SunnyDissertation12082016_FINAL

University of Reading
The Effects of Customer Satisfaction on
Electronic Word-of-Mouth in the UK
Airline Industry
Yuk Yin Wong
August 2015
Dissertation submitted in partial fulfilment of the regulations for the degree of
MSC in Marketing and International Management

Declaration
This dissertation is a product of my own work and is not the result of anything done in
collaboration.
I consent to the University’s free use including online reproduction, including electronically,
and including adaptation for teaching and education activities of any whole or part item of
this dissertation.
Yuk Yin Wong
Word Length: 12516 words

Abstract
The aim of this study is to investigate the relationship between customer dis/satisfaction and
air travellers’ intention to participate eWOM communication in the UK airline industry. It
also further explores prior experience of eWOM’s influence on this relationship.
Electronic word-of-mouth (eWOM) is recognized as an increasingly influential means of
interpersonal communication and a powerful marketing tool (Liang et al., 2013). Although
previous studies have focussed on related motivations for eWOM in the travel industry,
limited research was dedicated to this subject in the airline industry.
Previous research literature examined eWOM from a motivational perspective and identified
3 motives, including customer dis/satisfaction, that influence consumers’ intention to
participate in eWOM (Henning- Thurau et al., 2003; Liang et al., 2013). Theories, such as
social influence model (Dholakia et al. (2004) and balance theory (Heider, 1958), also further
investigates why consumers produce eWOM. Six hypotheses are presented and tested within
the study based on theories suggested by research literatures, where data is collected from 60
participants who reside in the UK. The findings of this study show that four out of six
hypotheses are supported. Results show that extremely satisfied air travellers are more likely
to produce eWOM and WOM, while extremely dissatisfied air travellers are more like likely
to produce eComplaint and Complaint. However, as results for testing prior experience’s
influence on eWOM are not statistically significant, prior experience is found to have no
moderate effects on intention to engage in eWOM communication.
This study contributes to the research literature and the managerial aspects of the UK airline
industry. The UK airline industry managers are advised to strengthen their understanding of
what motivates their consumers to engage in eWOM and complaints in online platforms. It is
also important for managers in the UK airline industry to know what factors create more
satisfying experience and less dissatisfying experience. Combining findings from literature
review and results of this study, perceived safety of an airline and consumer’s purpose of
travelling are two major factors that influence customer dis/satisfaction. Managers should
focus on understanding these two factors and improve customer satisfaction, in return,
motivate travellers to produce more positive eWOM.

Acknowledgement
Upon the completion of this dissertation, I would like to extend my sincere and deepest
gratitude towards all the individuals who have helped me along this journey. I am thankful
for the active guidance, support, and encouragement they have given me in completing this
thesis.
I am ineffably indebted to my advisor, Professor Yuksel Ekinci, for his full support, expert
guidance, understanding and giving his precious advice regarding the topic of my research.
Despite having a busy schedule, he found time to meet with me and guided me through all
the aspects that I was not familiar with.
I am grateful to my friends Max Leblanc and Kojo Eleazer Amerteyfio for being a source of
motivation and for guiding me with the language and structure of my dissertation.
I would also like to thank my friends and family for who have share and completed the
questionnaire of this dissertation. Without your help, I would not be able to complete this
research.

I
List of Tables
Page
Table 2.1: Motives for Word-of-Mouth Communication Behaviour.....................................9
Table 2.2 Conceptual Framework for eWOM Communication............................................11
Table 3.1 Desires Motivating Consumers to Post in Online Platforms................................28
Table 5.1 Education Level of the Sample.............................................................................46
Table 5.2 Distribution of Nationality................................................................................... 47
Table 5.3 Paired Sample T-Test Results ..............................................................................55
Table 5.4 Independent Sample T-Test Results ....................................................................58
Table 5.5 Hypothesis Testing Results ..................................................................................60

II
List of Figures
Page
Figure 3.1 Customer Satisfaction as Bipolar Structure........................................................23
Figure 3.2 Two Factor Theory of Customer Dis/satisfaction..............................................24
Figure 3.3 Measures of Customer Dis/satisfaction..............................................................26
Figure 4.1 Research Model..................................................................................................36
Figure 5.1 Gender Distribution of the Sample.....................................................................45
Figure 5.2 Age Distribution of the Sample..........................................................................46
Figure 5.3 Distribution of Residency...................................................................................48
Figure 5.4 Distribution of Prior Experience in eWOM........................................................49
Figure 5.5 Types of Opinions Posted in Non-Real Time Communication Media(s)...........50
Figure 5.6 Types of Real Time Electronic Media(s) Used.................................................. 51
Figure 5.7 Types of Non- Real Time Electronic Media(s) Used .........................................52
Figure 5.8 Monthly Duration Used for Tourism eWOM on Real Time Electronic Media(s)
...............................................................................................................................................53
Figure 5.9 Monthly Duration Used for Tourism eWOM on Non-Real Time Electronic
Media(s) ...............................................................................................................................53

Table of Contents
Page
List of Tables……………………………………………………………………………….I
List of Figures II……………………………………………………………………………II
Chapter 1. Introduction ………………………………………………………………….1
1.1 Background of the study………………………………………………………………..1
1.2 Aim and objectives of the study ……………………………………………………….4
1.3 Structure of the study …………………………………………………………………..5
Chapter 2. An Overview of Electronic Word of Mouth (eWOM)……………………...6
2.1 Introduction……………………………………………………………………………. 6
2.2 Definition of eWOM……………………………………………………………………6
2.3 Models of eWOM……………………………………………………………….......….8
2.3.1 Conceptual Framework for eWOM Communication…………………....…..10
2.3.1.1 Focused- Related Utility……………………………………...…....12
2.3.1.2 Consumption Utility…………………………………….…...…….12
2.3.1.3 Approval Utility…………………………………………....………13
2.3.1.4 Moderator- Related Utility………………………………....………13
2.3.1.5 Homeostase Utility………………………………………....………14
2.3.2 Social Influence Model ……………………………………………………...14
2.3.3 Balance Theory………………………………………………………………16
2.3.3.1 Balanced and Unbalanced State……………………………………16
2.3.3.2 WOM Motives from Balance Theory……………………………...18
2.4 eWOM in the Airline Industry…………………………………………………………20
2.5 Conclusion……………………………………………………………………………...21
Chapter 3 Customer Satisfaction in the Airline Industry………………………………22

3.1 Introduction………………………………………………………………….…………22
3.2 Definition of Customer Satisfaction……………………………………..…….……….22
3.3 Models of Customer Satisfaction………………………………………...….……...… 23
3.3.1 Theories of Customer Dis/satisfaction on WOM ……………...….…..……..25
3.4 Measurements of Customer Satisfaction………………………………...….………….26
3.5 Customer satisfaction in the Airline Industry…………………………...….…………..27
3.5.1 Focus-related Utility and Homeostase Utility Theory ……………………….28
3.5.2 Influence of Perceived Safety on Satisfaction ……………………………….29
3.5.3 Effects of the Travel Purpose on Customer Satisfaction …………………….30
3.6 Conclusion………………………………………………………………………………31
Chapter 4. Methodology…………………………………………………………………..32
4.1 Introduction……………………………………………………………………………..32
4.2 Aim and Objectives of the Study……………………………………………………….32
4.3 Research Philosophy……………………………………………………………………33
4.4 Types of Data…………………………………………………………………………...33
4.5 Research Design………………………………………………………………………...34
4.5.1 Conceptual Framework……………………………………………………….36
4.5.2 Research Instrument and Measurement………………………………………38
4.5.3 Data Collection and Sampling………………………………………………..39
4.5.4 Scenario Testing……………………………………………………………...40
4.5.5 Pilot Study…………………………………………………………………....41
4.6 Data Analysis Methods………………………………………………………………...41
4.6.1 Reliability and Validity………………………………………………………42
4.6.2 Paired Sample T-Test………………………………………………………...42
4.6.3 Independent Sample T-Test…………………………………………………..42

4.7 Conclusion……………………………………………………………………………..43
Chapter 5. Findings……………………………………………………………………....44
5.1 Introduction…………………………………………………………………………....44
5.2 Demographics of the Sample……………………………………………………….....44
5.3 eWOM Communication Behaviour………………………………………………...…49
5.4 Hypothesis Testing……………………………………………………………………54
5.4.1 Hypothesis 1………………………………………………………………...55
5.4.1.1 Hypothesis 1a……………………………………………………..56
5.4.1.2 Hypothesis 1b……………………………………………………..56
5.4.1.3 Hypothesis 1c…………………………………………………......56
5.4.1.4 Hypothesis 1d……………………………………………………..57
5.4.1.5 Hypothesis 2a and b………………………………………………58
5.4.2 Summary of Hypothesis Testing Results …………………………..60
5.6 Conclusion……………………………………………………………………………61
Chapter 6. Conclusion…………………………………………………………………..62
6.1 Introduction…………………………………………………………………………..62
6.2 Summary of Main Findings…………………………………………….…………….62
6.3 Contribution of the Study……………………………………………….……………65
6.4 Managerial Implications…………………………………………………………….. 66
6.5 Limitation of the Study ………………………………………………………………67
6.6 Further Research Areas……………………………………………………………….68
References………………………………………………………………………………..69
Appendices……………………………………………………………………………….75

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Chapter 1
Introduction
1.1 Background of the Study
Word-of-mouth (WOM), also known as buzz marketing, is an interpersonal communication
medium between consumers sharing their opinions about their consumption experience
(Brooks, 1975; Dichter,1966; Richins, 1984). It is identified as more credible than marketer-
sourced promotions and offers trustworthy messages that affects consumer decision-making
(Brown et al., 2001; Herr et al., 1991; Senecal & Nantel, 2004). However, though WOM is
effective in providing the right type of information to consumers, it is difficult to trace (Dwyer,
2007). With the development of technology and increasing use of electronic media, electronic
word-of-mouth (eWOM) is becoming more common among consumers and is measurable since
comments of products and services are written and available on the websites (Godes & Mayzlin,
2004). Research has also suggested that eWOM is more influential than traditional WOM, as it
can quickly reach a large audience, does not rely only on opinions of acquaintances, can be
accessed immediately and permanently, provides anonymity, and enables individuals to build
up personal and social networks (Litvin et al., 2008; Senecal & Nantel, 2004; Sun et al., 2006;
Brown et al., 2007; Buffardi & Campbell, 2008).
As eWOM reaches a wide range and large amount of consumers, it is essential for businesses to
understand why consumers leave negative reviews and what motivates them to produce eWOM
in order to provide better products and services. Applying this perspective to the airline
industry, we can see that marketing in the airline industry is changing together with the
development of technology. The tourism industry has become one of the leaders in the use of

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Internet, as companies in this industry communicate very effectively with their existing and
potential customers through online channels. Importantly, the company website has become the
primary source of contact with potential visitors (Zach and Heverin, 2010). Other than the
company website, eWOM is an important reference for decision-making for travellers, as travel
products are considered as high-risk and high-involvement purchases (Litvin et al., 2008;
Simpson & Siguaw, 2008; ). Moreover, consumers’ decision-making process is also
increasingly influenced by reading eWOM comments (Liang et al.,2013). Companies in the
airline industry need to understand what motivates their consumers to participate in eWOM, as
consumers in this industry are provided with too many airline choices, including new entrants
and existing rivals in the industry (Tan, 2013).
Previous studies have examined eWOM from motivational perspective, including the concept of
attitude-intention- behaviour, consumer attitude theory, social influence model, as well as the
balance theory (Henning- Thurau et al., 2003; Liang et al., 2013; Dholakia et al., 2004; Heider,
1958)). Studies by Henning- Thurau et al. and Yoo and Gretzel have identified 3 out of 11
motives to be statistically significant enough to influence consumer’s participation in eWOM;
these will be discussed in later sections (2003; 2008). Bronner and de Hoog (2011) have also
identified two types of motivations when consumers post online opinions: self-directed
motivation and other directed motivation. Based on the motivational perspective, social
influence model by Dholakia (2004) suggests that people participate in eWOM based on
individual-level and group-level driver, and eWOM is mainly motivated by social interactions.
Finally, motivation to participate in eWOM can also be explained through the balance theory
(Heider, 1958), which suggests that people uses eWOM to restore equilibrium when their inner
balance has become unbalanced.

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More recent studies by Liang et al. (2013) examined the effect of the adoption of eWOM
communication technology, customer dis/satisfaction with travel consumption experience, and
subject norm on overall attitude towards eWOM communication. These three antecedents have
all been found to have a positive effect on overall attitude towards eWOM communication,
where the overall attitude towards eWOM communication is a mediator between the three
antecedents and traveller’s intention to use eWOM communication media.
More specific studies were conducted regarding customer satisfaction’s effect on WOM in
earlier years. It is said that customer satisfaction is a bipolar construct, where customer
satisfaction takes on a value on a single dimension with the endpoints of “low” and “high”
satisfaction and that the majority of customer satisfaction measurement scales are of a bipolar
type (Hausknecht, 1990). Soderlund (1998) suggests that customer behaviour, including WOM,
are performed either by very satisfied customers or very dissatisfied customers and that
relationship between customer satisfaction and WOM is fairly symmetrical. Moreover, further
studies of theories such as the focus-related utility theory and homostase utility are explored to
explain the effects of customer dis/satisfaction on participation in eWOM activities.
Focus- related utility and homeostase utility theory are used to explore the effects of customer
satisfaction on travellers’ overall eWOM attitude (Liang et al., 2013), which could also be
applied in the airline industry. In terms of influencing factors on customer satisfaction in the
airline industry, service, perceived safety, and travel purpose are said to have the largest effects.
1.2 Aim and Objectives of the study

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Researchers in this field have suggested the lack of discussion on facilitation of WOM in its
relationship with customer satisfaction (Soderlund, 1998). With further advancement in
technology and development of eWOM, Soderlund’s study also appears to be out of date.
Consequently, the aim of the study is to assess the impact of customer dis/satisfaction with
travel consumption experience on eWOM in the airline industry.
The primary objective of this study is to develop a research instrument to assess this effects of
customer dis/satisfaction on eWOM, specifically in the UK airline industry.
To that end, the paper will also:
1. Review literature on eWOM
2. Review literature on customer dis/satisfaction in the airline industry.
1.3 Structure of the Study
Chapter 2 consists of a literature review of eWOM definitions, its models, antecedents, its
impact on the airline industry, its measurements, as well as the outcome

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Chapter 3 provides an explanation of the definition, models, and measurements of customer
satisfaction. It also describes previous studies of customer satisfaction in the aviation industry.
Chapter 4, explains the methodology used in this study. It describes the data collection
procedure and steps taken to reach the results.
Chapter 5 discloses the findings statistically, explaining the results, graphs, and tables. The
chapter shows the demographic data analysis and the requirements for analysis. Statistical
analysis is also explained, showing the results of each examination.
Chapter 6 consists of the summary of findings and shows the interpretation of results. It also
discusses limitations of the study, as well as possible directions of future researches.

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Chapter 2
An Overview of Electronic Word of Mouth (eWOM)
2.1. Introduction.
This chapter is an overview of current literature and theories of eWOM. It will provide
definition of eWOM from previous studies and review models of eWOM. Further investigation
on motives for WOM communication behaviour, including the social influence model and
balance theory will be discussed. Finally, it will provide a summary of eWOM in the airline
industry.
2.2 Definition of eWOM
eWOM is defined as ‘ any positive or negative statement made by potential, actual, or former
customers about a product or company, which is made available to a multitude of people and
institutions via the internet (Henning-Thurau et al., 2003). It can take place in many ways, such
as Web-based opinion platforms, discussion forums, boycott web sites, and news groups etc. Of
these options, web-based consumer-opinion platforms are identified to be the most widely used
eWOM format, with communications produced on these platforms able to form stronger
impacts on consumers than other formats. These platforms are also identified as easier to
operate, as consumers needs less Internet-related knowledge to obtain information on these
platforms, and they provide information on almost every area of consumption.
Research has identified eWOM to be more influential than traditional WOM for various
reasons. First of all, eWOM messages can be distributed to and reach a large audience base

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quickly (Litvin, Goldsmith, & Pan, 2008). Second, audiences who have received the messages
actively seek a wider range of comments and information online; Therefore, they do not solely
rely on opinions from one person or acquaintances (Senecall & Nantel, 2004). Third, as eWOM
allows for anonymity, people who publish comments online are encouraged to produce word of
mouth knowing they cannot be identified. (Phelps et al., 2004). Fourth, eWOM remains
permanently on the Internet, which is accessible immediately or after a period of time (Sun,
Youn, Wu, & Kuntaraporn, 2006). Finally, eWOM is identified as being able to build up
personal and social networks (Brown, Broderick, & Lee, 2007; Buffardi & Campbell, 2008)
with eWOM being so influential, reaching friends and strangers over the Internet, it is very
important that companies understand what motivates consumers to produce eWOM.
Recognising these motivations allow managers to promote their products better and prevent
negative publicity, which affects the image of products.
2.3 Models of eWOM
From a motivational perspective, earlier researches have identified 17 motives for predicting the
frequency of visiting online platforms and intention to write online comments with the

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motivational perspective (Henning- Thurau et al., 2003). Motives could be defined as the
“general drivers that direct a consumer’s behaviour toward attaining his or her needs” (Assael,
1998). Therefore, motives significantly determine consumer behaviour and are useful in
explaining why consumers read eWOM online (Henning- Thurau et al., 2003). Table 2.1 lists
out the 17 motives for WOM communication that are suggested by Ditcher (1996), Engel,
Blackwell, and Miniard (1993), as well as Sundaram, Mitra, & Webster (1998).
Table 2.1. Motives for Word-of-Mouth Communication Behaviour
Author(s) Motive Description
Ditcher (1966) Product- involvement A customer feels so strongly about the product that pressure builds up in
wanting to do something about it; recommending the product to others reduces
the tension caused by the consumption experience
Self- involvement The product serves as a means through which the speaker can gratify certain

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emotional needs
Other- involvement Word-of-mouth actively addresses the need to give something to the receiver
Message-involvement Refers to discussion which is stimulated by advertisements,commercials, or
public relations
Engel, Blackwell,
& Miniard (1993)
Involvement Levels of interest or involvement in the topic underconsideration serves to
stimulate discussion
Self-enhancement Recommendations allow person to gain attention,showconnoisseurship,
suggest status,give the impression of possessing inside information, and assert
superiority
Concern for others A genuine desire to help a friend or relative make a better purchase decision
Message intrigue Entertainment resulting from talking about certain ads or selling appeals
Dissonance reduction Reduce cognitive dissonance (doubts)following a major purchase decision
Sundaram, Mitra,
& Webster(1998)
Altruism (positive
WOM)
The act of doing something for others without anticipating any reward in return
Product involvement Personal interest in the product, excitement resulting from product ownership
and product use
Self-enhancement Enhancing images among other consumers by projecting themselves as
intelligent shoppers
Helping the company Desire to help the company
Altruism (negative
WOM)
To prevent others from experiencing the problems they had encountered
Anxiety reduction Easing anger, anxiety, and frustration
Vengeance To retaliate against the company associated with a negative consumption
experience
Advice seeking Obtaining advice on how to resolve problems
Source: Henning- Thurau et al. (2004)
As eWOM and traditional WOM communication have a certain degree of conceptual closeness,
the 17 motives for WOM communication listed in Table 2.1 are also applicable for eWOM
communication.

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Moreover, motives for eWOM communication could also be explained by the social influence
model (Dholakia et al., 2005) and balance theory (Heider, 1958).
2.3.1 Conceptual Framework for eWOM Communication
Balasubramanian and Mahajan (2011) have provided three types of social interaction utility to
identify motives in engaging in eWOM communication: focus-related utility, consumption
utility and approval utility. Based on the three utilities of Balasubramanian and Mahajan (2001),
Henning- Thurau et al. (2004) extended their framework to include two additional consumer
utilities, moderator- related utility and homeostase utility, which focus on the unique aspects of
Web-based consumer-opinion platforms.
Table 2.2 Conceptual Framework for eWOM Communication
Utilities Motives
Focus-Related Utility  Concern for other consumers
 Helping the company

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 Social benefits
 Exerting power
Consumption Utility  Post-purchase advice-seeking
Approval Utility  Self-enhancement
 Economic rewards
Moderator- RelatedUtility  Convenience
 Problem-solving support
Homeostase Utility  Expressing positive emotions
 Venting negative feelings
Source: Henning- Thurau et al. (2004)
Table 2.2 is a list of the utilities suggested by Balasubramanian and Mahajan (2001) and
Henning- Thurau et al. (2004) as well as motives for engaging in eWOM that are associated
with each utility.
2.3.1.1 Focused- Related Utility
Focus-related utility is the benefits that consumers experience when adding value to the
community through their positive contributions (Balasubramanian & Mahajan, 2001). This
utility is based on the assumption that “adding value” to the online community is an important

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goal of the individual. It suggests that consumers are motivated to engage in eWOM based four
of the motives that were previously identified for traditional WOM: concern for other
consumers, helping the company, social benefits, and exerting power (Henning-Thurau, 2004).
For example, by engaging in eWOM, travellers become better integrated into online travel
communities and gain social benefits for better identification of themselves in public.
2.3.1.2 Consumption Utility
Consumption utility refers to consumers obtaining value through directly consuming the
contribution of other individuals from the online community (Balasubramanian & Mahajan,
2001). Consumption takes place when individuals read reviews and comments regarding a
product or service that are written by other members on the Web-based opinion- platform. The
post-purchase advice-seeking motive is under this utility, as writing or gaining information on
online consumer-opinion platform allow individuals to gain more specific and useful feedbacks
about the product or service they will or have already purchased. These information help
consumers to better use, operate, modify, and/or repair a product (Henning- Thurau, 2004).
2.3.1.3 Approval Utility
Approval utility refers to the satisfaction consumers experience when other individuals consume
and approve of the their contributions on an opinion-platform. These feedbacks can be formal,
such as “contribution rankings” on the platform, or informal, which may happen when another
user publicly or privately praises another’s contributions to the group (Henning- Thurau et al.,
2004). Based on the WOM communication literature, Henning- Thurau et al., identified two
motives that are associated with approval utility: self-enhancement and economic rewards. Self-

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enhancement motivation (Engel et al., 1993; Sundaram et al., 1998) is driven by a person’s
desire to received positive recognition from others. When others read a consumer’s electronic
communication, it provides the consumer with a level of social status, which can become
important to one’s self-concept. In other cases, some eWOM information provider may receive
money or other remuneration from the platform operator. This motivation of economic reward
becomes an important driver of eWOM communication.
2.3.1.4 Moderator-Related Utility
Moderator-related utility occurs when a third party makes the complaining process easier for
consumers. For instance, platform staff may interact with a company on behalf od the
consumer. eWOM communication motives that refer the moderator-related utility are
convenience and problem-solving support through the platform operator. Online platforms
make it more convenient for consumers to complain about a product or service. And with the
hope that platform operators will actively support consumers in solving their problems,
consumers have the ability to express dissatisfaction with low financial and psychological risk.
2.3.1.5 Homeostase Utility
Homostase utility refers to the concept that individuals will act to restore stability after their
original balanced state of being is unbalanced (Henning- Thurau et al., 2003), which could be
restored, in this case, by posting a comment on an online public opinion platform. Motives that
are associated with this utility include expressing positive emotions and venting negative
feelings. Consumers need to express positive emotions because such positive consumption
experience causes a psychological tension inside him or her, which causes the consumer to have
a strong desire to share the joy with someone else (Ditcher, 1966). Relatedly, venting negative
feelings that are associated with dissatisfying consumption experience on a online platform can
reduce the anxiety associated with the negative experience (Sundaram et al., 1998).

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2.3.2 Social Influence Model
The social influence model proposed by Dholakia et al.(2005) suggests that people participates
in virtual community due to the antecedents of individual-level (‘desire’) and group-level driver
(‘social intention’). The model extends from a group-level standpoint, proposing that the
transmission of interpersonal information exchange via eWOM is mainly motivated by social
interactions. Study by Bagozzi (2000) also proposed similar concept of social intention, which
many purchases and consumptions can be made jointly by social groups that shows a strong
sense of social identity.
Bagozzi and Dholakia (2002) explored on this concept, attempting to theorize how virtual
community participation works in terms of compliance, internalisation, and identification. They
use the social psychological model of goal-directed behaviour (Perrugini & Bagozzi, 2001) and
social identity theory (Tajfel, 1978) as the two underlying frameworks and conceptualise
participation in virtual chat rooms as ‘intentional social action’ involving the group. The results
of their study identifies internalisation, such as the congruence of one’s goal with those of group
members, as well as identification, such as the conception of one’s self in terms of the group’s
defining features, as exhibiting significant effects on consumer participation of eWOM.
Compliance, on the other side, does not show to have effects on consumer participation on
eWOM, as participation in virtual community is voluntary and anonymous.
Dholakia et al. (2004) developed the social influence model by extending from Bagozzi and
Dholakia’s (2002) study. The model has three parts: value perceptions, social influence
variables, and decision making and participation. There are also two phases of the model, where
during the first phase, virtual community participants are likely to seek out media with goal-
directed reasons, to fulfil a set of motivations. In the second phase, the participants begin to

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form their social identities, where they come to view themselves as members of the community,
‘belonging’ to the community (Dholakia et al., 2004). Okazaki (2009) extends the model by
introducing uses and gratification, inherent novelty seeking, and opinion leadership, as
antecedents of the social intention to participate in eWOM.
2.3.3 Balance Theory
Various authors have identified that WOM communication mainly happens when consumes
experience disconfirmation between their expectation and actual state of products or services
(Anderson, 1998). Similar to the homeostase utility by Henning- Thurau (2004), balance theory
by Heider (1958) suggests that individuals will strive to restore equilibrium after their originally
balanced state has become unbalanced. Heider predicts that when a person perceives tension
that an imbalanced state exists, his or her ability to automatically think and behave are likely to
be affected. This prediction is very much related to Petty and Cacioppo’s (1986) central route to
persuasion assumptions. They suggests that consumers who are in imbalanced states take active
mental steps, for instance, controlled thinking, to reduce tension and achieve balance. Within
the central route to persuasion, consumers actively seek additional information to resolve their
dilemmas.
2.3.3.1 Balanced and Unbalanced State

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Heider (1958) distinguishes two types of relations between separate entities: unit and sentiment
relations. Heider explains that when separate entities are perceived as belonging together, such
as members of a family, they comprise a unit. Sentiment relation refers to the positive or
negative feelings or valuation that one gives to an entity, such as a person, activity, or object.
All relations are formed by the perceiver’s subjective point of view, thus, although a brand may
provide a specific benefit or has a given attribute, if the consumer perceives the opposite, the
two entities, the brand and attribute/benefit, will result in segregation.
Heider (1958) explains that a balanced state is when the relations among the entities fit together
harmoniously, where there is no stress toward change. This means that sentiments are not
entirely independent of the perception of unit connections between entities and that the
perceptions of unit, in turn, are not entirely independent of sentiments. In other words,
sentiments and unit relations are mutually interdependent and that if a balanced state does not
exist, then forces toward this state will arise. And if a change is not possible, the state of
imbalance will produce tension.
When tension caused by imbalance arises in the mind of the individual, the individual is likely
to perform mental and physical effort to eliminate the tension. The cognitive dissonance theory
by Festinger (1957) further explains this disconfirmation. Cognitive dissonance results in an
imbalance state of mind. It causes stress for consumers as the performance of product or service
they purchased does not match their pre-purchase expectation. Therefore, for consumers to
minimize their discomfort from cognitive dissonance, they need actions to minimise this
distress, such as to engage in WOM communication to express their concerns. Heider (1958)
points out that “...if I dislike what I own (a negative sentiment toward an object belonging to a
person) I may begin to like it (change in sentiment) or sell it (change in unit relation).” Whereas

17
when a situation is balanced, when no tension is felt, Heider (1958) suggests that conscious
thinking or actions are not needed to reach the balance state.
2.3.3.2 WOM Motives from Balance Theory
Out of the 17 motives, 5 motives are identified as statistically significant to influence consumers
on their online comment-writing behaviour: concern for other consumers, extraversion/post
self-enhancement, social benefits, advice seeking and economic incentives. Yoo and Gretzel
(2008) later replicate the study, where results of their study show that only 3 factors motivate
travellers to publish online travel reviews. The motivations include helping a travel-service
provider, concerns for other consumers, and desire for enjoyment/positive self-enhancement.
More recent research by Bronner and de Hoog (2011) developed a classification of vacationers’
motivations to participate in eWOM communication and identified two typologies of
motivations when travellers post online opinions: self-directed motivation and other directed
motivation. Self-directed motivation is shown to be more negative and text-only, while other-
directed motivation is more positive with opinions and text.
Based on WOM communication literature, two motives are identified to be associated with
balance theory: expressing positive emotions and venting negative feelings. Balance of

18
consumer’s inner state can be restored by expressing positive emotions that are experienced as
part of a successful consumption experience (Sundaram et al., 1998).
The consumer needs to express positive emotions because his or her positive consumption
experiences contribute to a psychological tension inside him or her that is resulted in a strong
desire to share the joy of the experience with someone (Ditcher, 1966). By performing
behaviour, such as writing comments on Web-based opinion platforms, this tension may be
reduced.
Similarly, venting negative feelings by participating in eWOM communication can lessen
consumer’s frustration and reduce the anxiety associated with the event (Sundaram et al., 1998)
A consumer’s desire for expressing its negative feelings is a major driving force behind the
articulation of negative personal experience. Moreover, consumers could reduce the discontent
associated with his or her negative emotions by sharing a negative consumption experience
through the publication of online comment (Alicke et al., 1992; Berkowitz, 1972).
Previous studies also explored the concept of eWOM though the concept of attitude-intention-
behaviour to understand the traveller’s attitude towards eWOM communication behaviour.
Rokeach (1968) have identified attitude as ‘a relatively enduring organization of beliefs around
an object or situation predisposing one to respond in some preferential manner,’ where attitude
is more enduring and serves as a disposition towards a specific behaviour, rather than a
momentary predisposition. Behavioural intentions, in this concept, are assumed to influence the
motivational factors that affect behaviour. It also indicates how much of an effort individuals
are planning to exert to perform the behaviour, where a person is more likely to perform a
specific behaviour when they have a stronger intention (Ajzen, 1991).

19
Liang et al. (2013) explored individual’s behavioural intentions to use eWOM communication
media. Three antecedents are identified to influence the overall attitude towards eWOM
communication, where overall attitude towards eWOM communication acts as a mediator
between the three antecedents of eWOM communication behaviour and the traveller’s intention
to use eWOM communication media.
2.4 eWOM in Airline Industry
The travel industry, including the airline industry, is increasingly being influenced by the
development of communication. The increasing use of mobile and online communication
technologies has evolved traveller’s WOM behaviour in recent years (Lee & Youn, 2009).
eWOM, in particular, has become an important reference for decision-making such as choosing
tourism destinations, as well as booking hotels and restaurants. (Litvin et al., 2008; Simpson &
Siguaw, 2008). As travel products are considered high- risk and high-involvement purchases,
travellers rely heavily on opinions from their peers, relatives, and friends before making their
decisions (Beldona, Morrison, & O’Leary, 2005) With these opinions becoming more
accessible through online travel companies such as TripAdvisor and TravBuddy, travellers
increasingly rely on peer-to-peer recommendations instead of information and commercials
provided by official companies, as they consider consumer assessments being more trustworthy
(Bansal & Voyer, 2000; Kozinets, 2002). However, companies from the traveling industries
could consider opinions from these platforms as consumer feedbacks, using these information to
improve their goods and services.
eWOM communication channels for the airline industry commonly take place in several ways
including web-based opinion platforms, discussion forums and social networks. Social
networking sites (SNS) such as Facebook, Twitter, and Instagram, have enabled consumers to

20
connect with others by exchanging information, opinions and thoughts about products and
brands (Chu and Kim, 2011). It is an ideal tool for eWOM, as consumers are able to freely
create and disseminate brand-related information in their social networks that are composed of
friends, classmates and other acquaintances (Vollmer & Precourt, 2008). Chu and Kim (2011),
investigated SNS as an online tool for eWOM of social factors that influence consumers’
engagement in eWOM via SNS and identified that tie strength, trust, normative and
informational influence are positively associated with users’ overall eWOM behaviour.
Homophily, as another factor, was shown to have a negative relationship with eWOM
behaviour.
Research by Hvass & Munar (2012) analysed and categorised social media content posted by
airlines and has identified a lack of strategic perspective among airlines’ utilization of social
media, as these information lack uniformity.
2.5 Conclusion
Competition within the UK airline industry has intensified over the years, while the
transformation of technology has made eWOM an important factor travellers consider when
choosing between airlines (Litvin et al., 2008; Simpson & Siguaw, 2008). Social networking
sites have made the exchange of opinions and information between consumers easier (Chu &
Kim, 2011). Therefore, it is important for managers in the airline industry to understand the
motives of eWOM, which could be explained through a motivational perspective (Henning-
Thurau et al., 2003) and more specifically the social influence model (Dholakia, 2002) and
balance theory (Heider, 1958).

21
Chapter 3
Customer Dis/satisfaction in the Airline Industry
3.1. Introduction
When customers are satisfied with a product or service, they are presumably more likely to
repeat purchases, accept other products in the same product line, and participate in favorable
word-of-mouth publicly. Therefore, it is important for marketers to understand the factors that
affect customer satisfaction (Cardozo, 1965). This chapter provides a definition of customer
satisfaction and dissatisfaction (customer dis/satisfaction), as well as its current literature
models and measurements. Moreover, the chapter will also discuss about customer
dis/satisfaction in the airline industry and how perceived safety and travel purpose influence
level of customer dis/satisfaction.
3.2 Definition of Customer Satisfaction
Customer dis/satisfaction is becoming an increasingly discussed topic as companies believe it to
be associated with rewarding customer behaviour for the firm. Various studies, indeed, confirm
this view, such as a positive association between customer satisfaction and loyalty (Anderson
and Sullivan, 1993; Fornell, 1992; Rust and Zahorik, 1993; Taylor and Baker 1994), as well as
between customer satisfaction and the likelihood for customers to recommend the supplier’s
offer to people around them (Hartline and Jones, 1996; Parasuraman et al., 1988; Selnes, 1998).
Customer dis/satisfaction is defined as ‘an outcome resulting from the consumption experience’
(Yi, 1990). This concept is becoming increasingly discussed as it is believed to influence

22
consumer behaviour and their decision-making process. More specifically in this study, we will
be measuring customer dis/satisfaction’s influence on air travellers’ intention to participation in
eWOM communication.
3.3 Models of Customer Satisfaction
Previous researches have examined two ways of explaining customer dis/satisfaction’s effect on
consumer behaviour. The first conceptualization assumes that customer dis/satisfaction is a
bipolar construct, where it takes on a value on a single dimension with the endpoints “low” and
“high” satisfaction (Hausknecht, 1990). The second conceptualization illustrates customer
satisfaction and dissatisfaction as two different constructs, commonly referred to as the “two-
factor theory.” It assumes that some interactions between a supplier and a customer produce
satisfaction, while other facets produce dissatisfaction (Yi, 1990), providing a more complex
image of customer satisfaction than the bipolar structure.
Figure 3.1 Customer Satisfactions As a Bipolar Structure
Source: Hausknecht (1990)
Figure 3.1 demonstrates Hausknecht’s concept of customer satisfaction as a bipolar structure,
where customer satisfaction is a unified concept with endpoints of “low” and “high”
satisfaction.
Figure 3.2 Two Factor Theory of Customer Dis/satisfaction
Low
Satisfaction
High
Satisfaction
Customer Satisfaction

23
Source: Yi (1990)
Figure 3.2 illustrates the two factor theory by Yi (1990). This theory suggests customer
satisfaction and dissatisfaction as two orthogonal axes that are independent of one another,
where positive experience are more likely to produce customer satisfaction and negative
experience are more likely to produce customer dissatisfaction.
3.3.1 Theories of Customer Dis/satisfaction on WOM
Positive
Experience
Negative
Experience
Customer
Satisfaction
Customer
Dissatisfaction

24
Of the relationship between customer satisfaction and WOM, Hart (1990) believes that
customers tell more people about their experience when they are dissatisfied, as negative events
are likely to produce a stronger response than positive events (a “negative bias”). According to
Taylor (1991), one main reason for this may be because negative emotions signal that actions
need to be taken, whereas positive emotions do not.
However, there are also other researchers that believe positive events produce a stronger
response than negative events (a “positive bias”) (Holmes and Lett, 1977; Fornell and
Westbrook, 1984). These researchers explain that individuals have higher intention to strive for
interpretation in positive rather than negative as a basis of interaction with others. Customers
are also likely to reinterpret distort, minimize negative aspects, or even enter denial when faced
with information that challenges their positive conceptions. The fact that individuals want to
appear rational may cause them to leave positive information rather than negative (Johnston,
1995).
Soderlund (1998) believes these two views on customer satisfaction’s relationship to be non-
contradictory, but simply referring to different parts of the customer satisfaction continuum.
The association is believed to be negative in the lower part of the continuum and positive in the
higher part.
3.4 Measurement of Customer Satisfaction

25
Various measurements instruments of satisfaction have been proposed and utilized in customer
dis/satisfaction studies, including evaluative cognitive measures, emotional measures, and
behavioural measures. Figure 3.1 shows the measures of customer dis/satisfaction.
Figure 3.3: Measures of Customer Dis/satisfaction
1. Evaluative (Cognitive) Measures
a. Overall, how satisfied have you been with this ______?
100% 90 80 70 60 50 40 30 20 10 0%
Completely Half & Half Not at all
Satisfied Satisfied
b. How satisfied were you with __________?
Very Dissatisfied Somewhat Dissatisfied Slightly Satisfied Neither
Slightly Satisfied Somewhat Satisfied Very Satisfied
2. Emotional Measures
a. Mark on one of the nine blanks below the position which most closely reflects your
satisfaction with ___________.
Delighted Pleased Mostly Satisfied Mixed Mostly Dissatisfied
________ ________ ________ ________ ________
Unhappy Terrible Neutral Never Thought About it
________ ________ ________ ________
b. Based on Oliver 1980: (Agree/ Disagree)
I am satisfied with ______.
If I had it to do all over again, I would _______.
My choice to ________ was a good one.
I think I did the right thing when I decided.
I am not happy that I did what I did about ________.
3. Behavioural Measures
a. Complaint/ Compliment count
b. How likely are you to use _______ in the future?
Very unlikely Unlikely Likely Very Likely
-2 -1 +1 +2
Source: Hausknecht (1988)
Figure 3.3 demonstrates the measurement instruments that were proposed and utilized on
previous customer dis/satisfaction studies. Westbrook and Oliver (1982) have compared a
number of the developed customer dis/satisfaction measures. From two analyses of pilot data,

26
the researchers came up with three conclusions. Firstly, Westbrook and Oliver (1982) found that
likert, semantic differential and a composite verbal scale performed best on convergence verses
divergence criteria. Second, discriminability of various scales seemed to be product class
dependent, meaning that the evaluation of satisfaction is only suitable for measuring customer
satisfaction in relation to products and not services. And finally, the measures did not succeed
very well in discriminating satisfaction from attitude, as a whole.
3.5 Customer Dis/satisfaction in the Airline Industry
As mentioned in earlier sections, previous studies suggests that dis/satisfaction has an influence
over eWOM (Liang et al., 2013), while eWOM is an important factor travellers consider when
choosing between airlines. Two of the utilities that were used for eWOM, focus-related utility
and homeostase utility (Henning- Thurau et al., 2003), are also applicable for customer
dis/satisfaction in the airline industry. Moreover, perceived safety and purpose of travel are
suggested to be two influencing factor for customer dis/satisfaction in the airline industry
(Johnson, Garbarino, & Sivadas, 2006).
3.5.1 Focus-related Utility and Homeostase Utility Theory
Previous study by Liang et al. (2013) explores the effects customer satisfaction has on
traveller’s overall eWOM attitude. The study examines the concepts using focus-related utility
and homeostase utility theory proposed by Henning-Thurau et al. (2004).

27
The focus related utility theory refers to the benefit consumers receive when they add value to
the community through their positive contributions (Balasubramanian & Mahajan, 2011). In the
context of this study, travellers are motivated to post online opinions under the belief that
sharing their experience of travelling by air makes a positive contribution to the online
community. Figure 3.1 include the desires that motivate consumers to contribute in online
opinion platforms.
Table 3.1 Desires Motivating Consumers to Post in Online Opinion Platforms
● To help (or warn) other people about travel products
● To help a firm by recommending their services
● To gain social benefits for better identification of themselves in public
● To develop better social integration into online travel communities
● To shift the power to consumers from firms in the belief that online travel comments
influence public perception of a firm’s corporate image
Source: Liang, Ekinci, Occhiocupo & Whyatt (2013)
As mentioned earlier, the focus-related utility is based on the motives of concern for other
consumers and helping the company. More specifically, the motive of helping the company is a
result of consumers’ satisfaction with a product and their subsequent desire to help the company
(Sundaram et al., 1998). When consumers are satisfied with the product, they are motivated to
engage in eWOM communication to give the company “something in return” for a good
experience (Henning- Thurau et al., 2004).
Moreover, the homeostase utility also explains customer dis/satisfaction’s effect on engagement
in eWOM communication in the airline industry. It suggests that people strive to restore
stability after their balanced state of being was disturbed (Henning-Thurau et al., 2004). In the

28
context of travel experience, unbalanced status may be restored by posting a comment on an
online public opinion platform.
3.5.2 Influence of Perceived Safety on Satisfaction
Consumers perceive traveling by airplane to have various risks (Ringle, Sarstedt &
Zimmermann, 2011). Perceived risk is identified as the subjective expectation of a loss
(Sweeney, Soutar, and Johnson, 1999). It generates feelings of uncertainty, discomfort, and
anxiety (Dowling and Staelin, 1994). If an airline service is chosen despite its perceived
riskiness, it creates cognitive dissonance. This causes customers to lower their pre-consumption
expectation of the airline service to reduce the internal imbalance associated with the risky
purchase. Previous research by Mattila (2001) suggests that when the actual experience of an
airline service is compared to a less risky purchase, the lowered levels of expectations lead to
reduced satisfaction levels, showing that increased perceived risk influences satisfaction
negatively (Johnson, Garbarino, & Sivadas, 2006).
Purchasing a plane ticket implies financial risk, social risk, and psychological risk (Cunningham
& Young, 2002; Roehl and Fesenmaier, 1992). While the listed risks apply to all traveling
services, air travel also exposes travellers to physical risk. Although the airline industry
advertise safety as its “number one priority” (International Air Transport Association, 2010),
while the accident rates have also decreased over the past 20 years (International Civil Aviation
Organization, 2009), accidents related to air travel cannot be prevented completely. With airline
disasters extensively covered by the media, travellers also perceive air travel as more risky than
other forms of transportation. Moreover, increased public awareness of these incidents also
causes travellers to magnify risks associated with low-probability events (Viscusi, 1985).

29
As much as airlines want to reduce perceived risks in consumer’s mind through different safely
and security measures, it is difficult for passengers to factually assess an airline’s safety level.
Therefore, passengers base their evaluation of safety off of the airline’s service quality
(Rhoades & Waguespack, 2000), the aircraft’s appearance, or the intensity of the security
checks at the airport (Johnson, Garbarino, & Sivadas, 2006). Though security checks decrease
perceived risks, it also negatively impacts satisfaction, as it increases waiting time (Gkritza,
Niemeier, & Mannering 2006). Nonetheless, such negative impacts of waiting time are assumed
to be outweighed by the risk-reducing effects of perceived safety. Therefore, safety measures
have a positive influence on customer satisfaction.
3.5.3 Effects of the Travel Purpose on Customer Satisfaction
Personal characteristics are suggested to have moderating effects on consumer’s perception of
safety and risk (Mattila, 2001), while the purpose of travel, such as for business or pleasure, is
also said to influence customer satisfaction. Various studies show that sociodemographic
variables as drivers of customer satisfaction (Fornell et al., 1996; Rigdon et al., 2011; Danaher
and Mattsson, 1998). Purpose of travel influences consumer’s mind-set and behaviours. For
instance, business travellers behave more rationally than pleasure travellers do, as business
travellers fly more frequently (Aksoy, Atilgan, & Akinci, 2003). Business travellers are also
better educated, older, and more acquainted with the routine of flying than pleasure travellers
(Siomkos, 2000), allowing them to assess the low probability of an accident more accurately,
making them less afraid of accidents (Siomkos, 2000). This also means that safety perceptions
are less influential on customer satisfaction when business travellers evaluate the overall
experience of traveling by plane. Therefore, as perceived risk for business travellers decreases,
customer satisfaction increases.
3.6 Conclusion

30
Customer dis/satisfaction was suggested to have an influence on traveller’s intention to
participate in eWOM communication (Liang et al., 2013). Previous studies have viewed
customer dis/satisfaction in different perspectives. While some research assumed that customer
satisfaction is a bipolar structure (Hausknecht, 1990), others suggested customer satisfaction
and dissatisfaction as two independent factors. In explaining customer dis/satisfaction in the
airline industry, focus-related utility and homeostase utility that explain the motives of eWOM
could also be related here (Henning- Thurau, 2004). Finally, other factors such as perceived
safety and travel purpose are suggested to influence customer dis/satisfaction as well.

31
Chapter 4
Methodology
4.1. Introduction
This chapter examines the methodology used in this study to achieve the research objectives. It
describes the conceptual framework, as well as methods used to measure customer satisfaction
effects on eWOM communication. Questions from previous tested and proven studies are used
to measure the concepts. The study consists of three parts: testing of scenarios, pilot study, and
final study. Scenarios used in the final study are first tested to ensure quality and relatability to
the research. These scenarios are then corrected and improved to insure they minimize any
errors that may occur during the final study. A pilot study is then conducted to check that
participants comprehend each question correctly. Any flaws for the pilot study are, thereafter,
detected and revised based on participant comments. Reliability and validity of the study,
measures and data analysis methods are also discussed in this chapter.
4.2 Aim and objectives of the study
This research aims to understand travellers’ eWOM communication behaviour, specifically in
the UK airline industry. This study will be a hypothesis testing study that expands on study by
Liang et al. (2013) to examine one of the antecedents that affect consumer’s intention to
participate in eWOM communication media: customer dis/satisfaction with travel experience.
4.3 ResearchPhilosophy

32
The study needs a well developed research philosophy in order to conduct reliable research. To
achieve this, a suitable research method and implementation of the study need to be decided
after generating the definition of key terms and conceptual ideas. Various data collection
methods are examined and considered before measuring the effects customer satisfaction has on
eWOM behaviours. The two types of research methods include qualitative and quantitative
research method, with qualitative method using focus groups studies, interviews, and
observation and quantitative method using surveys and experimental methods.
Quantitative data collection method is chosen for this study to achieve a representative sample
size and to ensure reliability and quality of research. Quantitative method also overcomes the
study time constraints that influence this academic research. This method enables the
researchers to collect a large number of data in a shorter period of time compared to the
qualitative method (Malhotra et al, 2012). More specifically, online surveys were used in this
study, directly targeting population sample.
4.4 Types of Data
Primary and secondary data are collected during the research process to address a specific
research problem. Secondary data is first collected to ensure a better understanding of existing
and previous studies of the topic. With the background knowledge obtained from secondary
data, primary data are collected using quantitative data collecting method. This research will
examine existing theories of customer satisfaction and eWOM and add new concepts based on
the collected data from primary data. Primary data collection is exercised in this research to
measure the specific research problem concepts and their effects, customer satisfaction’s effects
on electronic word of mouth behaviour.

33
Primary data can be qualitative, quantitative, or a mix of both depending on the information
needed for the study. Qualitative research provides depth, insights, and an understanding of the
research problem using small samples (Malhotra et al., 2012). However, as it does not require
any statistical analysis, which may make the information gathered misleading, the minimum
information gathered through qualitative research is not representative enough. On the contrary,
with data collected from quantitative research technique, studies could be used to quantify and
statistically analyse data to test the hypothesis (Malhotra et al., 2012).
After careful consideration, quantitative research technique is chosen as the data collection
method for this research. As this study is of the nature of hypothesis testing, a substantial size of
sample is needed to ensure reliable findings. Generalizable data collected from this research
would be able to assist other researchers to project to the whole population.
4.5 ResearchDesign
There are three types of research approaches: deductive, inductive, and abductive. Out of the
three, this study uses deductive research approach, developing a hypothesis or hypotheses based
on an existing theory and using a designed research strategy to test these hypotheses (Wilson,
2014). This study examines the relationship between variables with the use of statistical data
gathered from quantitative data collection method.
The study first examines existing eWOM behaviours of travellers in the UK. It then explores
the correlation between customer satisfaction and eWOM. In order to collect a large enough
sample size to achieve generalizable data, online surveys method through Google Forms is used
in this research for data collection. The method of online survey also made interpretation and
analysis of data easier compared to alternative methods. A list of questions were first
constructed and organized in Google Document and transferred to Google Forms for

34
convenience to send and collect data from participants. A pilot study is constructed prior to the
final data collection procedure to ensure questions of the survey is comprehensible by
participants and that it is valid and reliable. The main survey was delivered to participants in
online platforms through a link generated from Google Forms. These platforms include
Facebook Pages, Groups and Messenger, Reddit forum, Twitter, and other online forums.
4.5.1 Conceptual Framework
Extending from previous study by Liang et al. (2013), this study re-examines their theory of
customer dis/satisfaction with travel consumption experience, more specifically in the UK
airline industry. The research contains two research models. One examines customer

35
dis/satisfaction’s effects on traveller’s intention to participate in eWOM communication, where
the other investigates how prior experience of eWOM influences the relationship between
customer dis/satisfaction and intention to engage in eWOM communication. Figure 4.1 shows
the research hypothesis
Figure 4.1 Research Model
The hypothesis of this study aim to explain the effect of the customer dis/satisfaction with air
travel experience on behavioural intention to engage in eWOM communication and whether
social media experience amplifies the effects customer satisfaction has on behavioural intention
to use eWOM communication. Based on the theory that customer dis/satisfaction with air travel
experience has a positive influence on behavioural intention to participate in eWOM
communication and that the relationship between customers dis/satisfaction intention to use
eWOM communication is positively influenced by social media experience, six hypothesis were
constructed to test the indicated theory.
H.1.a. Extremely satisfying experience creates more eWOM compared to satisfactory
experience.
a,b

36
H.1.b. Extremely dissatisfying experience creates more eComplain than satisfactory
experience.
H.1.c. Extremely satisfying experience creates more WOM than satisfactory experience.
H.1.b. Extremely dissatisfying experience creates more complain than satisfactory
experience.
H.2.a Travellers with prior experience of eWOM are more likely to create eWOM
than travellers without prior experience.
H2 b. Travellers with prior experience of eWOM are more likely to create eComplain
4.5.2 ResearchInstrument and Measurement
To measure the relationship between customer satisfaction and eWOM, participants will be
asked to fill out an online questionnaire (Appendix 1). The questionnaire will be separated into
four parts: key terms and screening questions, eWOM communication behaviour, scenarios, and
participants’ information.

37
In part 1, key terms within the questionnaire will be defined and explained. These terms include
electronic word-of-mouth, electronic media, electronic word-of-mouth communication, and
eWOM about the travel experience by plane. Participant will also answer whether they have
previously participated in eWOM communication. Participants who have previously engaged in
eWOM communication will proceed to part 2, where they will answer questions related to their
eWOM communication behaviour, and further proceed to part 3. Participants who have not
previously participated in eWOM communication will move directly to part 3, where they to
read three scenarios of traveling by plane and answer whether they will recommend or complain
about the airline after the experience, either through WOM or eWOM.
The three scenarios include three types of situations: extremely dissatisfied experience (S1),
extremely satisfied experience (S2), and satisfactory experience (S3). The scenarios are
evaluated prior to construction of the questionnaire by participants online and are rated as
dissatisfied experience, satisfied experience, and satisfactory experience. These scenarios are
intentionally designed to represent situations that could cause extreme dissatisfied, extreme
satisfied, or satisfactory experiences.
These scenarios describe three situations air travellers may or may not have experienced. After
reading each scenario, participants are asked to answer four questions in relation to their
intention to recommend or complain, interpersonally or through online platforms. Answers for
these questions are used to understand the correlation between these scenarios and participant’s
wiliness to participate in online or interpersonal WOM and complain.
Finally, participants are asked to give information regarding their gender, age group, education,
nationality, as well as country of residence. These information provides the research

38
demographic knowledge of the participants and better understand the relationship between
customer dis/satisfaction and intention to participate in eWOM communication.
4.5.3 Data Collection and Sampling
Target population needs to be defined precisely to in order to receive accurate results. As this
study will investigate effects of customer dis/satisfaction on eWOM in the airline industry, the
target population are people who travel to and from the UK by plane. However, study
limitations such as time, cost, and access, make it impossible to reach the whole population
from different backgrounds. Therefore, a suitable sampling technique is needed to generate a
representative sample and achieve the objectives in a given time. There are two types of
sampling techniques: probability and non-probability sampling (Malhotra, Birks & Wills,
2010). Probability sampling refers to sampling techniques that involves random selection,
where as non-probability sampling does not (Malhotra, Birks & Wills, 2012). Due to limitation
in time, cost, and access, this study will use non-probability sampling, more specifically
convenience sampling.
Convenience sampling refers to the sampling technique where the researcher gathers samples
from the ones that are the easiest to reach (Malhotra, Birks & Wills, 2012). Participants are
selected based on their convenience. Questionnaires will be posted in non-real time
communication channels, such as Facebook group and pages, Twitter, Reddit, and real time
communication channels, such as Facebook Messenger and WeChat. This technique allows the
researcher to gather a large amount of data with minimal time provided. However, one problem
with this technique is that it involves possibility of bias within the results because the selection
is not random. Data is collected from 60 respondents by delivering the online survey through
social media channels and messenger applications.

39
4.5.4 Scenario Testing
Three scenarios are included in the questionnaire and were tested to ensure they deliver the
expected effects. The scenarios deliver three situations that travellers may experience. These
scenarios depict situations that could cause extreme dissatisfaction (scenario 1), extreme
satisfaction (scenario 2), and satisfactory experience (scenario 3) for travellers when traveling
with airlines. The scenarios were sent to 20 participants (10 males and 10 females) who reside
in the UK to ensure the cases lead to the expected reactions. Scenario 1 and scenario 2 receives
evaluation of the expected outcome, while scenario 3 received mixed results. 100% of the
respondents felt that scenario 1 results in “extremely dissatisfying experience” and that scenario
2 results in “extremely satisfying experience.” However, only 78% of participants voted
scenario 3 as “satisfactory experience.” The remaining 22% of respondents expressed that
scenario 3 would be an “extremely satisfied experience,” showing a need of improvement in
scenario 3 to achieve the expected reaction.
Scenario 3 is improved by attempting to decrease its satisfactory level. Respondents gave
feedback stating that “free food always makes me happy” and suggested the researcher to make
the scenario less inviting. Therefore, the sentence “you receive a bottle of water shortly before
you were served for lunch with food that was edible” was changed to “Food and water were
available for sale around 30 minutes after take-off.” Description of service on the plane was
also changed from “decent” to “normal.” Furthermore, “drinks” were provided throughout the
flight instead of “snacks.” The scenario was evaluated again and has received more percentage
of feedback as “satisfactory” (Appendix 1).
4.5.5 Pilot Study
A pilot study was organized after testing the scenarios. A series of questions were prepared
surrounding the topic of customer satisfaction, eWOM, and the three scenarios. The

40
questionnaire was delivered to 10 people in the UK in order to ensure comprehensibility and to
detect flaws and incoherencies. Participants in the pilot study provide feedback to the
researcher, where the questionnaire is improved based on the comments from respondents.
Based on the feedbacks given, questions that measures well-being were taken off from the
questionnaire.
4.6 Data Analysis Methods
Data collected is organized and analysed using SPSS Statistics Version 21. Validity and
reliability of the data is examined before processing further analysis. Through the application of
paired sample t-test and independent sample t-test, hypothesis of this study are tested (Malhotra,
Birks & Wills, 2012).
4.6.1 Reliability and Validity
Reliability and validity is especially important during the survey design and data collection and
analysis process. Reliability refers to the “extent to which a scale produces consistent results if
repeated measurements are made on the characteristics.” Whereas validity is defined as “the
extent to which a measurement represents characteristics that exist in the phenomenon under
investigation.” Tests and rules are developed to test reliability and validity to ensure the data
and results obtained are reliable and valid (Malhotra, Birks & Wills, 2012).
4.6.2 Paired Sample T-Test
Paired sample t-test is used in this study to understand customer satisfaction’s influence on
eWOM behaviour. The paired sample t-test statistical technique is used to compare two
population means in the case of two samples are correlated. It is used in case-control study,

41
when the samples are matched pairs, or during ‘before-after’ studies. This research will use
paired sample t-test to compare samples from in a case-control study. A controlled scenario will
be compared with two different extreme scenarios to understand the effects extreme scenarios
may have on consumer’s intension in participating in eWOM communication (Malhotra, Birks
& Wills, 2010).
4.6.3 Independent Sample T-Test
Independent sample t-test is conducted to understand whether prior experience to eWOM
communication has an influence on future eWOM communication. Similar to paired sample t-
test, the independent sample t-test also compares two means. It assumes that the variables in the
analysis are split into independent and dependent variables, and that the difference in the mean
score of the dependent variable is based on the influence on independent variables (Malhotra,
Birks, & Wills, 2010).
4.7 Conclusion
This chapter explains the methodology behind this research. This study aims to understand
customer dis/satisfaction’s influence on air traveller’s intention to engage in eWOM
communication, where 6 hypotheses are developed in pursue of understanding this relationship.
Secondary research was done to understand the theories proposed by previous literature,
whereas primary research was done to test out hypotheses listed in this research. With the use of
online questionnaire, quantitative data collection method is used to collect responses from
participants based on convenience sampling. Pilot study was organized prior to the actual
questionnaire to ensure comprehensibility and to minimize errors. Finally, data collected
through the online questionnaire are analysed using SPSS, where paired sample t-test and
independent sample t-test (Malhotra, Birks & Wills, 2012) are used to test the stated
hypotheses.

43
Chapter 5
Findings
5.1 Introduction
This chapter presents the demographics of the sample, validity and reliability of the measures,
as well as data characteristics of this study. It shows hypothesis testing and the effects of
customer satisfaction on travellers’ eWOM behaviour. Furthermore, the chapter displays social
media experience’s influence on customer satisfaction and travellers’ eWOM behaviour.
5.2 Demographics of the Sample
After 1 week of data collection, 60 usable surveys were received. Demographics of the sample
can be shown through gender, age, education, nationality, and current residency of the sample.

44
Figure 5.1 Gender Distribution of the Sample (n=60)
Figure 5.1 demonstrates the gender distribution of the sample. The sample consists of 65%
female and 35% male, which results to 21 male and 39 female within the sample.
Age distribution is another important factor in this study, as travellers from different age groups
may display varied behaviour in communication online. Figure 5.2 displays the age distribution
of the sample.
35%
65%
Gender Distribution
Male
Female

45
Figure 5.2 Age Distribution of the Sample (n=60)
Based on Figure 5.2, 60% of participants fall into the age group of 18 to 25, which amounts to
more than half of the sample. Moreover, the sample also consists of 28% who falls into the age
group of 26 to 25, which is the second largest age group within the sample.
Table 5.1 displays the education level of participants.
Table 5.1 Education Level of the Sample (n=60)
Frequency Percentage
High School 1 2%
College Diploma 3 5%
Bachlor's Degree 18 30%
Master's Degree 38 63%
Doctoral Degree 0 0%
Other 0 0%
As shown in Table 5.1, 63% of participants hold a Master’s degree, whilst 30% hold a
Bachelor’s degree (undergraduate).
60%
28%
7%
3% 2%
Age Distribution
18-25
26-35
36-45
46-55
56-65
Over 65

46
Table 5.2 Distribution of Nationality (n=60)
Nationality Frequency Percentage
British 21 35%
Chinese 16 27%
Taiwanese 6 10%
Thai 3 5%
Filipino 2 3%
Turkish 2 3%
Norwegian 1 2%
Australian 1 2%
Singaporean 1 2%
Malaysia 1 2%
Slovenian 1 2%
Greek 1 2%
Italian 1 2%
Indian 1 2%
Mixed 2 3%
In terms of nationality, 35% of participants are British, followed by 27% Chinese, and 10%
Taiwanese. Figure 5.2 displays the nationality distribution of participants.

47
Figure 5.3 Distribution of Residency (n=60)
As the study investigates the aviation industry, specifically in the UK, 92% of the respondents
live in the UK, whereas the remaining 8% live in other countries (Figure 5.3). These countries
include Hong Kong (China), the United States, Thailand, Italy, and Taiwan.
5.3 eWOM Communication Behaviour
92%
8%
Distribution of Residency
UK
Others

48
Figure 5.4 Distribution of Prior Experience In eWOM
55% of the participants expressed prior experience in using electronic media to communicate
opinions related to traveling with a specific airline (Figure 5.4).
Figure 5.5 Types of Opinions Posted in Non-Real Time Communication Medias
55%
45%
Distribution of Prior Expereince in
eWOM
Yes
No

49
Figure 5.5 shows the types of opinions the sample posted in non-real time electronic media in
the past 6 months. Commenting on photos and opinions appear to be the most common activity
the sample engage in, where 70% of them expressed this activity in the past 6 months.
Furthermore, 58% of the participants sent E-Mails, 53% ticked ‘like/dislike,’ and 49% posted
photos, as well as answered other’s enquiries and questions in the previous 6 months.

50
Figure 5.6 Types Real Time Electronic Media(s) Used
In terms of the types of electronic medias, Facebook Messenger appears to be the most popular
real- time electronic media within the participants, with 84% of participants express using it in
the past 6 months. WhatsApp (72%) and Skype (38%) are the second and third most commonly
used real-time electronic media (Figure 5.6).
Figure 5.7 Types Non-Real Time Electronic Media(s) Used

51
For non real-time electronic media, 79% of participants have previously used Facebook to
communicate travel and tourism related opinions, which is also the most commonly used
channel. Furthermore, 55% of participants used travel information website, while 36% of them
used E-mail (Figure 5.7).
Figure 5.8 Monthly Duration Used for Tourism eWOM on Real Time Electronic Media(s)

52
Figure 5.9 Monthly Duration Used for Tourism eWOM on Non- Real Time Electronic
Media(s)
Figure 5.8 displays the length of duration participants used for communicating travel and
tourism related opinions in a month via real time electronic media. 58% of participants express
using real time electronic media less than 30 minutes per month, while 21% of them expressed
using 40 minutes to an hour per month. For non-real time electronic media (Figure 5.9) 49%
express using less than 30 minutes per month, 27% express using it 40 minutes to an hour per
month, while 21% express using 2 to 4 hours per month.
58%
12%
21%
9%
Duration Used for Real Time Electronic
Media(s) (Monthly)
Less than 30 minutes
40 minutes - 1 hour
2-4 hours
5-7 hours
8-10 hours
More than 10 hours
49%
27%
21%
3%
Duration Used for Non-Real Time Electronic
Media(s) (Monthly)
Less than 30 minutes
40 minutes - 1 hour
2- 4 hours
5- 7 hours
8- 10 hours
More than 10 hours

53
5.4 Hypothesis Testing
Validity and reliability needs to be ensured prior to testing the research hypothesis. The
procedures of factor analysis allow data reduction and summarisation, reducing data into a
manageable level (Malhotra, Birks, & Wills, 2010). Groups of correlated variables are
established, while the relationships among these groups of interrelated variables are examined
and represented in a few underlying factors. (Pallant, 2005; Malhotra, Birks, & Wills, 2010).
To test hypothesis 1 a, b, c, and d, this study compares means of extremely dissatisfying
scenario (S1) and extremely satisfying scenario (S2) are compared with the controlled scenario,
the satisfactory scenario (S3), using the paired sample t-test. For testing hypothesis 2 a and b,
means of S1 and S2 are compared using independent t-test to understand the influence of prior
experience of eWOM on the relationship between customer dis/satisfaction and intention to
engage in eWOM communication.
5.4.1 Hypothesis 1
Table 5.3 shows the results for the paired sample t-test, with scenario 3 (S3) as comparative
scenario.
Table 5.3.: Paired Sample T-Test Results: S3 as Comparative Scenario

54
Variable S3
Satisfactory
Scenario
S1
Extremely
Dissatisfactory
Scenario
S2
Extremely
Satisfactory
Scenario
p-
value
S3-
S1
p
value
S3-
S2
Mean SD Mean SD Mean SD
H1a:
eWOM
2.83 .90 1.82 1.30 3.92 .87 .00* .00*
H1b:
eComplain
2.52 .83 3.82 1.11 1.92 .71 .00* .00*
H1c:
WOM
3.10 .71 1.72 1.12 4.28 .74 .00* .00*
H1d:
Compain
2.95 .79 4.20 1.12 1.92 1.25 .00* .00*
*Statistically significant less than 0.05
The p-values in Table 5.3 confirm that the statistics results for customer dis/satisfaction’s
effects on the four WOM behaviours- eWOM, eComplain, WOM, and complain- meet the
validity criteria, as the p-value for all of the scenario comparisons are 0.00. The p-value of each
of the measurements are statistically significant (p<.05).,
Results of this study show that means for satisfactory experience scenario (S3) are 2.83
(eWOM), 2.52 (eComplain), 3.10 (WOM), and 2.95 (Complain). This study uses S3 as the
control group, where results of S3 will be compared with results in scenario 1, extremely
dissatisfying experience (S1) and scenario 2, extremely satisfying experience (S2).
5.4.1.1 Hypothesis 1a

55
Hypothesis 1a suggests that extremely satisfying experience creates more eWOM than
satisfactory experience. The results of this study support this proposition. As shown in Table 5.3
means for S2 eWOM (x̅ = 3.92) is higher than S3 eWOM (x̅ =2.83). This shows that extremely
satisfying experience has a positive influence on eWOM and that extremely satisfying
experience creates more eWOM compared to satisfactory experience (p<.05). Hence the
hypothesis is supported.
5.4.1.2 Hypothesis 1b
The mean for eComplain (x̅ = 3.82) in S1 is higher than the mean of eComplain in S3 (x̅ = 2.52).
As predicted in hypothesis 1b, extremely dissatisfying scenario create more eComplain than
satisfactory scenario (p<. 05). Therefore, hypothesis 1b is supported by the results presented in
Table 5.3.
5.4.1.3 Hypothesis 1c
Hypothesis 1c posits that S2 creates more WOM than S3. As the mean for S2 WOM (x̅ = 4.28)
is higher than S3 WOM (x̅ = 3.10), it suggests that extremely satisfying experience have a
positive influence on WOM. Therefore, the hypothesis 1c, that suggests extremely satisfying
experience creates more WOM than satisfactory experience (p<.05), is supported.
5.4.1.4 Hypothesis 1d
Hypothesis 1d suggests that S1 should produce more Complain than S3. Mean for S1 Complain
(x̅ = 4.20) is higher than S3 Complain (x̅ = 2.95), indicating that extremely dissatisfying scenario
has positive influence on Complain. The results show that extremely dissatisfying experience
creates more Complain than satisfactory scenario (p<.05). Therefore, the hypothesis is
supported.

56
5.4.1.5 Hypothesis 2a and b
Table 5.4 shows the results of the independent sample t-test.
Table 5.4 Independent Sample T-Test Results: Using Prior Experience of eWOM as
Grouping Variable
Group Statistics
Variable Prior
Experien
N Mean Std.
Deviation
t-Value Significant
(2-tailed)

57
ce
H2a:
S1eWO
M
Yes 33 1.85 1.20 .209 .83
No 27 1.78 1.42 .205 .83
H2b:
S1eCOM
P
Yes 33 3.85 1.09 .243 .809
No 27 3.78 1.16 .242 .810
S2eWO
M
Yes 33 3.97 .728 .519 .606
No 27 3.85 1.03 .502 .618
S2eCOM
P
Yes 33 2.03 1.26 .830 .410
No 27 1.78 1.05 .846 .401
Hypothesis 2 a indicates that travellers with prior experience of eWOM are more likely to create
eWOM than travellers without prior experience, while hypothesis 2b suggests that travellers
with prior experience of eWOM are also more likely to create eComplain than travellers
without prior experience. Based on these two hypotheses, participants who have had prior
experience of eWOM should have a higher mean for S1 and S2 eWOM and S1 and S3
eComplain than participants who have no prior experience. As predicted, means of participants
with prior experience do have slightly higher means in all results compared with means of
participants with no prior experience. However, due to the lack of statistical significance, it is
not significant enough to prove the hypotheses.
As Table 5.4 displays, p-values for all results are higher than 0.05. As p-value of lower than
0.05 is required to indicate statistical significance, results in this independent sample t-test are
not statistically significant. Results from the independent sample t-test all show that there is no
moderation effect. Therefore, hypothesis 2a and 2d are rejected.
The findings indicate that customer satisfaction influences travellers’ intention to engage in
eWOM and eComplain, as well as WOM and Complain. When experiencing an extremely

58
dissatisfying situation, travellers are likely to participate in eComplain and even more likely to
complain face-to-face. Similarly, when travellers experience extremely satisfying situations,
they are more likely to engage in eWOM and also more likely to recommend the airline to their
friends and family face-to-face.
5.4.2 Summary of Hypothesis Testing Results
Table 5.5 presents the hypotheis testing results for this research.
Table 5.5 Hypothesis Testing Results
Hypotheses Supported
H.1.a. Extremely satisfying experience
creates more eWOM than satisfactory
experience.
Yes

59
H.1.b. Extremely dissatisfying experience
creates more eComplain than satisfactory
experience.
Yes
H.1.c. Extremely satisfying experience
creates more WOM than satisfactory
experience.
Yes
H.1.d. Extremely dissatisfying experience
creates more Complain than satisfactory
experience.
Yes
H.2.a Travellers with prior experience of
eWOM are more likely to create eWOM
No
H.2.b. Travellers with prior experience of
eWOM are more likely to create eComplain
No
As shown in Table 5.5, findings from the independent and paired sample t-test supports four
hypotheses of this study.
5.6 Conclusion
Based on the data collected, demographic analysis were first completed to understand the
profiles of participants. Paired sample t-test is first carried out to compare extreme scenarios
with satisfactory scenarios. The results were shown to be statistically significant, where testing
of results were able to continue. Four hypothesis were tested with the resutls from paired

60
sample t-test, while all four hypothesis were supported by the results. Upon completion of the
paired sample t-test, it is seen that customer dis/satisfaction has a positive influence on eWOM,
where extremely dissatisfying experience could cause more online and interpersonal
complaints, while extremely satisfying experience could lead to more online and interpersonal
recommendations. Finally, independent sample t-test is carried out to check whether prior
experience of eWOM makes a difference with the results.

61
Chapter 6
Conclusion
6.1 Introduction
This chapter reviews and provides a summary and explaination of the main findings. Theories
gathered from the literature review are compared with data collected from this study, producing
the contribution of this research. This study is based on the UK airline industry. With the results
analysed and explained, results will be applied to the UK airline industry while managerial
implications will be discssed. This chapter will also discuss the limitation of this study and
reviewing gaps within current literature knowledge. Moreover, this section will also suggest
how the findings could be utilized in future researches.
6.2 Summary of the Main Findings
The aim of this study is to investigate critically on reviews regarding customer satisfaction’s
influence on eWOM communication behavior. The study mainly focuses on the airline industry
within UK. Based on theories proposed by previous studies, this research develops theoretical
frameworks to test customer dis/satisfaction’s impact on traveller’s motivation to conduct
eWOM communication. Moreover, as previous experience of eWOM is shown to be an
influencing factor of future eWOM communication, whether travellers have previously
participated in eWOM was used as a grouping factor in testing its impact.
The literature review first defines the two important factor of this study, eWOM and customer
satisfaction, and then further investigates the theories and models behind these factors.

62
Balanced theory by Heider (1958) is chosen to be the bases of this research. It explores
consumer’s balanced and unbalanced state, suggesting an unbalanced state within a consumer’s
mind is likely to motivate them in communicating online in order to balance their internal state.
Based on this model, 5 motives for eWOM, including customer dis/satisfaction, are identified
by Liang et al. (2013). Liang’s findings suggest that consumers who are either highly satisfied
or highly unsatisfied are more likely to participate in eWOM communication than when they
experience neutral situations (Liang et al., 2013). This study further investigates in this motive,
customer dis/satisfaction, to further verify and understand this theory, more specifically in the
UK airline industry.
eWOM has become an important reference in the decision- making process for travellers.
Travelers increasingly rely on peer-to-peer recommendation instead of information and
commercial provided by companies (Ringle, Sarstedt & Zimmermann, 2011). The airline
industry, being a part of the travel industry, is also affected by eWOM, where travellers actively
communicate online regarding their opinions of specific airlines.
Customer dis/satisfaction, being one of the main motives for eWOM communication is
impactful within the airline industry, as perspectives of the airline hugely influence consumer’s
decision-making process. Customer satisfaction within the airline industry is impacted by
various factors, including service, and more importantly perceived safety and travel purpose
(Ringle, Sarstedt & Zimmermann, 2011).
Based on the knowledge collected from previous literatures, suitable research and analysis
methods were selected to further understand the relationship between customer dis/satisfaction
and eWOM. The study chose paired sample t-test and independent sample t-test as a research

63
instrument. Validity and reliability of data collected were also investigated based on their
significance level before further analysis.
Through looking at data’s significance level, data of the paired sample t-test appear to be
statistically significant, while results from the independent t-test did not achieve the requirement
(p-value < 0.05). While data from the independent sample t-test are not statistically significant,
hypothesis 2 was, therefore, rejected without further analysis.
Based on results of the paired sample t-test, means of two experimental scenarios (S1 and S2)
were compared with the controlled scenario (S3), where hypothesis 1a-d are, at the end,
supported by the results. S1, the extremely dissatisfying scenario, supporting hypotheses 1b and
d, has a higher mean compared to the controlled scenario. The results suggest that extremely
dissatisfying experience creates more online and interpersonal complaints than satisfactory
experience. S2, the extremely satisfying scenario, on the other side, supports hypothesis 1a and
c. Results in the paired sample t-test show that means of S2 are higher than the means of S3,
which suggests that extremely satisfying experience creates more online and interpersonal
WOM than satisfactory experience.
Linking the findings with theories proposed by Heider (1958) and Liang et al. (2013), it appears
that extreme customer dis/satisfaction does, indeed, have an influence on consumer’s eWOM
behavior. Balance theory suggests that extremely dissatisfying and extremely satisfying
situations causes an imbalance state in consumer’s mind. To ease the feelings caused by the
imbalance state, consumers engage in eWOM. Applying the findings in relation to the UK
airline industry, travelers are more likely to engage in eWOM communication when
experiencing extreme dissatisfying and extreme satisfying situations. These dissatisfying or

64
satisfying experiences are likely to be influenced by traveller’s perception on how safe an
airline is and the purpose of their trip, as suggested by Ringle, Sarstedt & Zimmermann (2011).
In terms of prior experience’s influence on intention to participate in eWOM communication,
the results show that participants with prior experience in eWOM are more likely to recommend
or complain online. However, due to data’s inability to achieve statistical significance,
hypothesis 2 a and b are rejected, where the data is unable to conclude that customer satisfaction
has any influence on eWOM behaviour.
Findings of the study propose that four out of six hypotheses are supported. Confirmation factor
analysis is completed to further understand the theories proposed by previous literature, where
customer dis/satisfaction is, again, proven to have a positive influence on eWOM (Liang et al.,
2013).
6.3 Contribution of the Study
This study contrinutes to academic studies, as well as to the managerial aspects of the airline
industry. Studies have previously investigated in the various antecedents of electronic word of
mouth in the travel industry (Liang et al., 2013). This study is done based on its suggested
theory and more specifically focuses on one of the suggested influence antecedents- customer
dis/satisfaction. However, minimal studies have focused on customer dis/satisfaction’s
influence on eWOM communication within the airline industry. Therefore, this research closes
the gap by investigating within in this industry.
Previous research have introduced the five antecedents that influence the overall attitudes
towards eWOM and of overall attitude’s influence on the intention to participate in eWOM
communication (Liang et al., 2013). Customer dis/satisfaction has statistically proven to be one

65
of the antecedents that influences overall attitudes of eWOM. However, no previous research
have ever explore whether customer dis/satisfaction has any direct influence on the intention to
participate in eWOM communication. With the support of balance theory (Heider, 1983),
primary research, and suitable data analysis method, this research have found that customer
dis/satisfaction is, indeed, statistically significant in positively influencing intention to
participate in eWOM communication.
This study investigates particularly in the UK airline industry. It provides managerial
implications to this industry. As eWOM is an important influence on consumer’s decision-
making process, it is valuable for managers in this industry to understand what causes their
consumers to participate in electronic word of mouth activities and how airlines could achieve
higher customer satisfaction.
6.4 Managerial Implications
The airline industry is increasingly influenced by the advancement of communication. Digital
communication has become easier due to the increasing use of mobile and online
communication technilogies (Lee & Youn, 2009). Instead of solely communicating their
opinions interpersonally, travellers are now more likely to communicate through electronic
word of mouth. While eWOM is said to be more influencial than traditional WOM, it is
important for managers in the airline industry to understand what motivates consumers to
engage in positive eWOM and what prevents travellers to complain electronically in online
community (Henning-Thurau et al., 2003).
Based on the results of this research, that extremely dissatisfied experience will cause more
eComplain/complain, and that extremely satisfied experience will cause more eWOM/WOM
and, managers in the airline industry need to create more satisfying experience for their

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