receive from

long-term relationships above and beyond the core service performance” (Gwinner et al.,

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1998). Five types of relational benefits are incorporated in this research, namely social,

confidence, functional, special treatment and hedonic benefits.

1.4 Structure of the thesis

This thesis is further structured as follows. First, the theoretical foundation of the proposed

conceptual model is presented. Customer loyalty will be explained in more detail and more

attention is given to the relational benefits approach and the relational benefits included here.

Second, the construction of the proposed model will be outlined, which is based on existing

literature. Third, the methodology of this research will be described and fourth, the results are

given. Next, the research question will be answered in the conclusion. Sixth, the limitations of

this research are given including suggestions for future research. Finally, theoretical and

managerial implications are explained.

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2. Conceptual and Theoretical Foundation

This chapter presents the theoretical framework for the foundation of the proposed model

regarding social media.

2.1 The concept of Customer Loyalty

Relationship building with customers increases customer satisfaction and loyalty (Reynolds &

Beatty, 1999; Berry & Parasuraman, 1991; Czepiel, 1990). According to Hennig-Thurau et al.

(2002), customer loyalty is an important relationship marketing outcome. Keller (1993, cited

in Anderson & Srinivasan, 2003, p.125) defines loyalty as “a favorable attitude for a brand

manifested in repeat buying behavior”. This study incorporates loyalty toward a specific tour

operator as well as loyalty toward their online presence. Therefore, loyalty must be

distinguished from e-loyalty, defining e-loyalty as “a favorable attitude toward a given firm

operating online resulting in repeated use of the online relationship” (Anderson & Srinivan,

2003). Loyalty relationships between tourists and a service provider are often described by

trust, commitment and satisfaction and can be influenced on- and offline, hence both

concepts are relevant for this study. As a concept, loyalty captures behavioral, cognitive and

affective aspects and can be characterized by attitude (Vogt, 2011).

Two key dimensions of loyalty exist in the literature. On the one hand there is behavioral

loyalty and on the other hand there is attitudinal loyalty (Anderson & Srinivasan, 2003;

Hallowell, 1996; Pritchard et al., 1999). Behavioral loyalty can be characterized by repeat

purchases from one particular supplier, an increase in scale and/or scope of the relationship

and by recommendations given. Attitudinal loyalty is about feelings customers have creating

a sort of attachment to a particular product, service or organization and this is solely

cognitive (Hallowell, 1996). Attitude is often related to behavior, but it must be noted that

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these concepts may differ from each other. One may have a favorable attitude toward a

specific product or service, but not purchase it repeatedly because of other comparable

products or services or a stronger attitude to those other products or services. Furthermore,

consumers’ attitude toward a brand needs to be compared to their attitude toward other

brands of the same consumption context. That is to be able to see differences in the strength

of attitudes toward these brands and to measure customer loyalty (Dick & Basu, 1994).

Figure 2 shows a two-dimensional understanding of customer loyalty, wherein attitudinal and

behavioral loyalty are both incorporated. No loyalty forms a combination of low relative

attitude toward a brand and low repeat patronage. On the opposite there is true loyalty; a

combination of high relative attitude toward a brand and high repeat patronage. Between

these two dimensions, two other dimensions exist, namely latent loyalty and spurious loyalty.

Latent loyalty means that a person may feel attached to a certain brand, but does not show a

repeat patronage. Spurious loyalty however, is the complete opposite of the previous

dimension. It represents a person who makes use of a product or service regularly, but no

feeling of attachment to the product or service exists (Dick & Basu, 1994).

Figure 2: Relative attitude behavior relationship

Source: Dick & Basu (1994, p.101)

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Customer loyalty forms an important basis for the development of sustainable competitive

advantage (Dick & Basu, 1994), because loyal customers have several benefits in

comparison with ordinary customers. First of all, they can cause an increase in revenues for

a firm. Second, often they purchase more additional goods and services (Gwinner et al.,

1998). Prokesch (1995) argues that British Airways had found that their effort in relationship

building had led to an increase of 9% in business generated by their customers. Third, loyalty

reduces customer turnover and loyal customers create positive word of mouth (Gwinner et

al., 1998; Heskett et al., 1994). Moreover, retaining a customer is less expensive than to

attract a new one, due to less sales and marketing costs (Conze et al., 2010; Hennig-Thurau,

2002; Gwinner et al. 1998; O’Reilly & Paper, 2009). Health (1997) argues that loyal

customers may yield up to ten times more than average customers.

2.2 The concept of Customer Satisfaction

Customer satisfaction is incorporated in the conceptual model, because in many studies it

proved to be an important determinant of customer loyalty as will become clear later on.

Just as customer loyalty, customer satisfaction takes two forms in the field of this study. First,

customer satisfaction toward a specific tour operator can be defined as “the contentment of

the customer with respect to his or her prior experience with a given firm” (Anderson &

Srinivan, 2003). Second, customer e-satisfaction can be described as “the contentment of

the customer with respect to his or her prior experience with a given firm operating in an

online environment” (Anderson & Srinivan, 2003). According to Heskett et al. (1994),

customers are satisfied when the service delivered meets their needs. It would be even

better, if the service delivery exceeds customers’ expectations. Therefore, it can also be

described as the difference between customer expectations and the delivered service

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(Faché, 2000). In other words, customer satisfaction is the result of customers’ perception of

the value they receive in a relationship (Hallowell. 1996).

2.3 The Relational Benefits Approach

In this study the relational benefits approach was used, which indicates the importance of

benefits for both customers and companies to continue their relationship in the long run.

Positive outcomes of customer loyalty are already mentioned above, however, to create a

long-term relationship also the customer must possess relational benefits. In other words,

there has been a shift in the literature from a business point of view to the customer’s point of

view. Many different types of relational benefits have already been investigated (Gwinner et

al., 1998; Hennig-Thurau et al., 2002). For this research, the most appropriate variables are

chosen; those relational benefits through social media of which it seems plausible to have a

significant effect on customer loyalty.

Customers who are in a relationship with an organization would like to receive a satisfactory

core service. By developing a long-term relationship with a service business, customers will

have extra benefits next to the core service. According to Gwinner et al. (1998), these type of

benefits are called relational benefits. Hennig-Thurau et al. (2002) define relational benefits

as “benefits customers likely receive as a result of having cultivated a long-term relationship

with a service provider”. Literature shows that there are several types of relational benefits.

Researchers do not always use the same benefits in their research and in some cases

relational benefits are adapted or combined. Gwinner et al. (1998) have found significant

relationships between relational benefits and customer loyalty, customer satisfaction and

word of mouth.

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Below, different types of relational benefits that seem important for this research are

explained.

2.3.1 Social Benefits

The first type of benefits often used in research are social benefits customers receive from a

service. Gwinner et al. (1998) define social benefits as “a customers’ need for social bonding

and dealing with someone familiar”. This type of benefit covers the emotional side of

relationships and is about personal recognition of customers by employees and friendships

between them (Yen & Gwinner, 2003). It includes the joy that comes with a close relationship

with a salesperson (Reynolds & Beatty, 1999). Many customers receive social benefits of

having a relationship with a particular service provider, although it seems more common in

situations where there is much personal interaction. However, social media are new online

environments that might allow personal interaction. Social benefits seem important to

incorporate in the conceptual model, since the need for social bonding comes very close with

the concept of social media where people come together to interact with each other.

2.3.2 Confidence Benefits

Another type of relational benefits often employed in research are confidence benefits.

Confidence benefits are defined by Gwinner et al. (1998) as “the customers’ desire for

reduced risks, reliability, and integrity of the company they are engaging with in a

relationship”. It includes trust and confidence in an organization and the feeling of comfort

and security about a company (Gwinner et al., 1998). According to Yen & Gwinner (2003),

confidence benefits are the most important type of relational benefits in face-to-face

encounters regardless the type of service. Furthermore, confidence benefits seems to be an

important variable in the e-business environment according to Su et al. (2009). Su et al.

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argue that customers are concerned about trusting online businesses. Furthermore,

customers perceive personal communication as a more reliable source than impersonal

communication (Hennig-Thurau et al., 2002), which may lead to distrust in the information

given through social media by tour operators. Therefore, confidence benefits seem important

to incorporate in the model.

2.3.3 Functional Benefits

Thirdly, functional benefits are designated as relational benefits. This type of benefits covers

several aspects in the literature. According to Reynolds & Beatty (1999), functional benefits

encompass confidence and special treatment benefits. These type of benefits are already

included separately in the theoretical model of this research. However, also items referring to

knowledge are often included in functional benefits. Parra-López et al. (2011), Paul et al.

(2009) and Wang & Fesenmaier (2004) indicate the existence of the knowledge aspect of

this type of benefits. As Wang & Fesenmaier point out, members of communities are looking

for functional benefits when they search online to fulfill specific needs. These specific needs

may be related to information gathering which helps in decision-making processes. Since the

knowledge aspect is not included in any of the other types of benefits, the functional benefits

in this study will cover this knowledge aspect. Though, it will not cover confidence and

special treatment benefits in this research as the latter variables are treated separately.

Moreover, confidence and special treatment benefits are variables originally applied by

Gwinner et al. (1998) and later used by many other researchers as well, for example by Kim

(2009), Lee et al. (2008), Ruiz-Molina et al. (2008) and Chang & Chen (2007).

2.3.4 Special Treatment Benefits

Fourth, special treatment benefits will be included in the theoretical model of this research.

This type of benefits is about special deals and treatment which is unavailable to non-

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relational customers (Yen & Gwinner, 2003). These benefits include price breaks, faster

service and individualized additional services (Hennig-Thurau et al., 2002; Kim, 2009; Lee et

al., 2008). Special treatment benefits can be utilized by firms to reward loyal customers and

to extend the core service (Lee et al., 2008). There are already many examples of this being

applied in online environments including social media, which is the reason to incorporate this

variable in the conceptual model.

2.3.5 Hedonic Benefits

A type of benefits which is little used in the literature, are hedonic benefits. Wang &

Fesenmaier (2004) argue that one must also take into account experiential aspects when it

comes to consumer information searching, because people also pursue enjoyment and

entertainment. According to the hedonic perspective, consumers are searching for pleasure

in their activities. The online network environment of travel communities is able to bring

amusement, fun, enjoyment and entertainment to people (Wang & Fesenmaier, 2004).

Hedonic benefits are the final relational benefits variable included in the conceptual model of

this study, because it is expected that social media are often used for fun.

2.3.6 Application of the Relational Benefits Approach in the context of Social

Media

According to Yen & Gwinner (2003), the relational benefits approach is mainly applied in the

context of relationships between customers and employees in face-to-face encounters.

Czepiel (1990) defines a customer-salesperson relationship as “an ongoing series of

interactions between a salesperson and a customer while the parties know each other”. Over

the years, the relational benefits perspective is also increasingly used in the context of the

online environment. Due to the use of the Internet, personal contact with employees is

reducing more and more. Therefore, it is interesting to apply the relational benefits approach

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in an online environment. Yen & Gwinner (2003) were one of the first investigating if

relational benefits in the online environment lead to any significant outcomes like satisfaction

and loyalty. Their findings suggested that this approach remained valid in an online context.

The relational benefits approach may already have been applied in an online context, yet

little research has been done on the existence of relational benefits within the world of social

media. Let alone the existence of relational benefits within the world of social media

regarding interactions between customers and service providers, for example tour operators,

being active on social media. This is of particular interest because social media re-introduce

personal encounters in online environments.

2.4 Relationship Commitment

In the literature, relationship commitment is often added as a mediator between relational

benefits and customer loyalty. According to Hennig-Thurau et al. (2002), relationship

commitment can be defined as “a customer’s long-term orientation toward a business

relationship that is grounded on both emotional bonds and the customer’s conviction that

remaining in the relationship will yield higher net benefits than terminating it”. Another

definition often referred to, is the one of Morgan & Hunt (1994): “an exchange partner

believing that an ongoing relationship with another is so important as to warrant maximum

efforts at maintaining it; that is, the committed party believes the relationship is worth working

on to ensure that it endures indefinitely”. Morgan & Hunt believe that relationship

commitment comes close to customer loyalty and, in addition, is central in relationship

marketing. Berry & Parasuraman (1991, p.139) agree at the latter point, arguing that

“relationships are built on the foundation of mutual commitment”.

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2.5 Word of Mouth

Relationship building increases customer satisfaction and loyalty, but also causes an

increase in the amount of positive word of mouth (Berry & Parasuraman, 1991; Hennig-

Thurau, 2002; Reynolds & Beatty, 1999). According to Litvin et al. (2007), word of mouth

proved to be one of the most important sources of information in a purchase decision making

process. Particularly in the hospitality and tourism industry, sectors characterized by

intangible products, it is not possible to evaluate products before consumption. People use

the Internet to gather information and are being influenced by travel reviews of others sharing

their experiences on social networking sites (Litvin et al., 2007). As stated earlier, this is

largely due to the customer’s perception of personal communication being a more reliable

source than impersonal communication (Hennig-Thurau et al., 2002). Word of mouth seems

to be a phenomenon which is highly associated with social media and therefore incorporated

in the theoretical model of this study. Whether people share their experiences on- or offline,

word of mouth captures “all informal communications directed at consumers about the usage

or characteristics of particular goods and services, or their sellers” (Westbrook, 1987). It

concerns evaluations that can be either positive, neutral or negative (Anderson, 1998).

Heskett et al. (1994) indicate the importance of customer satisfaction and loyalty in terms of

their future behavior toward a company. The more satisfied customers are, the more likely it

is these customers will be retained. Moreover, consumers who are intended to repurchase

are more likely to create positive word of mouth (Anderson, 1998; Heskett et al., 1994;

Petrick, 2004b, cited in Petrick & Li, 2006). These customers are called apostles. On the

other hand, there are the terrorists; customers who are very unsatisfied and have a

devastating impact on the firm by creating negative word of mouth (Heskett et al., 1994).

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3. The Construction of the Proposed Model

This section proposes the construction of the proposed model, measuring the influence of

social media on customer loyalty. Based on the literature reviewed, expected relationships

are presented.

3.1 Consequences of Relational Benefits

As outlined before, this study incorporates five different types of relational benefits customers

can perceive from a tour operator being active on social media. The first type are social

benefits. Research has shown that social benefits have a significant impact on customer

loyalty and relationship commitment (cf. Hennig-Thurau et al., 2002, p.240). Although

Hennig-Thurau et al. (2002) did not find support for a positive relationship between social

benefits and customer satisfaction, Reynolds & Beatty (1999) did. Since the interaction

between consumers and a firm’s employees is an important factor of customer’s perception

of the quality of a service, social benefits proved to have a positive effect on customer

satisfaction with the salesperson according to Reynolds & Beatty (1999, p.22). Moreover,

these researchers found a significant effect of social benefits on customer loyalty to the

salesperson. Since the salesperson is substituted by the tour operator being active on social

media in this research, the next hypotheses were formulated:

Hypothesis 1a: Social Benefits are positively associated with Customer e-Satisfaction.

Hypothesis 1b: Social Benefits are positively associated with Customer e-Loyalty.

Hypothesis 1c: Social Benefits are positively associated with Relationship Commitment.

Great significance has been found for the impact of trust and confidence benefits on

relationship satisfaction (cf. Hennig-Thurau et al., 2002, p.240; Yen & Gwinner, 2003, p.493)

and loyalty (cf. Chang & Yen, 2007, p.106; Hennig-Thurau et al., 2002, p.240; Yen &

25

Gwinner, 2003, p.493). Furthermore, there seem to be contradictions in the literature about

the effect of confidence benefits on relationship commitment. Berry (1995, cited in Hennig-

Thurau et al., 2002, p.242), Ganesan and Hess (1997, cited in Hennig-Thurau et al., 2002,

p.242) and Morgan and Hunt (1995, cited in Hennig-Thurau et al., 2002, p.242) all argued

that trust in a service provider should lead to customer commitment. But according to

Hennig-Thurau et al. (2002, p.241), there is only an indirect effect with relationship

satisfaction as a mediator. To be sure not to exclude potential relationships, the following

hypotheses were formulated:

Hypothesis 2a: Confidence Benefits are positively associated with Customer e-Satisfaction.

Hypothesis 2b: Confidence Benefits are positively associated with Customer e-Loyalty.

Hypothesis 2c: Confidence Benefits are positively associated with Relationship Commitment.

Parra-López et al. (2011, p.651) found that functional benefits had a significant effect on the

intention to use social media. However, Wang & Fesenmaier (2004, p.718) did not find

support for a positive relationship between functional benefits and level of participation in an

online travel community. Because of the contradictions and the slightly different variables

used in this study, the following hypotheses were formulated in order to be sure not to

exclude potential relationships:

Hypothesis 3a: Functional Benefits are positively associated with Customer e-Satisfaction.

Hypothesis 3b: Functional Benefits are positively associated with Customer e-Loyalty.

Hypothesis 3c: Functional Benefits are positively associated with Relationship Commitment.

As it comes to special treatment benefits, more contradictions in literature can be found.

Some research showed no significant effects of this type of benefits on customer satisfaction

(cf. Hennig-Thurau et al., 2002, p.240) and customer loyalty (cf. Chang & Yen, 2007, p.106;

26

Hennig-Thurau et al., 2002, p.241), but according to Gwinner et al. (1998, p.109) and Yen &

Gwinner (2003, p.492) it does have a significant effect. Though Hennig-Thurau et al. (2002,

p.240) did not find support for the effect of special treatment benefits on customer

satisfaction and customer loyalty, they did find support for the positive relationship between

special treatment benefits and relationship commitment. Because of the contradictions, all

potential relationships were included in the conceptual model in order to check whether these

exist or not.

Hypothesis 4a: Special Treatment Benefits are positively associated with Customer e-

Satisfaction.

Hypothesis 4b: Special Treatment Benefits are positively associated with Customer e-

Loyalty.

Hypothesis 4c: Special Treatment Benefits are positively associated with Relationship

Commitment.

Wang & Fesenmaier (2004, p.718) found little support for a positive relationship between

hedonic benefits and level of participation in an online travel community. Once again, not

wanting to exclude potential relationships, the next hypotheses were formulated:

Hypothesis 5a: Hedonic Benefits are positively associated with Customer e-Satisfaction.

Hypothesis 5b: Hedonic Benefits are positively associated with Customer e-Loyalty.

Hypothesis 5c: Hedonic Benefits are positively associated with Relationship Commitment.

3.2 The influence of Customer Satisfaction and Relationship

Commitment on Customer Loyalty

Hennig-Thurau et al. (2002, p.241) state that relationship satisfaction and relationship

commitment proved to be mediators between relational benefits and relationship marketing

27

outcomes. These mediators allow a full understanding of the relationship between relational

benefits and customer loyalty. Their research showed that relationship satisfaction and

relationship commitment have a strong and significant effect on customer loyalty (Hennig-

Thurau et al., 2002, p.241). Reynolds & Beatty (1999, p.22) agree at this point by stating that

customer satisfaction proved to have a positive influence on customer loyalty. However, Yen

& Gwinner (2003, p.492) did not find support for this relationship. In terms of commitment,

Gutek et al. (2000, cited in Yen & Gwinner, 2003, p.484) states that customers are more

loyal when they are in a close relationship with an employee of a specific firm. Based on

these studies, the following hypotheses were proposed:

Hypothesis 6: Customer Satisfaction with the Tour Operator is positively associated with

Customer Loyalty to the Tour Operator.

Hypothesis 7: Relationship Commitment is positively associated with Customer Loyalty to the

Tour Operator.

Perceived quality of performance is a main determinant for satisfaction and consumers are

provided with satisfactions apart from the products that are being sold (Reynolds & Beatty,

1999). Westbrook (1981, cited in Reynolds & Beatty, 1999, p. 14) states that consumers are

able to experience satisfaction from an overall experience with the company and through its

salespersons. In Reynolds & Beatty their research, satisfaction of salespersons and

satisfaction of the company are included as separate variables, because they believe that

customers are not only receiving benefits from their relationship with the company but from

their salesperson-relationship as well. Satisfaction of a salesperson proved to have a positive

impact on satisfaction of the company overall (Reynolds & Beatty, 1999, p.22). This is also

confirmed by Goff et al. (1997), Oliver & Swan (1989) and Crosby et al. (1999) as stated by

28

Reynolds & Beatty (1999, p.14). In this study a similar relationship is expected, namely

customers’ satisfaction with the tour operator being active on social media affecting the

satisfaction of the tour operator as a whole. Therefore, the following hypothesis was

formulated:

Hypothesis 8: Customer e-Satisfaction is positively associated with Customer Satisfaction

with the Tour Operator.

Reynolds & Beatty (1999) point out that there is also a difference between loyalty to a

salesperson and loyalty to a company, because of the human contact that is included in a

person-to-person relationship. Czepiel (1990) argues that this may be because trust,

attachment and commitment which arise in person-to-person relationships form the

foundation for person-to-firm relationships. A distinction was made between loyalty to a

salesperson and loyalty to the company, despite the positive relationship that exists between

the two. Loyalty to a salesperson is positively associated with loyalty to a company, only

Reynolds and Beatty pointed out that there is a chance that customers would follow a leaving

salesperson when the merchandise of the stores is similar. This is not the case in the field of

this study, because the tour operator its employee being active on social media remains

unknown for the customer. This potential relationship must not be excluded, wherefore a

distinction was made between e-loyalty and loyalty to the tour operator itself as well:

Hypothesis 9: Customer e-Loyalty is positively associated with Customer Loyalty to the Tour

Operator.

Making a distinction between e-satisfaction and satisfaction with the tour operator overall and

also between e-loyalty and loyalty to the tour operator overall, the next hypothesis was

formulated based on the first hypothesis:

29

Hypothesis 10: Customer e-Satisfaction is positively associated with Customer e-Loyalty.

Customer satisfaction proved to have a significant effect on relationship commitment in

several studies (cf. Beatson et al., 2008, p.215; Hennig-Thurau et al., 2002, p.237; Hennig-

Thurau & Klee, 1997, p.753; Park & Kim, 2008, p.158). Therefore, the following hypotheses

were formulated:

Hypothesis 11: Customer Satisfaction with the Tour Operator is positively associated with

Relationship Commitment.

Hypothesis 12: Customer e-Satisfaction is positively associated with Relationship

Commitment.

3.3 The influence of Relationship Commitment and Customer

Satisfaction on Word of Mouth

According to Hennig-Thurau et al. (2002, p.241), relationship commitment and relationship

satisfaction give an understanding between relational benefits and word of mouth. Their

research showed not only a significant effect of relationship satisfaction and relationship

commitment on customer loyalty, but on word of mouth as well. Dimitriadis (2010, p.306)

confirms the positive relationship between satisfaction and word of mouth. Pritchard et al.

(1999, cited in Hennig-Thurau, 2002, p.232) have found a significant effect of commitment on

customer loyalty in the hotel and airline industry. Gutek et al. (2000, cited in Yen & Gwinner,

2003, p.484) confirm this relationship as well, stating that customers are more willing to

promote a firm when they are in a close relationship with an employee of the specific firm.

Based on these findings, the following hypotheses were formulated:

Hypothesis 13: Relationship Commitment is positively associated with Word of Mouth.

Hypothesis 14: Customer e-Satisfaction is positively associated with Word of Mouth.

30

Hypothesis 15: Customer Satisfaction with the Tour Operator is positively associated with

Word of Mouth.

3.4 A graphical illustration of the proposed conceptual model

The formulated hypotheses are summarized in figure 3, which illustrates the proposed

conceptual model, measuring the influence of social media on customer loyalty in the travel

trade.

Figure 3: The proposed conceptual model

Source: own design

31

4. Methodology

This chapter describes the research design used for this research. Second, a description of

how the data is collected is given, followed by a description of the way the data is measured.

Finally, the method of data analysis is described.

4.1 Research design

As mentioned previously, the objective of this research is to create an understanding of how

social media affect customer loyalty to tour operators, by investigating the complex online

relationships they have with their clients. Therefore, literature on customer loyalty was

reviewed to design a research model. This is the exploratory part of the research. The

explanatory part will take place when the model is tested statistically, using hypotheses that

are associated with the theoretical model. These hypotheses propose causal relationships

between the different variables that eventually lead to loyalty to the tour operator.

4.2 Data collection

The study population consists of those customers of tour operators who have developed a

relationship with their tour operator through social media. Despite of the fact that travel

agents are also recommended to use social media, only tour operators were included in this

research to demarcate the field of research. It is expected that online bookers are more and

more purchasing their holidays directly with tour operators. In addition, travel agents their

clients are often more located locally, which differs from tour operators’ customer base. To

give insights into the research questions stated previously, the focus of this study lies on

customers posting messages or liking posts of their tour operator on Facebook. Solely

Facebook was used as the sampling frame because of two reasons. The first one is that

Facebook has more than 800 million active users worldwide (Facebook, 2011), which makes

32

this medium one of the most popular social networking sites. Its popularity is still increasing

today. Many marketers integrated a Facebook page into their social media marketing

strategy, whether it is to e.g. create awareness, provide service to customers, stimulate

sales and search presence or to foster customer loyalty. Facebook is very useful in targeting

the audience. It allows marketers to provide customers all the information needed, instead of

giving customers a quick update as is possible using Twitter (Zarella & Zarella, 2011).

Ellison et al. (2007) confirm this by arguing that the heavy usage patterns and technological

capacities of Facebook make this social medium interesting. Yet another reason to choose

for Facebook as a sampling frame, comes with a practical motivation. Just as for marketers,

it is easy for a researcher to approach the target group through Facebook because of its

high visibility.

To test the hypotheses of this research, this study employed a survey. The reason for this is

that many respondents are needed to validate the conceptual model and it is impossible to

interview each individual personally. In addition, the research question is descriptive and

there were several variables to be tested. Given these criteria, a survey would be appropriate

according to Vennix (2007). Since this study is about social media, the survey was sent out

online and more specifically to Facebook users who post messages or like posts of their tour

operator. The sampling frame was categorized by 39 different tour operators having a

Facebook page where they post messages frequently. Out of these Facebook pages,

individuals have been selected randomly and proportionally. Proportionally means that on

each Facebook page the same amount of people has been approached in order to attract an

equal variety in types of travelers. Both Dutch and Belgian Facebook users were selected to

increase the volume of potential respondents. Though there are more Dutch Facebook

pages than Belgian ones, there are also Belgian people liking Dutch tour operators and vice

33

versa. Where possible, both Dutch and Belgian pages of the same international tour

operators are included. In order to be able to generalize the results, the aim of this study is to

cover a heterogeneous set of consumers by including two nationalities and a wide variety of

tour operators.

The sample size required for the technique used in this study is approximately 110-165

respondents, since there are 11 latent variables (wherefore 10 times more respondents are

needed to conduct factor analysis (Wijnen, 2002) and 15 times more respondents are

needed to conduct Structural Equation Modeling (Stevens, 1996). Beforehand, a low

response rate was expected because of the unfamiliarity of the target group with the

researcher. Therefore, people their willingness to participate was tested by asking a few

potential respondents for their collaboration. This resulted in a response rate of 12.5%.

4.2 Measurement

50 items were used to capture the various constructs of the conceptual model. Table 16 in

appendix 1 presents the items used. All of these items are based on existing literature and

may be somewhat adjusted to the context of this study. The survey contained Likert Scales

from 1 to 7 (totally disagree – totally agree). The literature on which the items are based are

presented in tables 6 to 15 in appendix 1.

4.3 Data analysis

To understand the complex relationship between social media and customer loyalty, the

theoretical model of this research includes multiple observed variables. Structural Equation

Modeling is a suitable method to (dis-)confirm comprehensive theoretical models such as the

proposed one here, while basic statistical methods are not capable of testing complex

phenomena. As SEM techniques explicitly take measurement errors into account, validity

34

and reliability are greatly recognized (Schumacker & Lomax, 2004). For conducting this

analysis, the programs ‘SPSS’ and ‘AMOS’ were used. The performance of the analysis is

structured corresponding to Mulaik and Millsap (2000) “four-step” modeling approach,

including factor analysis and structural model testing.

Before testing the model, the data gathered needed to be examined. There was no need for

missing value analysis, because each question was mandatory to fill in and incomplete

response could not be resolved. Since Structural Equation Modeling is a multivariate

regression technique (Hair, 2010, p.641), the data were tested on the assumptions for

performing multivariate analysis. These assumptions include normality, linearity,

homoscedasticity and independence of error terms (Hair, 2010, p.182). Another important

assumption for regression analysis that will be tested is no multicollinearity (Field, 2006,

p.170).

35

5. Results

This section represents the results of this study. Out of 2594 people who were asked to fill in

the questionnaire, 11.22% responded. However, nearly half of these respondents did not

complete the survey which led to a sample size of 157 respondents. No missing value

analysis was conducted, because all incomplete response contained too many missing

values. Furthermore, all tour operators were equally represented. Table 1 shows the general

sample details. 36.3% people out of the sample size were male, 63.7% were female. In

terms of nationality, 28.0% are Belgian, 70.7% are Dutch and 1.3% have another nationality.

The majority of the respondents are married and have higher education.

Table 1: General sample details

Consumers # %

Total 157 100

Gender

Male

Female

57

100

36.3

63.7

Nationality

Belgian

Dutch

Other

44

111

2

28.0

70.7

1.3

Education

None

Primary education

Lower secondary education

Higher secondary education

Higher education (without the University)

University

I would rather not say

1

1

19

50

58

25

3

0.6

0.6

12.1

31.8

36.9

15.9

1.9

36

General sample details (continued)

Consumers # %

Domesticities

I live with my parents / grandparents

I live independent

I live independently with child(ren)

I am married / living together without children

I am married / living together with children

I am married / living together, children left home

Other

28

33

3

39

34

16

4

17.8

21.0

1.9

24.8

21.7

10.2

2.5

Source: own findings

This chapter begins with checking the assumptions for multivariate analysis. After that, this

section is structured based on the way the theoretical model is tested. Corresponding to

Mulaik and Millsap (2000), the conceptual model was tested using a four-step modeling

approach:

1. Exploratory factor analysis to determine the number of latent variables;

2. Confirmatory analysis to approve the measurement model;

3. Structural equation modeling to test hypothesized relationships between latent

variables;

4. Nested structural models testing to find the best fitting model.

The first step was performed in SPSS 17.0, while the other steps were carried out in AMOS

20.0.

5.1 Checking assumptions

As stated in §4.3, the data was tested on the assumptions of multivariate analysis, including

normality, linearity, homoscedasticity, independence of error terms and no multicollinearity.

The first assumption to test is the assumption of normality. SEM requires a multivariate

normal distribution, which implies a univariate normal distribution for each variable and a

37

bivariate normal distribution between pairs of variables (Gao et al., 2008). Normality of

multivariate distribution was tested using AMOS by checking Mardia’s coefficient of

multivariate kurtosis and the squared Mahalanobis distance. The critical ratio of Mardia’s

coefficient proved to be equal to 18.917, which indicates significant non-normality since this

value must be below the critical ratio of 1.96. Furthermore, higher values of the squared

Mahalanobis distance indicate larger differences between observations and the centroid

under normal distributed conditions. Therefore, these values are a sign of outliers influencing

non-multivariate normality and indicate that in these data 100 observations proved to be too

far from the centroid (Sharma, 1996). Univariate normality of the data was tested using the

Kolmogorov-Smirnov test and the Shapiro-Wilk test in SPSS. According to Field (2006),

histograms tell little about whether a distribution is close enough to normality and values of

skewness and kurtosis give only information about specific aspects of normality. The

Kolmogorov-Smirnov test is a more objective test to decide whether a distribution is normal

or not and is suitable for small sample sizes (Field, 2006, p.93). Unfortunately, nearly all

variables proved to be significantly non-normal according to both tests (<.05) as can be seen

in appendix 2. In order to meet the condition of normality, transformations to the raw data

were attempted, however without any improvements.

Second, the assumption of linearity was tested. Appendix 2 shows scatter plots of all the

hypothesized relationships. An interpretation of these scatter plots indicates that all

relationships are linear.

Furthermore, outliers shown by the scatter plots give reasons to assume that the third

assumption of homoscedasticity has been violated, so this was tested using Levene’s test

(Hair, 2010). As can be seen in appendix 2, only 10 relationships show homoscedasticity and

38

all the other relationships violate this assumption. Heteroscedasticity causes predictions to

be better at some levels of the independent variables than at others, which means

hypothesis tests will be either too stringent or too insensitive (Hair, 2010). Since

heteroscedasticity is often the result of non-normality, this problem can be remedied the

same way as non-normality can be. However, transformations to the data proved to be

unsuccessful and therefore the initial data including its consequences will be used in further

analysis.

The fourth assumption to test is the one of independence of error terms. This assumption

was tested using the Durbin-Watson test. Appendix 2 shows the output of this test on each

hypothesized relationship between predictors and dependent variables. These values can

vary between 0-4, with a value of 2 meaning the residuals are uncorrelated. There appeared

to be no violation of this assumption of independent error terms, since all values come very

close to 2 (Field, 2006, p.170).

The last assumption of no multicollinearity was tested using VIF values. Based on these

values, there is no reason to suspect high multicollinearity because all values are below the

critical value of >10. But since problems could already occur when VIF values are between 3

and 5, the potential existence of multicollinearity will not be ruled out and an eye will be kept

on it during the factor analysis.

The violations found have implications for the techniques to be used. A way to cope with

non-normality, is the use of an estimation method that makes no distributional assumptions,

like Unweighted Least Squares (ULS) or Asymptotically Distribution-Free Estimation (ADF).

However, as it comes to ULS, AMOS does not provide any tests indicating model fit and ADF

requires an enormous sample size measured in thousands. A third way to cope with a non-

39

normal distribution, is to use robust statistics along with Generalized Least Squares or

Maximum Likelihood, but robust statistics are unavailable in AMOS. A method which seems

more appropriate is bootstrapping (Blunch, 2008, p.225). Bootstrapping forms also a solution

to small sample sizes, as in this study (Davison & Hinkley, 1997). With bootstrapping the

sample is considered to be the population, out of which new samples with replacement are

taken. From each of these samples, the required sample statistics are calculated which gives

an empirical sampling distribution with estimates of the parameters and empirical standard

errors (Blunch, 2008).

Now that there is a solution to the violation of the assumption of normality, it is time to move

on to the actual analysis. A solution to the heteroscedastic data has not been found, which

must be kept in mind with the results of the hypothesis testing.

5.2 Exploratory factor analysis

With Exploratory Factor Analysis, it is possible to identify different latent variables. To

determine whether the proposed indicators measure only one underlying construct, the

Principal Component Analysis was used. Kaiser-Meyer-Olkin measure of sampling adequacy

tests if correlation patterns are diffused (KMO=0) or compact (KMO=1) and indicated that all

variables show good (.7 ≥ KMO ≥ .8) or even great (.8 ≥ KMO ≥ .9) values (Field, 2006,

p.650), which means it accepts the use of factor analysis on the data. Bartlett’s test of

sphericity showed great significance for all variables (p=.000) indicating that items are highly

correlated with each other, which is necessary for factor analysis to work (Field, 2006,

p.652). Furthermore, most communality values (>.50) indicate that items show sufficient

explanation (Hair, 2010, p.119), except for two items measuring Social and “confidence

benefits”. To determine visually if data have only one underlying factor, scree plots can be

viewed. All curves have a distinctive bend after the first component, which means that there

40

is only one component the items are measuring (Field, 2006, p.633; Blunch, 2008, p.54).

That all items are measuring just one factor, can also be seen in the component matrix which

shows only 1 component. The total variance explained is for most cases well above the

preferred 60% and each variable shows just one eigenvalue above the critical value >1.0.

These findings are based on the output shown in appendix 3 and indicate that the 11

proposed latent variables will be retained, because all items are uni-dimensional.

When using factor analysis to validate a questionnaire, as in this study, it is useful to check

the reliability of the scales (Field, 2006). The full useful data collection (n=157) was tested to

check if the scales are reliable. For this, Cronbach’s Alpha was used, of which its value is

good when it is around .800 (Field, 2006, p.676). As shown in appendix 3, all measurement

scales show high reliability. All of them exceed the value of .800 and half of them even .900.

Next to this, each item correlates well with the scale overall since all these correlations are

above .300 (Field, 2006, p.672). Despite of the unsufficient explanation of two items as found

during the Principal Component Analysis, all items will be retained in further analysis

because deletion of items would not result in substantial higher values of Cronbach’s alpha.

5.3 Confirmatory factor analysis

Using Confirmatory Factor Analysis, relations of the manifest indicators to the latent variables

are tested. If there is an acceptable fit of the measurement model, it is possible to move on to

step 3 in which the structural model will be tested (Mulaik & Millsap, 2000).

In this study, 2000 bootstrap samples were taken because of the model complexity. Simpler

models (e.g. Arbuckle, 2011, p.296), require smaller sample sizes (Hair, 2010, p.661). This

bootstrap method was combined with the Maximum Likelihood method, which is the most

preferred (Blunch, 2008, p.81) and the most common SEM estimation procedure providing

41

valid and stable results (Hair, 2010, p.661). Normally the Maximum Likelihood estimation

method requires normal distribution (Blunch, 2008), but this problem was dealt with using

bootstrapping (Bollen, 1989).

Once the proposed model has been estimated, theory and reality must be compared by

assessing model fit which indicates the similarity of the estimated covariance matrix (theory)

to the observed covariance matrix (reality) (Hair, 2010). Bollen & Stine (1992) showed that

the bootstrap generally used is inappropriate for assessing model fit, wherefore they

introduced a modified method called the Bollen-Stine bootstrap. Several model fit indices

exist. To test the overall model fit, Bollen-Stine bootstrap provides a p-value which need to

exceed .05 in order to accept the model. With p=.001 for the initial model, the measurement

model is rejected. Furthermore, χ2 had a value of 2391.959 and df=1145. Hooper et al.

(2008) have reviewed other researchers and their recommendations for model fit indices to

report and concluded that, next to χ2, it is sensible to report the RMSEA, the SRMR, the CFI

and the PNFI. Table 2 shows the acceptable threshold levels of these model fit indices.

Rejection of the model was supported by the following values: RMSEA=.084; SRMR=.266;

CFI=.826; and, PNFI=.669. Though, RMSEA and PNFI are close to what it is supposed to be

in order to accept the model. However, it is not uncommon to find a poor fit of the proposed

model (Hooper et al., 2008).

Table 2: Fit indices and their acceptable threshold levels

Fit index Acceptable Threshold Levels Reference

Bollen-Stine Bootstrap p >.05 Bollen & Stine (1992)

RMSEA Value < .07 Hooper et al. (2008)

SRMR Value < .08 Hooper et al. (2008)

CFI Value > .95 Hooper et al. (2008)

PNFI Value > .70 Gursoy & Rutherford (2004)

42

In order to improve model fit, items could be deleted if they show regression weights below .2

(Hooper et al., 2008). Since all items have significantly high factor weights (see appendix 4),

no items had to be deleted and an alternative solution must be found to solve the poor model

fit. A second way to improve model fit, is to look if there are any latent variables which have a

relatively high covariance and combine these two into one factor (Hooper et al., 2008).

Although VIF values did not denote high collinearity, this seemed the case for the

“relationship commitment” and “customer loyalty” variables after performing a second

Principal Component Analysis. The items of “relationship commitment” and “customer

loyalty” seem to be unidimensional as shown by the SPSS output in appendix 4. Taking a

closer look at the items of both initial variables, a logical reasoning corresponds with the

aggregation of those two. Reliability was checked again and Cronbach’s alpha showed a

value of .957, which is even better than the two latent variables separately. There was no

need to delete any items, because Cronbach’s alpha could not be improved. χ2 increased to

2548.810 and df is now equal to 1147, with p=.000. Other model fit indices have the following

values: RMSEA=.089; SRMR=.2656; CFI=.805; and, PNFI=.652. This means that there is no

improvement in model fit due when combining “relationship commitment” and “customer

loyalty”.

Although the Durbin-Watson test denoted independence of errors, covarying error terms

could improve model fit (Hooper et al., 2008). According to Jöreskog & Long (1993, cited in

Hooper et al., 2008, p.56), covarying error terms requires a strong theoretical justification,

which is easier to find within the same specific factor than across different factors (Hooper et

al., 2008). Therefore, a couple of error terms of items within the same factor were covaried in

order to try to improve the model fit, using modification indices. Error terms of items with a

relatively high modification index and sufficient theoretical justification were covaried. These

43

included 22 error terms in total. There were two other relatively high modification indices for

error terms, however these were not covaried in AMOS since there is not sufficient

theoretical justification through logical reasoning. Appendix 4 shows which error terms were

covaried including a theoretical justification.

Covarying all these items, did improve the model fit, but unfortunately not enough in order to

accept the measurement model. χ2 decreased to 2223.081 and df is now equal to 1125, with

p=.002. Tabachnick and Fidell (1996) state that reasonable results for other indices approve

continuation of working with the proposed model, despite of a non-significant χ2. Other model

fit indices have following values: RMSEA=.079; SRMR=.2656; CFI=.847; and, PNFI=.675.

These values are still not great, but there is more potential to improve model fit.

The measurement model has been respecified now in order to try to validate the latent

variable constructs. Relationships between these variables were tested in the next

paragraph.

5.4 Structural Equation Modeling

During this third step, the entire model was tested on path significance. Out of a second

Principal Component analysis, it was concluded that the items of the latent variable

“relationship commitment” proved to measure the same underlying factor as the items of

“customer loyalty” did. These variables were taken together as one factor called “customer

loyalty” in further analysis. By interpreting the items the term “customer loyalty” was chosen

to be retained and also because this study is about the influence of social media on customer

loyalty. Each hypothesis related to “relationship commitment” has been adjusted to the

combined latent variable “customer loyalty”, in order to get insight in the entire model.

44

Out of an initial Maximum Likelihood estimation in combination with bootstrapping, it can be

concluded that model fit has improved, but still not enough. Chi-square shows a non-

significant value (χ2=1988.624, df=1120, p=.010) and the model fit indices support this non-

significance with values of .071 for RMSEA, .0835 for SRMR, .879 for CFI and .698 for PNFI.

Still, RMSEA, SRMR and PNFI are very close to the threshold levels of these indices.

However, there is potential to improve model fit during step 4.

With the proposed model, only a few significant paths were found at a confidence interval of

95 percent. As the p-values in appendix 5 show, 7 latent variables proved to have a

significant effect (p≤.05) on another latent variable. Significance was found for hypothesis 6,

supporting the effect of “customer satisfaction” on “customer loyalty”. Next to “customer

satisfaction”, also “social benefits” proved to have a significant effect on “customer loyalty”

which means hypothesis 1c is accepted. Moreover, hedonic benefits proved to affect

“customer e-satisfaction” which leads to an acceptance of hypothesis 5a. A significant effect

of these benefits on “customer e-loyalty” was nearly found, however hypothesis 5b could not

be accepted (p=.056). Next, support was found for hypothesis 8, since the regression weight

for “customer e-satisfaction” in the prediction of “customer satisfaction” is significantly

different from zero at even a .001 probability level. Furthermore, hypothesis 9 is supported,

accepting a significant effect of “customer e-loyalty” on “customer loyalty”. “word of mouth”

was found to be significantly affected by “customer satisfaction” as well as by “customer

loyalty”, whereby hypothesis 13 and 15 are accepted.

5.5 Nested Structural Models

In this last section, a series of nested structural models were tested to find the best fitting

model. For this, the specification search tool in Amos was used. A total of 182 different

models was tested next to the saturated model. All these models were compared using BCC

45

and BIC values; model fit indices provided by the specification search tool in AMOS

(Arbuckle, 2011). Table 3 and 4 show how BCC and BIC values should be interpreted.

Table 3: Interpretation of BCC values

BCC Burnham and Anderson (1998) interpretation

0-2 There is no credible evidence that the model should be ruled out as

being the actual best model for the population of possible samples.

2-4 There is weak evidence that the model is not the best model.

4-7 There is definite evidence that the model is not the best model.

7-10 There is strong evidence that the model is not the best model.

>10 There is very strong evidence that the model is not the best model.

Table 4: Interpretation of BIC values

BIC Raftery (1995) interpretation

0-2 There is weak evidence against a competing model

2-6 There is positive evidence against a competing model

6-10 There is strong evidence against a competing model

>10 There is very strong evidence against a competing model

As can be seen in appendix 6, BCC as well as BIC put model 32 forward as the best fitting

model. However, an interpretation of the BCC values indicate that several other models (with

a BCC value between 0-2) should not be ruled out (Burnham & Anderson, 1998). This means

that 15 other models are also candidates in the competition of the best fitting model. Out of

an interpretation of the BIC values it can be concluded that BIC values between 0-2 have the

highest approximate posterior probability. According to Raftery (1995), this means there is

weak evidence for model 32 against model 22, but positive evidence against 25 other

models. The 15 models which were still in the running according to the BCC value, are now

dismissed by an interpretation of their BIC value. The second and only other model having a

BIC value between 0 and 2, was not indicated as one of the best fitting models by the BCC

46

index and therefore dismissed as well. Another indication for the best fitting model is the

value of C/df. C/df indicates that model 62 (C/df=1.764) is the best fitting model, however this

model did not appear in the list of best fitting models according to BCC and BIC. An

interpretation of the BIC value (7.149) of model 62 declares strong evidence against this

model (Raftery, 1995). Besides, the C/df values for all models are very close to each other.

According to Arbuckle (2011), the overall best fitting model must be included in the short list;

a function provided by the specification search tool in AMOS which shows the best model for

different parameters. This is the case for model 32, as it is displayed here (see appendix 6).

Most other potential best fitting models indicated by the BCC are not in this short list, which is

another reason to exclude these models. Using the plot function in the specification search

tool, no models could be found with an acceptable value of CFI (≥.95) or NFI (≥.90)

(Arbuckle, 2011). Given these criteria, model 32 is chosen as the best fitting model and

shown by figure 4.

The final model is now as good as it gets. Model fit improved, but unfortunately not enough

according to Bollen-Stine bootstrap (χ2=1999.957, df=1132, p=.015). Though model fit index

CFI has improved compared to its value of the proposed model, it still supports poor fit for

the final model (.879). On the other side, RMSEA (.070) and PNFI (.704) show acceptable

model fit values. SRMR improved extremely and has almost an acceptable value (.0827).

47

Figure 4: Final model

Source: own findings

5.6 Hypotheses testing

Table 5 shows the significant paths found by the best fitting model. Different from the original

SEM, 11 hypotheses have received support instead of 6. Almost all variables show a positive

influence on “customer loyalty”, either directly or indirectly. Appendix 6 shows the indirect

effects that were found.

Significance was found for the effect “social benefits” have on “customer e-loyalty” (H1b) and

“customer loyalty” (H1c). The social aspect of the relationship through social media

perceived by customers is positively associated with loyalty to the organization operating

online and even stronger with loyalty to the tour operator offline. If customers like their feeling

of a strong bond with their tour operator through social media, they will have a favorable

48

attitude toward the organization manifested in repeat buying behavior. No significant effect

was found for the relationship between “social benefits” and “customer e-satisfaction”. The

enjoyment of having a close relationship with a tour operator through social media, does not

have a significant effect on customers’ contentment with the tour operator through social

media. As noted in §3.1, contradictions about this relationship exist and the non-significance

found here corresponds to the findings of Hennig-Thurau et al. (2002). Furthermore, an

indirect effect of “social benefits” on “word of mouth” has been found. Therefore, a sense of

some sort of bond with a tour operator through social media positively affects the informal

communication with other customers.

Table 5: Significant direct effects found by the final model

Hypothesis Path

coefficients

S.E. P-value

H1b Customer e-Loyalty Social Benefits .228 .087 .009

H1c Customer Loyalty Social Benefits .434 .099 ***

H2a Customer e-Satisfaction Confidence

Benefits

.398 .072 ***

H3c Customer Loyalty Functional Benefits -.588 .152 ***

H5a Customer e-Satisfaction Hedonic Benefits .458 .091 ***

H6 Customer Loyalty Customer Satisfaction .525 .096 ***

H8 Customer Satisfaction Customer e-

Satisfaction

.757 .111 ***

H9 Customer Loyalty Customer e-Loyalty .410 .094 ***

H10 Customer e-Loyalty Customer e-

Satisfaction

1.076 .148 ***

H13 Word of Mouth Customer Loyalty .294 .059 ***

H15 Word of Mouth Customer Satisfaction .848 .099 ***

Source: own findings

49

As it comes to “confidence benefits” only support was found for hypothesis 2a, stating

“confidence benefits” have a significant effect on “customer e-satisfaction”. A feeling of trust

and confidence with respect to a tour operator creates contentment with the organization

operating in the online environment. No significant influence of “confidence benefits” on

“customer e-loyalty” and “customer loyalty” was found, which contradicts with existing

literature. Most researchers also found significance for the effect on “relationship

commitment”, which was combined with “customer loyalty” during the analysis of this study.

The results here are more corresponding to what Hennig-Thurau et al. (2002) have found; an

indirect effect of “confidence benefits” on “customer e-loyalty” and “customer loyalty”.

“confidence benefits” also proved to have a significant indirect effect on “word of mouth”.

When customers trust their tour operator operating in an online environment, they are

intended to be positive about the organization toward other consumers. Satisfaction and

loyalty, online as well as offline, proved to be mediating this effect.

Next, a remarkably significant effect of “functional benefits” has been found. It turned out that

“functional benefits” have only a direct effect on “customer loyalty” to the tour operator, which

is also negative. This means none of the hypotheses related to “functional benefits” were

accepted, not even hypothesis H3c since this relationship turned out to be negative instead

of positive. Customers who are looking for information in their decision making process and

who find the information they need through social media, did not appear to be loyal to the

tour operator. This finding matches to what has been stated in the introduction, about the

Internet increasing competition because of the greater access to new and more products.

The information provided by tour operators through social media enable consumers to

compare several organizations, which makes it more challenging for the tour operator to

make these customers loyal. Moreover, it slightly affects “word of mouth” indirectly in a

50

negative way. People who search for information which helps in their decision making, are

not only being able to compare but are also intended to share these comparisons with

others. On the other hand, customers who are not focused on information gathering show

more loyalty. These are the type of customers who are satisfied with their tour operator and

who are not interested in what the offerings are elsewhere. They are not intended to search

online for information that helps in their decision making process, because they feel

comfortable with the organization they always go to. No significant effects of “functional

benefits” on “customer e-satisfaction” and “customer e-loyalty” were found though. This

corresponds to the findings of Wang & Fesenmaier (2004), who stated that “functional

benefits” and level of participation in an online travel community are not positively related.

The provided information by tour operators through social media is not the reason why

customers are likely to get satisfied and does not encourage them to make use of the online

relationship on a regularly basis.

None of the hypothesized relationships regarding “special treatment benefits” proved to be

significant, which corresponds to the findings of Chang & Yen (2007) and Hennig-Thurau et

al. (2002). It appears that these benefits do not positively influence people their contentment

or loyalty toward a specific tour operator, neither online or offline. People who are looking for

special deals when it comes to their holidays, are very sensitive to price breaks, faster

service and individualized additional service. It could be that they find their special deal at

one tour operator this year, but when next year another tour operator offers a better special

deal they go to this other organization. For a tour operator it is very hard to make these

customers loyal, because these customers will search for the best deal every time they are

planning to book a holiday and they do not mind with whatever organization this holiday may

be.

51

“Hedonic benefits” showed a significant direct effect on “customer e-satisfaction” (H5a). The

pleasure found in the relationship with a tour operator through social media positively

influences customers’ satisfaction with the specific organization operating in the online

environment. Significant indirect effects have been found of this type of benefits on

“customer e-loyalty”, “customer satisfaction”, “customer loyalty” and “word of mouth”. The

enjoyment of the relationship with a tour operator through social media proves to be an

indication of contentment and for a favorable attitude toward the organization expressed in

repeat buying behavior and favorable informal communication with others. This connects to

the findings of Wang & Fesenmaier (2004), who found little support for a positive relationship

between “hedonic benefits” and level of participation in an online travel community.

“Customer satisfaction” appeared to have a positive effect on “customer loyalty” (H6). Even

stronger support was found for this relationship in an online context (H10). Customers who

are satisfied with their tour operator are likely to make use of the service of the organization

repeatedly. “customer e-satisfaction” is also positively associated with “customer satisfaction”

(H8), which suggests that contentment with the relationship with a tour operator in an online

environment fosters the contentment with the relationship with the organization overall. A

less strong, but still significant, influence have been found regarding loyalty. “customer e-

loyalty” proved to be positively associated with “customer loyalty” (H9), which means that the

attitude toward the online service of a tour operator positively influences the attitude toward

the tour operator overall. Indirectly, “customer e-satisfaction” has also a strong significant

effect on “customer loyalty”. If a customer’s prior online experience with a tour operator has

been satisfying, the customer is likely to have a favorable attitude toward the organization

with “customer e-loyalty” and “customer satisfaction” mediating this effect. There is also a

strong significant effect of “customer e-satisfaction” (indirectly) and “customer satisfaction”

52

(directly) on “word of mouth” (H15). People who are content with their relationship with their

tour operator, either online or offline, are likely to spread favorable “word of mouth”. This is

also the case for loyal customers, as significance have been found for the positive

relationship between “customer loyalty” and “word of mouth” (H13). However, satisfied

customers are 3 times more likely to spread favorable “word of mouth” than loyal customers

are. These findings correspond to the literature, except for the relationship between

“customer e-satisfaction” and “word of mouth”.

53

6. Conclusion

In this last section, conclusions are made based on the empirical findings in this study. These

findings helped in answering the research question stated in the introduction. The research

question was as follows:

In what way and to what extent do relational benefits of social media have an impact on

customer loyalty toward a specific tour operator?

Many studies have shown the importance of relational benefits influencing customer loyalty.

This study provides additional evidence for the role relational benefits play in an online

context, more specifically in a rather new context of social media. Relational benefits have

been applied mostly in face-to-face encounters, however the results prove that the relational

benefits approach remains relevant in a context of social media where personal encounters

are re-introduced.

The results of this study show that social benefits of a relationship with a tour operator

through social media are directly affecting customer loyalty in a positive manner. Customers

who like their strong bond with their tour operator through social media, are likely to have a

favorable attitude toward the organization expressed in repeat bookings. These customers

have the feeling that they have created a friendship and feel personally recognized by their

tour operator. Those sort of feelings prove to be the most important for customers in order to

value their relationship resulting in repeat bookings. An even stronger direct effect found was

the effect of functional benefits on customer loyalty. However, this effect turned out to be

negative, meaning that the provided information through social media enables consumers to

compare different organizations because of the greater access to new and more products. In

addition, people who search for information which helps in their decision making, are not only

54

being able to compare but are also intended to share these comparisons with others.

Customers’ access to more information makes relationship marketing challenging for an

organization due to increasing competition. On the other hand there are customers who

show more loyalty because they are less focused on information gathering. These people are

less interested in offerings elsewhere, because they are satisfied with the tour operator they

always go to. It turns out that customers are not encouraged to become loyal due to the

information tour operators are providing through social media. Confidence and hedonic

benefits only show an indirect effect on customer loyalty. A feeling of trust and confidence

with respect to a tour operator creates loyalty through satisfaction with the organization.

When someone trusts the information and intentions of a tour operator, he or she will

become satisfied about the tour operator’s service on social media, but he or she will not

become loyal immediately. So does the enjoyment of the online relationship with the tour

operator have an indirect effect through satisfaction, of which its influence is even slightly

stronger. The joy the online relationship brings, is a great determinant for customers’

satisfaction with their online relationship. And even though it influences customer loyalty not

directly, it does have the second greatest positive effect on customer loyalty but in an indirect

manner. Special treatment benefits appeared to have no influence on customer loyalty at all.

Customers who are looking for special deals are very sensitive to price breaks, faster service

and individualized service. These type of customers will search for the best deal every time

they are planning to book a holiday, regardless which tour operator offers the deal. Therefore

it is very hard for tour operators to drive these customers to become loyal.

In summary, the results of this study advocate that people are increasingly comparing offers

online with the advent of social media. Tour operators provide all the information themselves,

on which customers base their decisions when it comes to their holidays. But providing

55

information seems not the way to make customers loyal. On the other side, if a tour operator

does not provide information and competitors do, customers are not able to compare with

this specific tour operator which drives the organization out of the competition. Beside the

fact that customers are driven by gathering information online, they are also very deal-

oriented and special deals are quite easy to compare since practically all tour operators offer

price breaks or contests through social media. Providing information and special deals are

therefore not the way to go when fostering customer loyalty is the ultimate goal. Tour

operators are better off with intensifying their personal relationships with their clients, by

making them feel personally recognized and creating an online friendship. Moreover, the

online relationship should be fun for customers to have. Therefore, tour operators must focus

on triggering customers’ interest and they must attempt to delight their customers.

56

Limitations and Suggestions for Further Research

This research contains some limitations, which need to be kept in mind while drawing

conclusions. Also suggestions for further research will be proposed in this section.

First of all, only Facebook has been used as a sampling frame, while there are many other

social media not included in this study. Although Facebook was chosen through valid

reasoning, drawing accurate conclusions about the effect of social media on customer loyalty

toward a tour operator may require investigating other social media as well. A similar

research including several types of social media could be a suggestion for further research.

Second, social media are assumed to have other effects beside those on customer loyalty.

For example, special treatment benefits proved to have no influence on loyalty to the tour

operator, however it may have an effect on brand awareness. There were a few respondents

who commented that they did not belong to the target group, because they only participated

in an online relationship because of their chance to win a free holiday and that they had not

been on vacation with the specific tour operator before. In this case, the tour operator had

succeeded in attracting those customers by offering a contest. Therefore, the potential

special deals have should not be underestimated, but this study made clear that its potential

does not lie in stimulating customer loyalty. A suggestion for further research could be the

application of the relational benefits approach in the context of social media measuring other

marketing goals not included here.

Third, the model was only tested in a context regarding tour operators. However, the effect of

social media on customer loyalty is not only interesting for tour operators, but also for travel

agents who are also recommended by professional experts to use social media. Moreover,

57

other industries may also benefit from research on the effect social media have on customer

loyalty. Therefore, another suggestion for further research could be an application of the

theoretical model in the context of travel agents and other sectors. If other researchers would

find the same results, this could be additional justification of the results of this research.

Finally, the best fitting model did not give acceptable values for all model fit indices. Which

implies that the final model cannot be accepted. The poor model fit could be a consequence

of the small sample size used. Luckily this does not mean individual paths cannot be looked

at, but it must be noted that the data were heteroscedastic and that this could cause

problems in hypotheses testing. A suggestion for further research could be to investigate the

relationships with a larger sample size, to see if a better fitting model can be achieved.

58

Theoretical and Managerial Implications

Vogt (2011) evokes researchers to do more research in the field of customer relationship

management. In practice, customer relationship management is widely used in the tourism

industry, however little research is done with regard to this growing field. Vogt claims that a

greater understanding of customer loyalty and CRM programs used as a relational tool in

social networks, will become more important. This study meets the demand for this specific

CRM research in relation to the tourism industry, as it focuses on the use of social media in

fostering customer loyalty. Furthermore, although the use of the relational benefits approach

has been applied in an online context before, it is rather new in the environment of social

media as argued in §2.3.6.

From a managerial perspective, it is interesting to see where causal relationships lie between

relational benefits and customer loyalty. This in order to explore which relational benefits

have potential in fostering customer loyalty and therefore need special attention by

marketers, since customer loyalty is the primary goal of customer relationship management.

The results of this study show that tour operators should intensify their personal relationships

with their clients, by making them feel personally recognized and creating an online

friendship. The online social bond proved to be the most important factor influencing

customer loyalty. The second most important factor seemed to be the enjoyment of having

an online relationship with a tour operator, which demands tour operators to focus on

triggering customers’ interest and to attempt to bring pleasure to their customers through

social media. Customers’ trust also proved to be influencing customer loyalty, so tour

operators must attempt to reduce the anxiety of customers by communicating their reliability

as well. Just to mention an example, customers can rely on their tour operator when these

59

take care of them in emergency situations. Providing information and special deals did not

prove to stimulate customer loyalty and should be less focused on when the aim of tour

operators is to make their customers loyal. But as stated in the previous chapter, one could

expect special deals to have an effect on brand awareness, which means that special deals

could be worth something. And so does providing information. Tour operators must give

customers the chance to compare their service with competitors’ service. Customers are

likely to make decisions based on the information they are being offered. However, the

personal relationship that tour operators can build with their clients though social media, in

order to increase loyalty, will insulate those customers from the competition at the same time.

60

Acknowledgements

I would like to thank those who have guided me through the process of writing this Master’s

thesis. First of all I would like to thank my promoter, Prof. Dr. Robert Govers, for his quick

response at all times and the feedback, support and suggestions he has given me the past

months. Next to him, I owe many thanks to Ph.D. student Bart Neuts, who always brought

me in the right direction when having trouble during the analysis.

61

References

Anderson, E. W. (1998). Customer Satisfaction and Word of Mouth. Journal of Service

Research, 1(1), 5-17.

Anderson, R. E. & Srinivasan, S. S. (2003). E-Satisfaction and e-Loyalty: A Contingency

Framework. Psychology and Marketing, 20(2), 123-138.

Arbuckle, J. L. (2011). IBM® SPSS® Amos™ 20 User’s Guide. Retrieved 01/25/2011 from

ftp://ftp.software.ibm.com/software/analytics/spss/documentation/amos/20.0/en/Manu

als/IBM_SPSS_Amos_User_Guide.pdf

Beatson, A. Lings, I. & Gudergan, S. (2008). Employee Behavior and Relationship Quality:

Impact on Customers. The Service Industries Journal, 28(2), 211-223.

Benner, J. (2009). The Airline Customer Loyalty Model - A relational approach to

understanding antecedents of customer loyalty in the airline industry. Unpublished

Master’s thesis, Copenhagen Business School.

Berry, L. L., Parasuraman, A. & Zeithaml, V. A. (1991). Understanding Customer

Expectations of Service. Sloan Management Review, 32(3), 39-48.

Blunch, N. J. (2008). Introduction to Structural Equation Modeling using SPSS and AMOS.

London, UK: SAGE Publications Ltd.

Bollen, K. A. (1989). Structural Equations with Latent Variables. NY, USA: Wiley.

Bollen, K. A. & Stine, R. A. (1992). Bootstrapping Goodness-of-Fit Measures in Structural

Equation Models. Sociological Methods Research, 21(2), 205-229.

Burnham, K. P., and D. R. Anderson (1998). Model selection and inference: A practical

information-theoretic approach. New York: Springer-Verlag.

Conze, O., Bieger, T., Laesser, C. & Riklin, T. (2010). Relationship Intention as a Mediator

between Relational Benefits and Customer Loyalty in the Tour Operator Industry.

Journal of Travel and Tourism Marketing, 27, 51-62.

Chang, Y. & Chen, F. (2007). Relational Benefits, Switching Barriers and Loyalty: A study of

Airline Customers in Taiwan. Journal of Air Transport Management, 13, 104-109.

62

Cyr, D., Hassaneinb, K., Headb, M. & Ivanovc, A. (2007). The Role of Social Presence in

establishing loyalty in e-Service Environments. Interacting with Computers, 19(1), 43-

56.

Czepiel, J. A. (1990). Service Encounters and Service Relationships: Implications for

Research. Journal of Business Research, 20, 13-21.

Davison, A. C. & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge,

UK: Cambridge University Press.

Dick, A. S. & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual

Framework. Academy of Marketing Science, 22(2), 99-113.

Dimitriadis, S. (2010). Testing Perceived Relational Benefits as Satisfaction and Behavioral

Outcome Drivers. International Journal of Bank Marketing, 28(4), 297-313.

Ellison, N. B., Steinfield, C. & Lampe, C. (2007). The Benefits of Facebook “Friends”: Social

Capital and College Students’ Use of Online Social Networking Sites. Journal of

Computer-mediated Communication, 12, 1143-1168.

Faché, W. (2000). Methodologies for innovation and improvement of services in tourism.

Managing Service Quality, 10(6), 356-366.

Field, A. (2006). Discovering Statistics Using SPSS (2nd ed.). London, UK: SAGE

Publications.

Gwinner, K. P., Gremler, D. D. & Bitner, M. J. (1998). Relational Benefits in Service

Industries: The Customer’s Perspective. Journal of the Academy of Marketing

Science, 26(2), 101-114.

Hallowell, R. (1996). The Relationships of Customer Satisfaction, Customer Loyalty and

Profitability: An Empirical Study. International Journal of Service Industry

Management, 7(4), 27-42.

Han, H. & Kim, W. (2009). Outcomes of Relational Benefits: Restaurant Customer’s

Perspective. Journal of Travel and Tourism Marketing, 26, 820-835.

Hanna, R., Rohm. A. & Crittenden, V. L. (2011). We’re All Connected: The Power of the

Social Media Ecosystem. Business Horizons, 54(3), 265-273.

63

Health, R. P. (1997). Loyalty for sale: Everybody’s doing frequency marketing - But only a

few companies are doing it well. Marketing Tools, 4(6), 40.

Hekkert, V. (2011). Master class Social Media. Unpublished presentation at Master class

Social Media in the Travel Trade, 08/25/2011, Geleen, NL: Travel Counsellors.

Hennig-Thurau, T. & Klee, A. (1997). The Impact of Customer Satisfaction and Relationship

Quality on Customer Retention: A Critical Reassessment and Model Development.

Psychology and Marketing, 14(8), 737-764.

Hennig-Thurau, T., Gwinner, K. P. & Gremler, D. D. (2002). Understanding Relationship

Marketing Outcomes: an Integration of Relational Benefits and Relationship Quality.

Journal of Service Research, 4(3), 230-247.

Heskett, J. L., Jones, T. O., Loveman, G. W., Sasser, W. E., Schlesinger, L. A. (1994).

Putting the Service-Profit Chain to Work. Harvard Business Review, mar-apr, 163-

174.

Hooper, D., Coughlan, J. & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines

for Determining Model Fit. Electric Journal of Business Research Methods, 6(1), 53-

60.

Kasavana, M. L., Nusair, K. & Teodosic, K. (2010). Online social networking: redefining the

human web. Journal of Hospitality and Tourism Technology, 1(1), 68-82.

Kim, W. (2009). Customers’ Responses to Customer Orientation of Service Employees in

Full-Service Restaurants: A Relational Benefits Perspective. Journal of Quality

Assurance in Hospitality and Tourism, 10, 153-174.

Litvin, S. W., Goldsmith, R. E. & Pan, B. (2007). Electronic Word-of-Mouth in Hospitality and

Tourism Management. Tourism Management, 29, 458-468.

Lee, Y., Ahn, W. & Kim, K. (2008). A Study on the Moderating Role of Alternative

Attractiveness in the Relationship between Relational Benefits and Customer Loyalty.

International Journal of Hospitality and Tourism Administration, 9(1), 52-70.

Macintosh, G. (2007). Customer Orientation, Relationship Quality, and Relational Benefits to

the firm. Journal of Services Marketing, 21(3), 150-159.

64

Meadows-Klue, D. (2008). Falling In Love 2.0: Relationship Marketing for the Facebook

Generation. Journal of Direct, Data and Digital Marketing Practice, 9(3), 245-250.

Miller, M. (2011). The Ultimate Web Marketing Guide. Indianapolis, Indiana USA: Que

Publishing.

Morgan, R. M. & Hunt, S. D. (1994). The Commitment-Trust Theory in Relationship

Marketing. Journal of Marketing, 58(3), 20-38.

Mulaik, S. A. & Millsap, R. E. (2000). Doing the Four-Step Right. Structural Equation

Modeling, 7(1), 36-73.

O’Reilly, K. & Paper, D. (2009). Stakeholder Perceptions Regarding eCRM: A Franchise

Case Study. The International Journal of an Emerging Transdiscipline, 12, 191-215.

Parra-López, E., Bulchand-Gidumal, J., Gutiérez-Taño, D. & Díaz-Armas, R. (2011).

Intentions to use Social Media in Organizing and Taking Vacation Trips. Computers in

Human Behavior, 27, 640-654.

Parasuraman, A., Berry, L. L. & Zeithaml, V. A. (1991). Understanding Customer

Expectations of Service. Sloan Management Review, 32(3), 39-48.

Park, C., & Kim, Y. (2009). The Effect of Information Satisfaction and Relational Benefit on

Consumer's On-Line Shopping Site Commitment. In S. Bandyopadhyay

(Ed.), Contemporary Research in E-Branding (pp. 292-312). Pennsylvania, USA: IGI

Global.

Paul, M., Hennig-Thureau, T., Gremler, D. D., Gwinner, K. P. & Wiertz, C. (2009). Toward a

Theory of Repeat Purchase Drivers for Consumer Services. Journal of the Academy

of Marketing Science, 37(2), 215-237.

Petrick, J. F. & Li, X. (2006). What drives Cruise Passengers‘ Perceptions of Value? In

Dowling, R. K. (Ed.), Cruise Ship Tourism (p.63-73). Oxfordshire, UK: CABI.

Pritchard, M. P., Havitz, M. E. & Howard, D. R. (1999). Analyzing the Commitment-Loyalty

Link in Service Contexts. Journal of the Academy of Marketing Science, 27(3), 333-

348.

Prokesch, S. E. (1995). Competing on Customer Service: An Interview with British Airways’

Sir Colin Marshall. Harvard Business Review, 73, 1-18.

65

Raftery, A. E. (1995). Bayesian model selection in structural equation models. In: Testing

structural equation models, K. A. Bollen and J. S. Long, eds. Newbury Park, CA:

Sage Publications, 163–180.

Reynolds, K. E. & Beatty, S. E. (1999). Customer Benefits and Company Consequences of

Customer-Salesperson Relationships in Retailing. Journal of Retailing, 75(1), 11-32.

Ruiz-Molina, M., Gil-Saura, I. & Berenguer-Contrí, G. (2009). Relational Benefits and Loyalty

in Retailing: An Inter-Sector Comparison. International Journal of Retail and

Distribution Management, 37(6), 493-509.

Schumacker, R. E. & Lomax, R. G. (2004). A Beginner’s Guide to Structural Equation

Modeling. (2nd ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.

Sharma, S. (1996). Applied Multivariate Techniques. NY, USA: John Wiley & Sons.

Stevens, J. (1996). Applied multivariate statistics for the social sciences. Mahwah, NJ:

Lawrence Erlbaum Publishers.

Su, Q., Li, L. & Cui, Y. W. (2009). Analyzing Relational Benefits in e-Business Environment

from Behavioral Perspective. Systems Research and Behavioral Science, 26, 129-

142.

Sung-Bum, K. & Dae-Young, K. (2010). Travel Information Search Behavior and Social

Networking Sites: The Case of U.S. College Students. Retrieved 06/28/2011 from

http://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1134&context=gradconf_h

ospitality&sei-redir=1

Tabachnick, B. G. & Fidell, L. S. (1996). Using Multivariate Statistics. NY, USA:

HarperCollins.

Travel Magazine (2011a). Voordeel halen uit de Social Media. Travel Magazine, 300, 86-87.

Travel Magazine (2011b). Social Media boost de verkoop. Travel Magazine, 300, 88.

Travel Magazine (2011c). De reisagent: een organische vorm van Facebook. Travel

Magazine, 300, 115.

Vennix, J. (2007). Theorie en praktijk van empirisch onderzoek. Pearson education limited,

Edinburgh, UK.

66

Vogt, C. A. & Fesenmaier, D. R. (1998). Expanding the Functional Information Search Model.

Annals of Tourism Research, 25(3), 551-578.

Vogt, C. A. (2011). Customer Relationship Management in Tourism: Management Needs

and Research Applications. Journal of Travel Research, 50(4), 356-364.

Wang, Y. & Fesenmaier, D. (2004). Towards understanding Member’s General Participation

in and Active Contribution to an Online Travel Community. Tourism Management, 25,

709-722.

Westbrook, R. A. (1987). Product/consumption-based affective responses and post-purchase

processes. Journal of Marketing Research, 24(3), 258–270.

Wijnen, K., Janssens, W., De Pelsmacker, P. & Van Kenhove, P. (2002). Marktonderzoek

met SPSS: Statistische Verwerking en Interpretatie. Antwerp, Belgium: Garant-

Uitgevers n.v.

Xiang, Z. & Gretzel, U. (2010). Role of Social Media in Online Travel Information Search.

Tourism Management, 31, 179-188.

Yen, H. J. R. & Gwinner, K. P. (2003). Internet Retail Customer Loyalty: the Mediating Role

of Relational Benefits. International Journal of Service Industry Management, 14(5),

483-500.

Zarella, D. & Zarella, A. (2011). The Facebook Marketing Book. Sebastopol, California USA:

O’Reilly Media Inc.

67

Appendices

Appendix 1: Measurement scales reviewed for operationalization of

constructs

Table 6: Overview of consulted studies for the operationalization of constructs

Reference Research topic

Anderson & Srinivasan

(2003)

The authors investigate the impact of satisfaction on loyalty in the

context of electronic commerce.

Benner (2009) The objective of this thesis is to contribute to the understanding of drivers

of customer loyalty by exploring the dynamics of customer‐brand

relationships and the role they play for the creation and management of

customer loyalty in the airline industry.

Chang & Chen (2007) This research proposes a model explaining switching barriers and

customer loyalty stemming from customer relational benefits.

Cyr et al. (2007) In this paper a model for e-Loyalty is proposed and used to examine how

varied conditions of social presence in a B2C e-Services context influence

e-Loyalty and its antecedents of perceived usefulness, trust and

enjoyment.

Gwinner et al. (1998) This research examines the benefits customers receive as a result of

engaging in long-term relational exchanges with service firms.

Han & Kim (2009) This study was designed to propose and test a behavioral intention model

by incorporating these constructs in a full-service restaurant setting.

Hennig-Thurau et al. (2002) This article integrates customer loyalty and word of mouth by positioning

customer satisfaction and commitment as relationship quality dimensions

that partially mediate the relationship between three relational benefits

(confidence benefits, social benefits, and special treatment benefits) and

the two outcome variables.

Kim (2009) This study was designed to investigate how the customer orientation of

service employees (COSE) affects customers’ perceptions of relational

benefits and ultimately contributes to repurchase intention in the full-

service restaurant context.

Macintosh (2007) This research seeks to test a model examining the potential links between

customer orientation, expertise, and relationship quality at the

interpersonal level and the link between relationship quality and positive

service outcomes at the firm level, such as loyalty and positive word of

mouth.

68

Overview of consulted studies for the operationalization of constructs (continued)

Reference Research topic

Parra-López et al. (2011) This study proposes a theoretical model to explain the factors determining

the intentions to use social media when organizing and taking vacation

trips.

Paul et al. (2009) This research attempts to overcome that fragmented state of knowledge by

making major advances toward a theory of repeat purchase drivers for

consumer services.

Reynolds & Beatty (1999) This study examines the benefits customers receive from relationships with

clothing/accessories salespeople.

Ruiz-Molina et al. (2009) The purpose of this paper is to empirically test a model that reflects the

different types of relational benefits perceived by customers, as well as the

benefits obtained by the organization in terms of customer loyalty.

Su et al. (2009) This paper explores relational benefits from Chinese customer’s

behavioural perspective.

Vogt & Fesenmaier (1998) This study used a decision-making and information search model as a

framework for explaining the factors which influence the use of

communications as they relate to recreation and tourism experiences.

Yen & Gwinner (2003) This paper proposes a conceptual framework that utilizes the construct of

relational benefits to explain the link between Internet-based self-service

technology attributes and customer loyalty and satisfaction.

Zhang & Bloemer (2008) The authors develop and test a model that explains how value congruence

affects the key components of consumer-brand relationship quality and

outcomes, including satisfaction, trust, affective commitment, and loyalty.

69

Table 7: Studies consulted with respect to "Social Benefits"

Reference Items

Chang & Chen (2007) I enjoy certain social aspects of the relationship

Some airline employees know my name

I have developed friendships with certain airline employees

Gwinner et al. (1998);

Hennig-Thurau et al. (2002);

Ruiz-Molina et al. (2009)

I am recognized by certain employees

I enjoy certain social aspects of the relationship

I have developed a friendship with the service provider

I am familiar with the employees that perform the services

They know my name

Han & Kim (2009) I feel like there is a bond between this restaurant and me

I am familiar with employee(s) who perform(s) services

At least one of the staff at this restaurant knows me

Kim (2009) Based on all my experiences with the restaurant…

…I am recognized by (a) certain employee(s)

…I am familiar with the employee(s) who provide(s) the service

…I have developed a friendship with the employee(s)

…the employee(s) know(s) my name

Reynolds & Beatty (1999) The friendship aspect of my relationship with my sales associate is very

important to me

I enjoy spending time with my sales associate

I value the close, personal relationship I have with my sales associate

I enjoy my sales associate’s company

70

Table 8: Consulted studies with respect to "Confidence Benefits"

Reference Items

Chang & Chen (2007)

I feel I can trust this airline

I am not worried when I fly on this airline

I am confident that the service will be performed correctly by this airline

Gwinner et al. (1998);

Ruiz-Molina et al. (2009)

I believe there is less risk that something will go wrong

I feel I can trust the service provider

I have more confidence the service will be performed correctly

I have less anxiety when I buy the service

I know what to expect when I go in

I get the provider’s highest level of service

Han & Kim (2009) I have confidence that this restaurant provides the best deal

I feel I can trust this restaurant

I have more confidence that services at this restaurant will be performed

correctly

Hennig-Thurau et al. (2002) I know what to expect when I go in

This company’s employees are perfectly honest and truthful

This company’s employees can be trusted completely

This company’s employees have high integrity

Kim (2009) Based on all my experiences with the restaurant…

…I believe there is less risk that something will go wrong

…I feel I can trust the employee(s)

…I have more confidence the service will be performed correctly

…I have less anxiety when I decide to dine out at this restaurant

Yen & Gwinner (2003) I can trust this Web-based travel agency

This Web-based travel agency can free me from anxiety concerning the

security of online transactions

I know what to expect when I get on to the Web site of this travel agency

71

Table 9: Consulted studies with respect to "Functional Benefits"

Reference Items

Parra-López et al. (2011) Social media tools enable me to keep up to date with knowledge about the

tourist sites and activities of interest

Social media tools permit me to save costs and get the most from the

resources invested in the trip

Social media tools give me the possibility to provide and to receive

information about tourist sites and activities of interest

Paul et al. (2009) The customer benefits because s/he gains expert knowledge and

information about the service

Reynolds & Beatty (1999)

I value the convenience benefits my sales associate provides me very

highly

I value the time saving benefits my sales associate provides me very

highly

I benefit from the advice my sales associate gives me

I make better purchase decisions because of my sales associate

72

Table 10: Consulted studies with respect to "Special Treatment Benefits"

Reference Items

Chang & Chen (2007) I can get faster service if necessary

I am placed higher on the stand-by list when the flight is full

This airline will manage to give me a seat when the flight is full

This airline will upgrade my seat when possible

Gwinner et al. (1998);

Hennig-Thurau et al. (2002);

Ruiz-Molina et al. (2009)

I get discounts or special deals that most customers don’t get

I get better prices than most customers

They do services for me that they don’t do for most customers

I am placed higher on the priority list when there is a line

I get faster service than most customers

Han & Kim (2009) This restaurant provides me reliable benefit programs and services

I feel staff at this restaurant treat me special

Kim (2009) Based on all my experiences with the restaurant…

…I receive better prices or special deals that most customers don’t

…they provide services to me that they don’t provide to most other

customers

…I receive faster service than most other customers

…they pay extra attention to my needs

Yen & Gwinner (2003) I am able to save a lot of time on information searching when I browse and

order through this travel agency

I got extra services (e.g. member-only chat room, special offer and

frequent user program) from using this Internet travel agency

73

Table 11: Consulted studies with respect to "Hedonic Benefits"

Reference Items

Cyr et al. (2007)

Perceived enjoyment

I found my visit to this website interesting

I found my visit to this website entertaining

I found my visit to this website enjoyable

I found my visit to this website pleasant

Vogt & Fesenmaier (1998)

Hedonic needs

Emotional

Excite myself about travel

Be entertained

Excite myself with unique cultures

Sensory

Hear the sounds of the ocean

Smell the fresh air

Taste those foods I discover

Experiential

Experience the local culture

Realize experiences that I think about

Phenomenology

Understand the personality of a community

Wonder about daily life of area

74

Table 12: Consulted studies with respect to "Customer Satisfaction"

Reference Items

Anderson & Srinivasan

(2003)

I am satisfied with my decision to purchase from this Web site

If I had to purchase again, I would feel differently about buying from this

Web site

My choice to purchase from this Web site was a wise one

I feel badly regarding my decision to buy from this Web site

I think I did the right thing by buying from this Web site

I am unhappy that I purchased from this Web site

Benner (2009) My experiences with this airline exceed my expectations

Hennig-Thurau et al. (2002) My choice to use this company was a wise choice

I am always delighted with this firm’s service

Overall, I am satisfied with this organization

I think I did the right thing when I decided to use this firm

Reynolds and Beatty (1999) Please indicate your feeling with respect to your sales associate at

“company name”

Pleased – displeased (1-7)

Unhappy – happy (1-7)

Disgusted – contented (1-7)

Frustrating – enjoyable (1-7)

Please indicate your feelings with respect to shopping at “company name”

Pleased – displeased (1-7)

Unhappy – happy (1-7)

Disgusted – contented (1-7)

Frustrating – enjoyable (1-7)

Yen & Gwinner (2003) In general, I am satisfied with the service quality offered by this Internet

travel agency

I feel satisfied with the self-service interface of this Internet travel agency

Zhang & Bloemer (2008) Compared to other banks, I am very satisfied with X

Based on all my experience with X, I am very satisfied

My experiences at X have always been pleasant

Overall, I am satisfied with X

75

Table 13: Consulted studies with respect to "Customer Loyalty"

Reference Items

Anderson & Srinivasan

(2003)

I seldom consider switching to another Web site

As long as the present service continues, I doubt that I would switch Web

sites

I try to use the Web site whenever I need to make a purchase

When I need to make a purchase, this Web site is my first choice

I like using the Web site

To me this site is the best retail Web site to do business with

I believe that this is my favorite retail Web site

Cyr et al. (2007) I would use this website again

I would consider purchasing from this website in the future

I would consider using this website in the future

Hennig-Thurau et al. (2002) I have a very strong relationship with this service provider

I am very likely to switch to another service provider in the near future

Reynolds and Beatty (1999) I am very loyal to my sales associate at “company name”

I don’t plan to shop with my sales associate at “company name” in the

future

I am very committed to my sales associate at “company name”

I don’t consider myself very loyal to my sales associate at “company name”

I am very loyal to “company name”

I am very committed to “company name”

I don’t consider myself a loyal “company name” customer

I don’t plan to shop at “company name” in the future

Ruiz-Molina et al. (2008) As long as the present service continues, I doubt that I would switch store

I try to use the store whenever I need to make a purchase

When I need to make a purchase, this store is my first choice

I like using this store

To me this store is the best store to do business with

In comparison to other stores, I would consider this store as excellent

Yen & Gwinner (2003) This Internet travel agency is my first choice for my next purchase of travel

services

I will continue to purchase from this Internet travel agency

76

Table 14: Consulted studies with respect to "Relationship Commitment"

Reference Items

Hennig-Thurau et al. (2002) My relationship to this specific service provider is something that I am very

committed to

My relationship to this specific service provider is very important to me

My relationship to this specific service provider is something I really care

about

My relationship to this specific service provider deserves my maximum

effort to maintain

Table 15: Consulted studies with respect to "Word of Mouth"

Reference Items

Hennig-Thurau et al. (2002) I often recommend this service provider to others

Macintosh (2005)

I have talked to co-workers and friends about my experience with (firm)

Never = 0

Once or twice = 1

Several times = 2

Many times = 3

If you talked to co-workers or friends about (firm), your comments were

generally

Very negative = -2

Negative = -1

Neutral = 0

Positive = 1

Very positive = 2

Su et al. (2009) I often recommend this firm to my friends

I will introduce this firm to others

77

Table 16: Measurement Items included in questionnaire (Likert scales 1-7)

Model construct Measurement item Based on

Social Benefits Ik word herkend door reisorganisatie X op hun

Facebookpagina

Gwinner et al. (1998);

Hennig-Thurau et al. (2002)

Ik vind de sociale aspecten van mijn relatie met

reisorganisatie X op Facebook leuk

Via Facebook heb ik een vriendschapsrelatie

ontwikkeld met reisorganisatie X

Ik ben bekend met de Facebookpagina van

reisorganisatie X

Mijn naam is bekend bij reisorganisatie X op Facebook

Confidence

Benefits

Ik geloof dat er weinig risico is dat mijn privacy via

Facebook geschonden wordt door reisorganisatie X

Gwinner et al. (1998)

Ik kan de informatie op de Facebookpagina van

reisorganisatie X vertrouwen

Ik heb er vertrouwen in dat reisorganisatie X een

goede dienst verleent op Facebook

Door mijn relatie op Facebook met reisorganisatie X,

heb ik minder angst bij het boeken van een reis

Door de Facebookpagina van reisorganisatie X weet ik

wat ik van de organisatie kan verwachten

Op Facebook krijg ik de best mogelijke service van

reisorganisatie X

Functional

Benefits

De Facebookpagina van reisorganisatie X biedt mij

gemak

Reynolds & Beatty (1999)

Ik haal voordeel uit het advies dat ik krijg via de

Facebookpagina van reisorganisatie X

Ik kan betere beslissingen nemen bij het boeken van

mijn reis door het gebruik van de Facebookpagina van

reisorganisatie X

Reisorganisatie X houdt mij via Facebook op de

hoogte van interessante reizen en bestemmingen

Parra-López et al. (2011)

78

Measurement Items included in questionnaire (continued)

Model construct Measurement item Based on

Special

Treatment

Benefits

Via Facebook ontvang ik kortingen en speciale deals

van reisorganisatie X die de meeste klanten niet

krijgen

Gwinner et al. (1998);

Hennig-Thurau et al. (2002)

Via de Facebookpagina van reisorganisatie X krijg ik

betere prijzen dan de meeste klanten

Via Facebook ontvang ik een service van

reisorganisatie X die veel andere klanten niet krijgen

Indien nodig, kan ik via Facebook snellere service

krijgen van reisorganisatie X

Chang & Chen (2007)

Door middel van de Facebookpagina van

reisorganisatie X bespaar ik tijd bij het zoeken naar

informatie

Yen & Gwinner (2003)

Hedonic Benefits Ik vind mijn bezoek aan de Facebookpagina van

reisorganisatie X telkens interessant

Cyr et al. (2007)

Ik vind mijn bezoek aan de Facebookpagina van

reisorganisatie X amusant

Het bezoeken van de Facebookpagina van

reisorganisatie X verblijdt mij

Ik vind mijn bezoek aan de Facebookpagina van

reisorganisatie X telkens prettig

Door de Facebookpagina van reisorganisatie X krijg ik

zin om te reizen

Vogt & Fesenmaier (1998)

Customer e-

Satisfaction

Vergeleken met andere Facebookpagina’s van

reisorganisaties, ben ik erg tevreden over de

Facebookpagina van reisorganisatie X

Zhang & Bloemer (2008)

Op basis van al mijn ervaringen met de

Facebookpagina van reisorganisatie X, ben ik tevreden

over hun dienstverlening

Mijn ervaringen met de Facebookpagina van

reisorganisatie X zijn altijd prettig

Over het algemeen ben ik tevreden over de

Facebookpagina van reisorganisatie X

De Facebookpagina van reisorganisatie X gaat mijn

verwachtingen te boven

(Benner, 2009)

79

Measurement Items included in questionnaire (continued)

Model construct Measurement item Based on

Customer

Satisfaction with

Tour Operator

Vergeleken met andere reisorganisaties, ben ik erg

tevreden over reisorganisatie X in het algemeen

Zhang & Bloemer (2008)

Op basis van al mijn ervaringen met reisorganisatie X,

ben ik erg tevreden

Mijn ervaringen met reisorganisatie X zijn altijd prettig

Over het algemeen ben ik tevreden over

reisorganisatie X

Reisorganisatie X gaat in het algemeen mijn

verwachtingen te boven

Benner ( 2009)

Customer e-

Loyalty

Ik ben erg loyaal aan de Facebookpagina van

reisorganisatie X

Reynolds and Beatty (1999)

Ik ben van plan om binnenkort weer gebruik te maken

van de Facebookpagina van reisorganisatie X

Ik voel me erg verbonden met de Facebookpagina van

reisorganisatie X

Ik beschouw mezelf erg loyaal aan de Facebookpagina

van reisorganisatie X

Customer Loyalty

to Tour Operator

In het algemeen ben ik erg loyaal aan reisorganisatie X

Reynolds and Beatty (1999)

Ik ben van plan om binnenkort weer gebruik te maken

van reisorganisatie X

Ik ben in het algemeen erg verbonden met

reisorganisatie X

Ik beschouw mezelf een loyale klant van

reisorganisatie X

Relationship

Commitment

Ik ben gehecht aan mijn relatie met reisorganisatie X

Hennig-Thurau et al. (2002)

De relatie met reisorganisatie X is belangrijk voor me

Ik geef veel om de relatie met reisorganisatie X

Mijn relatie met reisorganisatie X verdient mijn

maximale inspanning om te onderhouden

80

Measurement Items included in questionnaire (continued)

Model construct Measurement item Based on

Word of Mouth Ik raad reisorganisatie X vaak aan anderen aan Hennig-Thurau et al. (2002)

Wanneer ik met vrienden of collega’s spreek over

reisorganisatie X, ben ik

1 = zeer negatief

2 = negatief

3 = eerder negatief

4 = neutraal

5 = eerder positief

6 = positief

7 = zeer positief

Macintosh (2005)

Ik ben van plan reisorganisatie X bij anderen te

introduceren

Su et al. (2009)

81

Appendix 2: Output used to check assumptions

Normality tests

Tests of univariate normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

SocialBenefits .063 157 .200* .983 157 .048

ConfidenceBenefits .056 157 .200* .985 157 .090

FunctionalBenefits .113 157 .000 .966 157 .001

SpecialTreatmentBenefits .064 157 .200* .985 157 .093

HedonicBenefits .084 157 .009 .957 157 .000

eSatisfaction .072 157 .046 .954 157 .000

Satisfaction .106 157 .000 .931 157 .000

eLoyalty .101 157 .000 .976 157 .008

Loyalty .083 157 .011 .966 157 .001

RelationshipCommitment .096 157 .001 .967 157 .001

WOM .104 157 .000 .917 157 .000

a. Lilliefors Significance Correction

*. This is a lower bound of the true significance.

Mardia’s coefficient

Kurtosis Critical ratio

Multivariate 217.743 18.917

82

Scatter plots

83

84

85

86

87

88

89

90

Levene’s Test

Test of Homogeneity of Variances

Social

Benefits

Levene

Statistic df1 df2 Sig.

eSatisfaction 3.199 23 128 .000

eLoyalty 1.321 23 128 .167

Commitment 1.955 23 128 .010

Test of Homogeneity of Variances

Confidence

Benefits

Levene

Statistic df1 df2 Sig.

eSatisfaction 2.763 25 128 .000

eLoyalty 1.367 25 128 .133

Commitment 2.157 25 128 .003

91

Test of Homogeneity of Variances

Functional

Benefits

Levene

Statistic df1 df2 Sig.

eSatisfaction 1.304 18 136 .194

eLoyalty 1.056 18 136 .403

Commitment 1.553 18 136 .081

Test of Homogeneity of Variances

Special

Treatment

Benefits

Levene

Statistic df1 df2 Sig.

eSatisfaction 2.869 23 130 .,000

eLoyalty .807 23 130 .718

Commitment 1.429 23 130 .109

Test of Homogeneity of Variances

Hedonic

Benefits

Levene

Statistic df1 df2 Sig.

eSatisfaction 1.190 19 134 .275

eLoyalty 1.447 19 134 .115

Commitment 1.834 19 134 .025

Test of Homogeneity of Variances

Customer e-

Satisfaction

Levene

Statistic df1 df2 Sig.

Satisfaction 3.435 20 134 .000

eLoyalty .932 20 134 .548

Commitment 1.542 20 134 .077

WOM 2.805 20 134 .000

92

Test of Homogeneity of Variances

Customer

Satisfaction Levene Statistic df1 df2 Sig.

Loyalty 1.525 18 132 .091

Commitment .831 18 132 .662

WOM 3.599 18 132 .000

Test of Homogeneity of Variances

Customer e-

Loyalty

Levene

Statistic df1 df2 Sig.

Loyalty 4,184 19 135 ,000

Test of Homogeneity of Variances

Relationship

Commitment

Levene

Statistic df1 df2 Sig.

Loyalty 1.498 21 133 .088

93

Durbin-Watson Test

Model Summaryb

Model Durbin-Watson

1.978a

a. Predictors: (Constant), HedonicBenefits,

SocialBenefits, SpecialTreatmentBenefits,

ConfidenceBenefits, FunctionalBenefits

b. Dependent Variable: eSatisfaction

Model Summaryb

Model Durbin-Watson

1.950a

a. Predictors: (Constant), eSatisfaction,

Satisfaction, SocialBenefits,

SpecialTreatmentBenefits, HedonicBenefits,

ConfidenceBenefits, FunctionalBenefits

b. Dependent Variable: RelationshipCommitment

Model Summaryb

Model Durbin-Watson

1.738a

a. Predictors: (Constant), Satisfaction, eLoyalty,

RelationshipCommitment

b. Dependent Variable: Loyalty

Model Summaryb

Model Durbin-Watson

2.293a

a. Predictors: (Constant), eSatisfaction,

SocialBenefits, SpecialTreatmentBenefits,

HedonicBenefits, ConfidenceBenefits,

FunctionalBenefits

b. Dependent Variable: eLoyalty

Model Summaryb

Model Durbin-Watson

1.927a

a. Predictors: (Constant), eSatisfaction

b. Dependent Variable: Satisfaction

Model Summaryb

Model Durbin-Watson

1.922a

a. Predictors: (Constant),

RelationshipCommitment, eSatisfaction,

Satisfaction

b. Dependent Variable: WOM

94

VIF values

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

ConfidenceBenefits .347 2.882

FunctionalBenefits .273 3.666

SpecialTreatmentBenefits .446 2.241

HedonicBenefits .378 2.644

eSatisfaction .233 4.287

Satisfaction .256 3.911

eLoyalty .266 3.756

Loyalty .227 4.415

RelationshipCommitment .309 3.240

WOM .246 4.060

a. Dependent Variable: SocialBenefits

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

FunctionalBenefits .320 3127

SpecialTreatmentBenefits .446 2.243

HedonicBenefits .378 2.643

eSatisfaction .234 4.274

Satisfaction .256 3.901

eLoyalty .269 3.721

Loyalty .224 4.459

RelationshipCommitment .292 3.430

WOM .248 4.025

SocialBenefits .480 2.082

a. Dependent Variable: ConfidenceBenefits

95

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

SpecialTreatmentBenefits .534 1.873

HedonicBenefits .392 2.552

eSatisfaction .247 4.056

Satisfaction .251 3.984

eLoyalty .266 3.764

Loyalty .229 4.363

RelationshipCommitment .291 3.433

WOM .248 4.028

SocialBenefits .444 2.252

ConfidenceBenefits .376 2.660

a. Dependent Variable: FunctionalBenefits

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

HedonicBenefits .378 2.644

eSatisfaction .247 4.052

Satisfaction .252 3.963

eLoyalty .266 3.766

Loyalty .225 4.449

RelationshipCommitment .291 3.433

WOM .246 4.058

SocialBenefits .449 2.226

ConfidenceBenefits .324 3.084

FunctionalBenefits .330 3.028

a. Dependent Variable: SpecialTreatmentBenefits

96

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

eSatisfaction .249 4.014

Satisfaction .251 3.986

eLoyalty .291 3.437

Loyalty .224 4.458

RelationshipCommitment .291 3.433

WOM .246 4.059

SocialBenefits .443 2.256

ConfidenceBenefits .320 3.123

FunctionalBenefits .282 3.546

SpecialTreatmentBenefits .440 2.272

a. Dependent Variable: HedonicBenefits

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

Satisfaction .257 3.884

eLoyalty .286 3.501

Loyalty .226 4.430

RelationshipCommitment .294 3.407

WOM .246 4.059

SocialBenefits .443 2.255

ConfidenceBenefits .321 3.113

FunctionalBenefits .288 3.473

SpecialTreatmentBenefits .466 2.146

HedonicBenefits .404 2.474

a. Dependent Variable: eSatisfaction

97

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

eLoyalty .266 3.758

Loyalty .242 4.124

RelationshipCommitment .293 3.412

WOM .335 2.987

SocialBenefits .452 2.214

ConfidenceBenefits .327 3.057

FunctionalBenefits .272 3.671

SpecialTreatmentBenefits .443 2.259

HedonicBenefits .378 2.644

eSatisfaction .239 4.180

a. Dependent Variable: Satisfaction

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

Loyalty .225 4.448

RelationshipCommitment .305 3.276

WOM .246 4.059

SocialBenefits .445 2.247

ConfidenceBenefits .324 3.082

FunctionalBenefits .273 3.665

SpecialTreatmentBenefits .441 2.269

HedonicBenefits .415 2.409

eSatisfaction .251 3.982

Satisfaction .252 3.972

a. Dependent Variable: eLoyalty

98

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

RelationshipCommitment .365 2.737

WOM .256 3.899

SocialBenefits .448 2.232

ConfidenceBenefits .320 3.120

FunctionalBenefits .279 3.589

SpecialTreatmentBenefits .442 2.264

HedonicBenefits .379 2.640

eSatisfaction .235 4.256

Satisfaction .272 3.682

eLoyalty .266 3.757

a. Dependent Variable: Loyalty

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

WOM .257 3.886

SocialBenefits .470 2.130

ConfidenceBenefits .320 3.121

FunctionalBenefits .272 3.673

SpecialTreatmentBenefits .440 2.272

HedonicBenefits .378 2.644

eSatisfaction .235 4.256

Satisfaction .252 3.962

eLoyalty .278 3.599

Loyalty .281 3.559

a. Dependent Variable: RelationshipCommitment

99

Coefficientsa

Model

Collinearity Statistics

Tolerance VIF

SocialBenefits .444 2.254

ConfidenceBenefits .323 3.094

FunctionalBenefits .275 3.640

SpecialTreatmentBenefits .441 2.269

HedonicBenefits .379 2.641

eSatisfaction .233 4.285

Satisfaction .341 2.930

eLoyalty .265 3.767

Loyalty .233 4.283

RelationshipCommitment .305 3.283

a. Dependent Variable: WOM

100

Appendix 3: Output used during exploratory factor analysis

Kaiser-Meyer-Olkin and Bartlett's Test

KMO and Bartlett’s test for each latent variable

Latent variable

#

Items

KMO Bartlett’s test

of sphericity

Sig.

Social Benefits 5 .822 .000

Confidence Benefits 6 .825 .000

Functional Benefits 4 .780 .000

Special Treatment Benefits 5 .832 .000

Hedonic Benefits 5 .829 .000

Customer e-Satisfaction 5 .857 .000

Customer Satisfaction with TO 5 .876 .000

Customer e-Loyalty 4 .801 .000

Customer Loyalty to TO 4 .861 .000

Relationship Commitment 4 .842 .000

WOM 3 .761 .000

Total variance explained

Total Variance Explained for Social Benefits

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total

% of

Variance Cumulative % Total % of Variance Cumulative %

1 3.013 60.263 60.263 3.013 60.263 60.263

2 .739 14.783 75.047

3 .510 10.204 85.250

4 .381 7.621 92.871

5 .356 7.129 100.000

Extraction Method: Principal Component Analysis.

101

Total Variance Explained for Confidence Benefits

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total

% of

Variance Cumulative % Total % of Variance Cumulative %

1 3.348 55.808 55.808 3.348 55.808 55.808

2 .993 16.556 72.363

3 .559 9.313 81.676

4 .442 7.374 89.051

5 .354 5.907 94.958

6 .303 5.042 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Functional Benefits

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total

% of

Variance Cumulative % Total % of Variance Cumulative %

1 2.570 64.257 64.257 2.570 64.257 64.257

2 .608 15.193 79.451

3 .474 11.843 91.293

4 .348 8.707 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Special Treatment Benefits

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.367 67.349 67.349 3.367 67.349 67.349

2 .765 15.305 82.655

3 .313 6.265 88.920

4 .305 6.098 95.018

5 .249 4.982 100.000

Extraction Method: Principal Component Analysis.

102

Total Variance Explained for Hedonic Benefits

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.471 69.430 69.430 3.471 69.430 69.430

2 .695 13.896 83.326

3 .342 6.839 90.165

4 .300 5.997 96.162

5 .192 3.838 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Customer e-Satisfaction

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.494 69.881 69.881 3.494 69.881 69.881

2 .536 10.724 80.605

3 .427 8.544 89.149

4 .313 6.262 95.412

5 .229 4.588 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Customer Satisfaction

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.860 77.191 77.191 3.860 77.191 77.191

2 .520 10.399 87.590

3 .364 7.282 94.872

4 .141 2.820 97.692

5 .115 2.308 100.000

Extraction Method: Principal Component Analysis.

103

Total Variance Explained for Customer e-Loyalty

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.229 80.721 80.721 3.229 80.721 80.721

2 .378 9.454 90.175

3 .296 7.401 97.576

4 .097 2.424 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Customer Loyalty

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.331 83.267 83.267 3.331 83.267 83.267

2 .250 6.247 89.514

3 .248 6.205 95.719

4 .171 4.281 100.000

Extraction Method: Principal Component Analysis.

Total Variance Explained for Relationship Commitment

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.511 87.774 87.774 3.511 87.774 87.774

2 .252 6.288 94.062

3 .154 3.841 97.903

4 .084 2.097 100.000

Extraction Method: Principal Component Analysis.

104

Total Variance Explained for Word of Mouth

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.594 86.470 86.470 2.594 86.470 86.470

2 .224 7.454 93.923

3 .182 6.077 100.000

Extraction Method: Principal Component Analysis.

Scree Plots

Social Benefits

Confidence Benefits

105

Functional Benefits

Special Treatment Benefits

Hedonic Benefits

106

Customer e-Satisfaction

Customer Satisfaction

Customer e-Loyalty

107

Customer Loyalty

Relationship Commitment

Word of Mouth

108

Component Matrices

Component Matrixa for Social Benefits

Component

1

Ik word herkend door reisorganisatie X op hun Facebookpagina .794

Ik vind de sociale aspecten van mijn relatie met reisorganisatie X op Facebook leuk .802

Via Facebook heb ik een vriendschapsrelatie ontwikkeld met reisorganisatie X .812

Ik ben bekend met de Facebookpagina van reisorganisatie X .637

Mijn naam is bekend bij reisorganisatie X op Facebook .822

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Confidence Benefits

Component

1

Ik geloof dat er weinig risico is dat mijn privacy via Facebook geschonden wordt door

reisorganisatie X

.613

Ik kan de informatie op de Facebookpagina van reisorganisatie X vertrouwen .730

Ik heb er vertrouwen in dat reisorganisatie X een goede dienst verleent op Facebook .832

Door mijn relatie op Facebook met reisorganisatie X, heb ik minder angst bij het boeken van

een reis

.779

Door de Facebookpagina van reisorganisatie X weet ik wat ik van de organisatie kan

verwachten

.741

Op Facebook krijg ik de best mogelijke service van reisorganisatie X .768

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

109

Component Matrixa for Functional Benefits

Component

1

De Facebookpagina van reisorganisatie X biedt mij gemak .817

Ik haal voordeel uit het advies dat ik krijg via de Facebookpagina van reisorganisatie X .852

Ik kan betere beslissingen nemen bij het boeken van mijn reis door het gebruik van de

Facebookpagina van reisorganisatie X

.789

Reisorganisatie X houdt mij via Facebook op de hoogte van interessante reizen en

bestemmingen

.744

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Special Treatment Benefits

Component

1

Via Facebook ontvang ik kortingen en speciale deals van reisorganisatie X die de meeste

klanten niet krijgen

.735

Via de Facebookpagina van reisorganisatie X krijg ik betere prijzen dan de meeste klanten .886

Via Facebook ontvang ik een service van reisorganisatie X die veel andere klanten niet

krijgen

.885

Indien nodig, kan ik via Facebook snellere service krijgen van reisorganisatie X .848

Door middel van de Facebookpagina van reisorganisatie X bespaar ik tijd bij het zoeken

naar informatie

.735

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

110

Component Matrixa for Hedonic Benefits

Component

1

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens interessant .801

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X amusant .839

Het bezoeken van de Facebookpagina van reisorganisatie X verblijdt mij .890

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens prettig .905

Door de Facebookpagina van reisorganisatie X krijg ik zin om te reizen .718

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Customer e-Satisfaction

Component

1

Vergeleken met andere Facebookpagina’s van reisorganisaties, ben ik erg tevreden over

de Facebookpagina van reisorganisatie X

.809

Op basis van al mijn ervaringen met de Facebookpagina van reisorganisatie X, ben

ik tevreden over hun dienstverlening

.864

Mijn ervaringen met de Facebookpagina van reisorganisatie X zijn altijd prettig .864

Over het algemeen ben ik tevreden over de Facebookpagina van reisorganisatie X .847

De Facebookpagina van reisorganisatie X gaat mijn verwachtingen te boven .793

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Customer Satisfaction

Component

1

Vergeleken met andere reisorganisaties, ben ik erg tevreden over reisorganisatie X in het

algemeen

.842

Op basis van al mijn ervaringen met reisorganisatie X, ben ik erg tevreden .934

Mijn ervaringen met reisorganisatie X zijn altijd prettig .930

Over het algemeen ben ik tevreden over reisorganisatie X .926

Reisorganisatie X gaat in het algemeen mijn verwachtingen te boven .745

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

111

Component Matrixa for Customer e-Loyalty

Component

1

Ik ben erg loyaal aan de Facebookpagina van reisorganisatie X .881

Ik ben van plan om binnenkort weer gebruik te maken van de Facebookpagina van

reisorganisatie X

.846

Ik voel me erg verbonden met de Facebookpagina van reisorganisatie X .930

Ik beschouw mezelf erg loyaal aan de Facebookpagina van reisorganisatie X .933

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Customer Loyalty

Component

1

In het algemeen ben ik erg loyaal aan reisorganisatie X .901

Ik ben van plan om binnenkort weer gebruik te maken van reisorganisatie X .901

Ik ben in het algemeen erg verbonden met reisorganisatie X .920

Ik beschouw mezelf een loyale klant van reisorganisatie X .928

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Component Matrixa for Relationship Commitment

Component

1

Ik ben gehecht aan mijn relatie met reisorganisatie X .932

De relatie met reisorganisatie X is belangrijk voor me .950

Ik geef veel om de relatie met reisorganisatie X .960

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om te onderhouden .905

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

112

Component Matrixa for Word of Mouth

Component

1

Ik raad reisorganisatie X vaak aan anderen aan .938

Wanneer ik met vrienden of collega’s spreek over reisorganisatie X, ben ik ... .925

Ik ben van plan reisorganisatie X bij anderen te introduceren .927

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Communalities

Communalities for Social Benefits

Initial Extraction

Ik word herkend door reisorganisatie X op hun Facebookpagina 1.000 .630

Ik vind de sociale aspecten van mijn relatie met reisorganisatie X op Facebook leuk 1.000 .643

Via Facebook heb ik een vriendschapsrelatie ontwikkeld met reisorganisatie X 1.000 .660

Ik ben bekend met de Facebookpagina van reisorganisatie X 1.000 .406

Mijn naam is bekend bij reisorganisatie X op Facebook 1.000 .675

Extraction Method: Principal Component Analysis.

Communalities for Confidence Benefits

Initial Extraction

Ik geloof dat er weinig risico is dat mijn privacy via Facebook geschonden wordt door

reisorganisatie X

1.000 .376

Ik kan de informatie op de Facebookpagina van reisorganisatie X vertrouwen 1.000 .533

Ik heb er vertrouwen in dat reisorganisatie X een goede dienst verleent op Facebook 1.000 .693

Door mijn relatie op Facebook met reisorganisatie X, heb ik minder angst bij het

boeken van een reis

1.000 .608

Door de Facebookpagina van reisorganisatie X weet ik wat ik van de organisatie kan

verwachten

1.000 .550

Op Facebook krijg ik de best mogelijke service van reisorganisatie X 1.000 .589

Extraction Method: Principal Component Analysis.

113

Communalities for Functional Benefits

Initial Extraction

De Facebookpagina van reisorganisatie X biedt mij gemak 1.000 .668

Ik haal voordeel uit het advies dat ik krijg via de Facebookpagina van reisorganisatie

X

1.000 .727

Ik kan betere beslissingen nemen bij het boeken van mijn reis door het gebruik van

de Facebookpagina van reisorganisatie X

1.000 .622

Reisorganisatie X houdt mij via Facebook op de hoogte van interessante reizen en

bestemmingen

1.000 .554

Extraction Method: Principal Component Analysis.

Communalities for Special Treatment Benefits

Initial Extraction

Via Facebook ontvang ik kortingen en speciale deals van reisorganisatie X die de

meeste klanten niet krijgen

1.000 .541

Via de Facebookpagina van reisorganisatie X krijg ik betere prijzen dan de meeste

klanten

1.000 .784

Via Facebook ontvang ik een service van reisorganisatie X die veel andere klanten

niet krijgen

1.000 .783

Indien nodig, kan ik via Facebook snellere service krijgen van reisorganisatie X 1.000 .719

Door middel van de Facebookpagina van reisorganisatie X bespaar ik tijd bij het

zoeken naar informatie

1.000 .540

Extraction Method: Principal Component Analysis.

Communalities for Hedonic Benefits

Initial Extraction

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens interessant 1.000 .642

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X amusant 1.000 .705

Het bezoeken van de Facebookpagina van reisorganisatie X verblijdt mij 1.000 .792

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens prettig 1.000 .818

Door de Facebookpagina van reisorganisatie X krijg ik zin om te reizen 1.000 .515

Extraction Method: Principal Component Analysis.

114

Communalities for Customer e-Satisfaction

Initial Extraction

Vergeleken met andere Facebookpagina’s van reisorganisaties, ben ik erg tevreden

over de Facebookpagina van reisorganisatie X

1.000 .655

Op basis van al mijn ervaringen met de Facebookpagina van reisorganisatie X, ben

ik tevreden over hun dienstverlening

1.000 .746

Mijn ervaringen met de Facebookpagina van reisorganisatie X zijn altijd prettig 1.000 .746

Over het algemeen ben ik tevreden over de Facebookpagina van reisorganisatie X 1.000 .718

De Facebookpagina van reisorganisatie X gaat mijn verwachtingen te boven 1.000 .629

Extraction Method: Principal Component Analysis.

Communalities for Customer Satisfaction

Initial Extraction

Vergeleken met andere reisorganisaties, ben ik erg tevreden over reisorganisatie X

in het algemeen

1.000 .710

Op basis van al mijn ervaringen met reisorganisatie X, ben ik erg tevreden 1.000 .873

Mijn ervaringen met reisorganisatie X zijn altijd prettig 1.000 .864

Over het algemeen ben ik tevreden over reisorganisatie X 1.000 .857

Reisorganisatie X gaat in het algemeen mijn verwachtingen te boven 1.000 .555

Extraction Method: Principal Component Analysis.

Communalities for Customer e-Loyalty

Initial Extraction

Ik ben erg loyaal aan de Facebookpagina van reisorganisatie X 1.000 .777

Ik ben van plan om binnenkort weer gebruik te maken van de Facebookpagina van

reisorganisatie X

1.000 .716

Ik voel me erg verbonden met de Facebookpagina van reisorganisatie X 1.000 .865

Ik beschouw mezelf erg loyaal aan de Facebookpagina van reisorganisatie X 1.000 .870

Extraction Method: Principal Component Analysis.

115

Communalities for Customer Loyalty

Initial Extraction

In het algemeen ben ik erg loyaal aan reisorganisatie X 1.000 .812

Ik ben van plan om binnenkort weer gebruik te maken van reisorganisatie X 1.000 .812

Ik ben in het algemeen erg verbonden met reisorganisatie X 1.000 .845

Ik beschouw mezelf een loyale klant van reisorganisatie X 1.000 .861

Extraction Method: Principal Component Analysis.

Communalities for Relationship Commitment

Initial Extraction

Ik ben gehecht aan mijn relatie met reisorganisatie X 1.000 .868

De relatie met reisorganisatie X is belangrijk voor me 1.000 .902

Ik geef veel om de relatie met reisorganisatie X 1.000 .922

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om te

onderhouden

1.000 .819

Extraction Method: Principal Component Analysis.

Communalities for Word of Mouth

Initial Extraction

Ik raad reisorganisatie X vaak aan anderen aan 1.000 .879

Wanneer ik met vrienden of collega’s spreek over reisorganisatie X, ben ik ... 1.000 .855

Ik ben van plan reisorganisatie X bij anderen te introduceren 1.000 .860

Extraction Method: Principal Component Analysis.

116

Reliability analysis

Social Benefits (Cronbach’s α=.831)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik word herkend door reisorganisatie X op hun Facebookpagina .650 .792

Ik vind de sociale aspecten van mijn relatie met reisorganisatie X op Facebook

leuk

.667 .791

Via Facebook heb ik een vriendschapsrelatie ontwikkeld met reisorganisatie X .674 .784

Ik ben bekend met de Facebookpagina van reisorganisatie X .478 .837

Mijn naam is bekend bij reisorganisatie X op Facebook .699 .776

Confidence Benefits (Cronbach’s α=.836)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik geloof dat er weinig risico is dat mijn privacy via Facebook geschonden

wordt door reisorganisatie X

.462 .836

Ik kan de informatie op de Facebookpagina van reisorganisatie X vertrouwen .583 .815

Ik heb er vertrouwen in dat reisorganisatie X een goede dienst verleent op

Facebook

.715 .792

Door mijn relatie op Facebook met reisorganisatie X, heb ik minder angst bij

het boeken van een reis

.667 .798

Door de Facebookpagina van reisorganisatie X weet ik wat ik van de

organisatie kan verwachten

.622 .807

Op Facebook krijg ik de best mogelijke service van reisorganisatie X .648 .801

117

Functional Benefits (Cronbach’s α=.813)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

De Facebookpagina van reisorganisatie X biedt mij gemak .654 .757

Ik haal voordeel uit het advies dat ik krijg via de Facebookpagina van

reisorganisatie X

.705 .729

Ik kan betere beslissingen nemen bij het boeken van mijn reis door het gebruik

van de Facebookpagina van reisorganisatie X

.616 .774

Reisorganisatie X houdt mij via Facebook op de hoogte van interessante

reizen en bestemmingen

.560 .798

Special Treatment Benefits (Cronbach’s α=.873)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Via Facebook ontvang ik kortingen en speciale deals van reisorganisatie X die

de meeste klanten niet krijgen

.589 .874

Via de Facebookpagina van reisorganisatie X krijg ik betere prijzen dan de

meeste klanten

.801 .826

Via Facebook ontvang ik een service van reisorganisatie X die veel andere

klanten niet krijgen

.797 .822

Indien nodig, kan ik via Facebook snellere service krijgen van reisorganisatie

X

.748 .834

Door middel van de Facebookpagina van reisorganisatie X bespaar ik tijd bij

het zoeken naar informatie

.596 .872

118

Hedonic Benefits (Cronbach’s α=.885)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens

interessant

.676 .871

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X amusant .734 .857

Het bezoeken van de Facebookpagina van reisorganisatie X verblijdt mij .807 .840

Ik vind mijn bezoek aan de Facebookpagina van reisorganisatie X telkens

prettig

.836 .835

Door de Facebookpagina van reisorganisatie X krijg ik zin om te reizen .583 .894

Customer e-Satisfaction (Cronbach’s α=.891)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Vergeleken met andere Facebookpagina’s van reisorganisaties, ben ik erg

tevreden over de Facebookpagina van reisorganisatie X

.703 .875

Op basis van al mijn ervaringen met de Facebookpagina van reisorganisatie

X, ben ik tevreden over hun dienstverlening

.776 .858

Mijn ervaringen met de Facebookpagina van reisorganisatie X zijn altijd prettig .770 .860

Over het algemeen ben ik tevreden over de Facebookpagina van

reisorganisatie X

.747 .865

De Facebookpagina van reisorganisatie X gaat mijn verwachtingen te boven .682 .880

119

Customer Satisfaction (Cronbach’s α=.923)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Vergeleken met andere reisorganisaties, ben ik erg tevreden over

reisorganisatie X in het algemeen

.754 .914

Op basis van al mijn ervaringen met reisorganisatie X, ben ik erg tevreden .882 .888

Mijn ervaringen met reisorganisatie X zijn altijd prettig .873 .890

Over het algemeen ben ik tevreden over reisorganisatie X .869 .893

Reisorganisatie X gaat in het algemeen mijn verwachtingen te boven .638 .937

Customer e-Loyalty (Cronbach’s α=.920)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik ben erg loyaal aan de Facebookpagina van reisorganisatie X .790 .905

Ik ben van plan om binnenkort weer gebruik te maken van de Facebookpagina

van reisorganisatie X

.739 .922

Ik voel me erg verbonden met de Facebookpagina van reisorganisatie X .868 .878

Ik beschouw mezelf erg loyaal aan de Facebookpagina van reisorganisatie X .871 .877

Customer Loyalty (Cronbach’s α=.933)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

In het algemeen ben ik erg loyaal aan reisorganisatie X .824 .918

Ik ben van plan om binnenkort weer gebruik te maken van reisorganisatie X .824 .918

Ik ben in het algemeen erg verbonden met reisorganisatie X .854 .909

Ik beschouw mezelf een loyale klant van reisorganisatie X .867 .904

120

Relationship Commitment (Cronbach’s α=.953)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik ben gehecht aan mijn relatie met reisorganisatie X .877 .941

De relatie met reisorganisatie X is belangrijk voor me .907 .932

Ik geef veel om de relatie met reisorganisatie X .926 .927

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om te

onderhouden

.835 .954

Word of Mouth (Cronbach’s α=.918)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Ik raad reisorganisatie X vaak aan anderen aan .856 .867

Wanneer ik met vrienden of collega’s spreek over reisorganisatie X, ben ik ... .831 .895

Ik ben van plan reisorganisatie X bij anderen te introduceren .837 .881

121

Appendix 4: Output used during confirmatory factor analysis

Table 17: Regression weights in the measurement model

Estimate P

A5 <--- Social_Benefits 1.000 ***

A4 <--- Social_Benefits .653 ***

A3 <--- Social_Benefits 1.079 ***

A2 <--- Social_Benefits .817 ***

A1 <--- Social_Benefits 1.020 ***

B6 <--- Confidence_Benefits 1.000 ***

B5 <--- Confidence_Benefits .960 ***

B4 <--- Confidence_Benefits 1.096 ***

B3 <--- Confidence_Benefits .833 ***

B2 <--- Confidence_Benefits .649 ***

B1 <--- Confidence_Benefits .572 ***

C4 <--- Functional_Benefits 1.000 ***

C3 <--- Functional_Benefits 1.185 ***

C2 <--- Functional_Benefits 1.438 ***

C1 <--- Functional_Benefits 1.213 ***

D5 <--- Special_Treatment_Benefits 1.000 ***

D4 <--- Special_Treatment_Benefits 1.115 ***

D3 <--- Special_Treatment_Benefits 1.129 ***

D2 <--- Special_Treatment_Benefits .987 ***

D1 <--- Special_Treatment_Benefits .914 ***

E5 <--- Hedonic_Benefits 1.000 ***

E4 <--- Hedonic_Benefits 1.193 ***

E3 <--- Hedonic_Benefits 1.171 ***

E2 <--- Hedonic_Benefits 1.016 ***

E1 <--- Hedonic_Benefits 1.093 ***

F1 <--- Customer_eSatisfaction 1.000 ***

F2 <--- Customer_eSatisfaction 1.176 ***

F3 <--- Customer_eSatisfaction 1.118 ***

F4 <--- Customer_eSatisfaction 1.106 ***

F5 <--- Customer_eSatisfaction 1.009 ***

122

Regression weights in the measurement model (continued)

Estimate P

G1 <--- Customer_Satisfaction 1.000 ***

G2 <--- Customer_Satisfaction 1.158 ***

G3 <--- Customer_Satisfaction 1.179 ***

G4 <--- Customer_Satisfaction 1.077 ***

G5 <--- Customer_Satisfaction .852 ***

H4 <--- Customer_eLoyalty 1.000 ***

H3 <--- Customer_eLoyalty .991 ***

H2 <--- Customer_eLoyalty .748 ***

H1 <--- Customer_eLoyalty .809 ***

I4 <--- Customer_Loyalty 1.000 ***

I3 <--- Customer_Loyalty .975 ***

I2 <--- Customer_Loyalty .878 ***

I1 <--- Customer_Loyalty .909 ***

J4 <--- Relationship_Commitment 1.000 ***

J3 <--- Relationship_Commitment 1.094 ***

J2 <--- Relationship_Commitment 1.086 ***

J1 <--- Relationship_Commitment 1.057 ***

K1 <--- WOM 1.000 ***

K2 <--- WOM .880 ***

K3 <--- WOM .947 ***

KMO and Bartlett’s Test

Latent variable #

Items

KMO Bartlett’s test

of sphericity

Sig.

Customer Loyaltya 8 .907 .000

a. Including items of the initial Customer Loyalty and Relationship

Commitment

123

Total Variance Explained for Customer Loyaltya

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total

% of

Variance

Cumulative

% Total % of Variance Cumulative %

1 6.164 77.051 77.051 6.164 77.051 77.051

2 .728 9.097 86.148

3 .273 3.411 89.559

4 .256 3.200 92.759

5 .204 2.549 95.309

6 .179 2.237 97.546

7 .129 1.613 99.158

8 .067 .842 100.000

Extraction Method: Principal Component Analysis.

a. Including items of the initial Customer Loyalty and Relationship Commitment

Customer Loyaltya

a. Including items of the initial Customer Loyalty

and Relationship Commitment

124

Component Matrixa for Customer Loyaltyb

Component

1

In het algemeen ben ik erg loyaal aan reisorganisatie X .854

Ik ben van plan om binnenkort weer gebruik te maken van reisorganisatie X .827

Ik ben in het algemeen erg verbonden met reisorganisatie X .878

Ik beschouw mezelf een loyale klant van reisorganisatie X .893

Ik ben gehecht aan mijn relatie met reisorganisatie X .926

De relatie met reisorganisatie X is belangrijk voor me .914

Ik geef veel om de relatie met reisorganisatie X .896

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om te

onderhouden

.828

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

b. Including items of the initial Customer Loyalty and Relationship Commitment

Communalities for Customer Loyaltya

Initial Extraction

In het algemeen ben ik erg loyaal aan reisorganisatie X 1.000 .729

Ik ben van plan om binnenkort weer gebruik te maken van reisorganisatie X 1.000 .684

Ik ben in het algemeen erg verbonden met reisorganisatie X 1.000 .772

Ik beschouw mezelf een loyale klant van reisorganisatie X 1.000 .797

Ik ben gehecht aan mijn relatie met reisorganisatie X 1.000 .858

De relatie met reisorganisatie X is belangrijk voor me 1.000 .836

Ik geef veel om de relatie met reisorganisatie X 1.000 .802

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om te

onderhouden

1.000 .686

Extraction Method: Principal Component Analysis.

a. Including items of the initial Customer Loyalty and Relationship Commitment.

125

Customer Loyaltya (Cronbach’s α=.957)

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

In het algemeen ben ik erg loyaal aan reisorganisatie X .807 .953

Ik ben van plan om binnenkort weer gebruik te maken van

reisorganisatie X

.775 .955

Ik ben in het algemeen erg verbonden met reisorganisatie X .836 .951

Ik beschouw mezelf een loyale klant van reisorganisatie X .855 .950

Ik ben gehecht aan mijn relatie met reisorganisatie X .900 .947

De relatie met reisorganisatie X is belangrijk voor me .885 .948

Ik geef veel om de relatie met reisorganisatie X .863 .950

Mijn relatie met reisorganisatie X verdient mijn maximale inspanning om

te onderhouden

.779 .955

a. Including items of the initial Customer Loyalty and Relationship Commitment

126

Covaried error terms and their modification indices

M.I. Par Change

ε41 ↔ ε40 16.858 .264

ε42 ↔ ε40 10.127 .189

ε42 ↔ ε41 15.477 .243

ε43 ↔ ε40 11.320 .195

ε43 ↔ ε41 19.546 .267

ε43 ↔ ε42 24.599 .277

ε45 ↔ ε40 11.268 -.163

ε45 ↔ ε42 13.618 -.172

ε46 ↔ ε41 21.810 -.258

ε46 ↔ ε42 15.408 -.201

ε46 ↔ ε43 28.783 -.267

ε46 ↔ ε45 45.852 .281

ε47 ↔ ε42 16.447 -.263

ε47 ↔ ε46 34.226 .339

ε48 ↔ ε50 16.381 .208

ε31 ↔ ε32 10.907 .140

ε28 ↔ ε29 10.856 .135

ε22 ↔ ε21 12.801 .214

ε25 ↔ ε21 13.317 -.274

ε17 ↔ ε16 14.697 .339

ε20 ↔ ε16 22.240 -.563

ε7 ↔ ε6 12.216 .331

ε8 ↔ ε7 30.729 .382

ε10 ↔ ε9 12.214 .409

127

Covariance between error terms and their justification

Variable Items with covaried

error terms

Justification

Confidence Benefits 7 ↔ 8 Both items are about trust in the organization.

9 ↔ 10 When people know what to expect, they probably

might have no fear during the booking and vice

versa.

6 ↔ 7 Trust in an organization for not violating one’s

privacy does not mean one can trust the information

provided and vice versa.

Special Treatment

Benefits

16 ↔ 17 Both items are about special deals.

16 ↔ 20 Special deals and discounts do not save time by

searching for information and vice versa.

Hedonic Benefits 21 ↔ 22 Customers might think visiting the Facebook page is

amusing when it is interesting and vice versa.

21 ↔ 25 Customers may think that the Facebook page is

interesting when they are going to feel like traveling

and vice versa.

Customer e-

Satisfaction

28 ↔ 29 When experiences with the Facebook page are

always pleasant, a customer will generally be

satisfied and vice versa

Customer Satisfaction 31 ↔ 32 Both items are about satisfaction with the tour

operator

Customer Loyalty 41 ↔ 40, 42 ↔ 40,

42 ↔ 41, 43 ↔ 40, 43 ↔ 41, 43 ↔ 42, 45 ↔ 40, 45 ↔ 42, 46 ↔ 41, 46 ↔ 42, 46 ↔ 43, 46 ↔ 45, 47 ↔ 42, 47 ↔ 46

All items are about feeling loyal and attached to the

organization, which is correlated to the desire to

book another holiday and providing effort in the

relationship

Word of Mouth 48 ↔ 50 If a customer recommends an organization to

someone else, he might intend to introduce this

organization to another person and the other way

around

128

Appendix 5: Output used during structural equation modeling

Table 18: Significant paths found with the proposed model

P-value

Customer Loyalty Social Benefits .003

Customer e-Satisfaction Hedonic Benefits ***

Customer Loyalty Customer Satisfaction ***

Customer Satisfaction Customer e-Satisfaction ***

Customer Loyalty Customer e-Loyalty .012

Word of Mouth Customer Loyalty ***

Word of Mouth Customer Satisfaction ***

129

Appendix 6: Output used during nested structural models testing

Best fitting models according to BCC

Model Params df C C-df BCC 0 BIC 0 C/df p Notes

32 143 1132 1999.957 867.957 0.000 0.000 1.767 0.000

42 144 1131 1997.114 866.114 0.127 2.212 1.766 0.000

52 145 1130 1994.186 864.186 0.171 4.341 1.765 0.000

43 144 1131 1997.348 866.348 0.362 2.447 1.766 0.000

62 146 1129 1991.938 862.938 0.895 7.149 1.764 0.000

44 144 1131 1997.910 866.910 0.924 3.009 1.766 0.000

54 145 1130 1995.133 865.133 1.119 5.288 1.766 0.000

64 146 1129 1992.242 863.242 1.199 7.453 1.765 0.000

45 144 1131 1998.206 867.206 1.220 3.305 1.767 0.000

55 145 1130 1995.261 865.261 1.247 5.416 1.766 0.000

56 145 1130 1995.307 865.307 1.292 5.462 1.766 0.000

46 144 1131 1998.329 867.329 1.343 3.427 1.767 0.000

47 144 1131 1998.696 867.696 1.710 3.795 1.767 0.000

57 145 1130 1995.741 865.741 1.726 5.896 1.766 0.000

58 145 1130 1995.898 865.898 1.883 6.053 1.766 0.000

48 144 1131 1998.941 867.941 1.955 4.040 1.767 0.000

59 145 1130 1996.078 866.078 2.063 6.233 1.766 0.000

Best fitting models according to BIC

Model Params df C C-df BCC 0 BIC 0 C/df p Notes

32 143 1132 1999.957 867.957 0.000 0.000 1.767 0.000

22 142 1133 2006.734 873.734 3.805 1.721 1.771 0.000

42 144 1131 1997.114 866.114 0.127 2.212 1.766 0.000

130

Best fitting models for different parameters (short list)

Model Params df C C-df BCC 0 BIC 0 C/df p Notes

1 139 1136 2224.558 1088.588 212.714 204.375 1.958 0.000

2 140 1135 2074.926 939.926 66.055 59.800 1.828 0.000

12 141 1134 2027.104 893.104 21.203 17.034 1.788 0.000

22 142 1133 2006.734 873.734 3.805 1.721 1.771 0.000

32 143 1132 1999.957 867.957 0.000 0.000 1.767 0.000

42 144 1131 1997.114 866.114 0.127 2.212 1.766 0.000

52 145 1130 1994.186 864.186 0.171 4.341 1.765 0.000

62 146 1129 1991.938 862.938 0.895 7.149 1.764 0.000

72 147 1128 1990.478 862.478 2.406 10.745 1.765 0.000

82 148 1127 1989.418 862.418 4.317 14.742 1.765 0.000

93 149 1126 1988.822 862.822 6.693 19.202 1.766 0.000

108 150 1125 1988.347 863.347 9.190 23.783 1.767 0.000

112 151 1124 1987.407 863.407 11.221 27.900 1.768 0.000 Unstable

122 152 1123 1987.158 864.158 13.944 32.707 1.770 0.000 Unstable

132 153 1122 1986.942 864.942 16.698 37.547 1.771 0.000 Unstable

142 154 1121 1986.811 19.540 19.540 42.473 1.772 0.000 Unstable

152 155 1120 1986.766 866.766 22.466 47.483 1.774 0.000 Unstable

162 156 1119 1986.470 867.470 25.142 52.244 1.775 0.000 Unstable

178 157 1118 1988.470 870.470 30.113 59.300 1.779 0.000

182 158 1117 0.000 Iteration

Limit

131

Table 19: Significant indirect effects found by the final model

Hedonic

Benefits

Special

Treatment

Benefits

Functional

Benefits

Confidence

Benefits

Social

Benefits

Customer

eSatisfaction

Customer

eLoyalty

Customer

Satisfaction

Customer

Loyalty WOM

Customer

eSatisfaction ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000 ,000

Customer

eLoyalty ,493 ,000 ,000 ,429 ,000 ,000 ,000 ,000 ,000 ,000

Customer

Satisfaction ,347 ,000 ,000 ,302 ,000 ,000 ,000 ,000 ,000 ,000

Customer

Loyalty ,384 ,000 ,000 ,334 ,093 ,838 ,000 ,000 ,000 ,000

WOM ,407 ,000 -,173 ,354 ,155 ,888 ,120 ,154 ,000 ,000