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What Make Gamers Loyal to Game Publishers: Examining Brand Loyalty in the Video Game Industry

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The video game industry is a lucrative market with a growing multi-billion dollar global annual revenue. Some notable game companies today are successful in retaining their player base and create millions of loyal gamers. However, there have been a lack of studies to understand the predictor of brand loyalty in the game industry. This study strives to analyse the relationship of different variables and brand loyalty to determine the most significant predictor among it. The methodology entails collecting response data from a group of respondents that acknowledges of playing video games on a routine basis. Statistical analysis of linear regression will then be used to generate findings from the data. The results of the findings make evident that there is a significant relationship between brand loyalty and perceived quality, differentiation, brand engagement, and social influence. Moreover, the finding further reveals that brand engagement is the most significant predictor of brand loyalty in gamers comparably to other variables. This research intends to help marketers in the game industry to better enhance their customers loyalty towards their brand or video games and potentially create re- purchase in the future. It is also crucial to note that there are other factors that limit a further and precise understanding of brand loyalty in the video game industry.
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WHAT MAKE GAMERS LOYAL TO GAME
PUBLISHERS: EXAMING BRAND LOYALTY IN THE
VIDEO GAME INDUSTRY
BY
BERNARD YAP YUNKANG
15062326
Research Project Submitted in Partial Fulfilment of the
Requirement for the Degree of
BSC (HONS) MARKETING
SUNWAY UNIVERSITY BUSINESS SCHOOL
MALAYSIA
JUNE 2019
i
DECLARATION
I hereby declare that the work has been done by myself and no portion of the work contained
in this thesis has been submitted in support of any application for any other degree or
qualification of this or any other university or institution of learning.
______________________________
<BERNARD YAP YUNKANG>
<15062326>
ii
ACKNOWLEDGEMENT
First and foremost, I would like to express my sincere gratitude for my supervisor, Dr Teoh
Chai Wen for the advice and guidance she had provided to me in accomplishing this research
paper. The assistance I had gotten from her was extremely helpful and it can never be
completed without her supervisory. Next, I will equally like to thank all my respondents who
had been kind to take the initiative to contributed their data for this research. Further, my friends
and family who have also contributed by offering non-related support to allow me
progressively complete this research. Perpetually, I sincerely appreciate everyone and anyone
who has contributed to this research paper.
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ABSTRACT
The video game industry is a lucrative market with a growing multi-billion dollar global annual
revenue. Some notable game companies today are successful in retaining their player base and
create millions of loyal gamers. However, there have been a lack of studies to understand the
predictor of brand loyalty in the game industry. This study strives to analyse the relationship
of different variables and brand loyalty to determine the most significant predictor among it.
The methodology entails collecting response data from a group of respondents that
acknowledges of playing video games on a routine basis. Statistical analysis of linear
regression will then be used to generate findings from the data. The results of the findings make
evident that there is a significant relationship between brand loyalty and perceived quality,
differentiation, brand engagement, and social influence. Moreover, the finding further reveals
that brand engagement is the most significant predictor of brand loyalty in gamers comparably
to other variables. This research intends to help marketers in the game industry to better
enhance their customers loyalty towards their brand or video games and potentially create re-
purchase in the future. It is also crucial to note that there are other factors that limit a further
and precise understanding of brand loyalty in the video game industry.
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TABLE OF CONTENTS
DECLARATION i
ACKNOWLEDGEMENT ii
ABSTRACT iii
LIST OF TABLES vii
LIST OF FIGURES viii
Chapter 1: INTRODUCTION 1
1.0 Chapter Overview 1
1.1 Research Background 1
1.2 Problem Statement 3
1.3 Scope and Significance of Study 5
Chapter 2: LITERATURE REVIEW 7
2.0 Chapter Overview 7
2.1 Underpinning theories of brand loyalty 7
2.1.1 Aaker’s Brand Equity Model 7
2.1.2 Oliver’s four-stage Loyalty Model 9
2.2 Perceived Quality 10
2.3 Differentiation 11
2.4 Brand Engagement 11
2.5 Social Influence 12
2.6 Conceptual Framework 13
Chapter 3: METHODOLOGY 15
3.0 Chapter Overview 15
3.1 Research Paradigm 15
3.2 Research Design 15
3.3 Sampling 16
3.4 Measurement 16
3.5 Data Collection Procedures 18
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3.6 Data Analysis 20
3.6.1 Descriptive Analysis 20
3.6.2 Reliability Analysis 20
3.6.3 Factor Analysis 21
3.6.4 Normality Test 21
3.6.5 Multiple Linear Regression Analysis 21
Chapter 4: FINDINGS 23
4.0 Chapter Overview 23
4.1 Demographic Information 23
4.1.1 Presentation and Interpretation of Results 23
4.2 Reliability Test Results 25
4.3 Factor Analysis Results 26
4.4 Normality Test Results 28
4.5 Hypothesis Testing 29
4.5.1 Multiple Linear Regression Analysis Results 29
Chapter 5: DISCUSSIONS 33
5.0 Chapter Overview 33
5.1 Discussion 33
5.1.1 Perceived Quality 33
5.1.2 Differentiation 34
5.1.3 Brand Engagement 35
5.1.4 Social Influence 37
5.2 Theoretical Implications 38
5.3 Managerial Implications 39
5.4 Limitations and Future Research 40
5.5 Conclusion 41
REFERENCES 42
REFLECTIVE REPORT 49
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APPENDICES 51
vii
LIST OF TABLES
Table 3.1: Questionnaire Items 17
Table 4.1: Frequency Statistics of the Demographic 23
Table 4.2: Table of Results for Reliability Test 25
Table 4.3: Table of Results for Factor Loading 26
Table 4.4: Table of Results for Normality Test 29
Table 4.5: Coefficients of Multiple Linear Regressions 29
Table 4.6: Collinearity Statistics of Multiple Linear Regressions 30
Table 4.7: Model Summary of Multiple Linear Regressions 30
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LIST OF FIGURES
Figure 2.1: Aaker’s Dimensions of Brand Equity (1992) 8
Figure 2.2: Oliver’s four-stage customer loyalty model 9
Figure 2.3: Proposed Conceptual Framework 13
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CHAPTER 1
INTRODUCTION
1.0 Chapter Overview
This chapter discusses the industry background of video game with some notable case study
of a few publishers with some successful game titles. The problem statement is also discussed
such as the lack of video game studies especially in the role of marketing. The scope and
significance of the study is also included, as well as the proposed research questions and
objectives.
1.1 Research Background
The revenues of the global video game market in 2018 will be worth more than $137.9 billion
US Dollars, with an estimation of more than 2.3 billion gamers around the world contributing
to the huge market share (Wijman, 2018). This amount is also an increase of 13.3% or $16.2
billion from the year before that. Its tremendous growth is more noticeable especially when
one compare its total global market share in 2012, which was only worth $70.6 billion. The
global game revenues in the industry has more than doubled in only a span of 6 years and it is
also estimated to increase and reach as much as $180 billion in 2021. This extreme growth
signifies that there is an increasing demand for video games entertainment globally. It is also
not just a fast growing industry, it is also considered to be the most exciting category of mass
media in the coming future (Marchand & Hennig-Thurau, 2013). In the video game industry,
it is segmented into a few different segment types which are mobile, console, and pc. The
biggest market share is mainly dominated by mobile devices such as smartphones and tablets
by as much as 51%, following with the second biggest segment which is the console with 25%,
a game console is a dedicated machine that are built to play video games, these are known to
be the PlayStation 4, Xbox One, and Nintendo Switch. The remaining competitive market
segment is held by the PC, with a share of 24%. These are known as computers such as desktops
and laptops.
It is notable that the mobile segment captures more than half of the entire video game
market in the world. One of the most successful game title on the mobile platform is Candy
Crush Saga, released on November 2012 on the Apple’s App Store. Candy Crush Saga is a
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freemium match-three puzzle video game developed and published by King. Freemium is a
business model in digital games or applications whereby a basic service is free of charge while
more advanced features must be paid (Wilson, 2006). A match-three is a tile-matching puzzle
game where the player manipulates tiles in order to make them disappear according to a
matching criterion. Most of King’s financial success comes from Candy Crush Saga as the
company’s peak revenue for a day was reported to earn more than $1 million per day (Gaudiosi,
2013). In 2014, more than 93 million people were playing Candy Crush Saga and based on
King’s IPO filing, it’s revenue for a 3 month period was reported to be over $493 million (King
Digital Entertainment PLC, 2014). It is even more fascinating when it is known that 70% of
the players playing Candy Crush Saga have not pay a single cent in on the game and there are
still able to attain such extreme financial success (Dredge, 2013). Some claimed that Candy
Crush Saga was only pure lucky in becoming a successful hit, but the developer King proved
everyone wrong with another newer sequel game title: Candy Crush Soda Saga with estimated
revenues of $118 million in 2017 (Chan, 2017). Many have studied and deduced that Candy
Crush Saga huge success is attributed to its well defined marketing and psychological execution
by the developers, King (Pahwa, 2018).
Another highly successful video game in the console segment is Grand Theft Auto 5
(GTA 5) that was released on multiple console such as the PS3, PS4, Xbox360, Xbox One and
later on to the PC. Grand Theft Auto 5 is developed by developers and publishers, Rockstar
Games. It is an action-adventure video game that follows three criminals committing heists in
an open world. The Grand Theft Auto 5 has also created many controversies related to its
violence and depiction of women in the game (Macdonald, 2013). Apart from that, it is still
considered as the most financially successful media entertainment title of all time with more
than 100 million sales of physical copies worldwide, while its previous series title, GTA 4 only
sold 25 million copies. This created a total of $6 billion revenue for the game developers and
it is well surpassed some of the best-selling blockbuster films (Cherney, 2018). Although it is
arguably true that these revenue is not only a one time sale from the same consumer, it is
actually a repeat purchase by the same consumer enjoying from the online multiplayer mode
in GTA 5 (Batchelor, 2018). The online multiplayer allows the players to interact with other
players to complete various missions and purchase virtual currency with real-life currency, this
is known as microtransaction. After 6 years of its released, Rockstar is still updating the game
and players are still playing GTA 5. Many have wonder how did Rockstar retain its players to
be loyal to its games with repeat purchases, it is suggested that their success can be attributed
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to its well-executed and marketing budget that cost $70 million to $109 million. Which is close
to their development cost of $137.5 million (Sinclair, 2013).
In the past, playing video games is usually known to be associated as geeky or nerdy.
However, this perception of playing video games will only exist in the past as it is now
considered to be a mainstream media that can create huge impact in contemporary popular
culture. This could already be seen with countless ways of fans of a video game establishing a
culture around video games rather than just playing it (Aoyama & Izushi, 2003). The industry
is known to have close links to other entertainment industry such as the film industry. A known
example will be Pokémon, it was first started off as a video game in 1996, then it was exploited
through tv-series and films which became a popular culture especially among millennials
(Aoyama & Izushi, 2003). According to Henry Jenkins, this could also be known as transmedia
storytelling when content flow across multiple media channels (Jenkins, 2003).
In essence, the video game industry is a highly profitable market with a constantly
growing market share annually and it is segmented into 3 different platforms with mobile
having more than half of the market share. The perspective of video game has shifted and it is
now playing an important part of popular culture similar to films entertainment and unlike the
past, playing a video game is now considered to be a widely accepted form of hobby and
leisure.
1.2 Problem Statement
As the video game industry is expanding constantly, much attention has been done to study the
industry, but the studies are mostly focused on the video game itself, or the interaction between
the players and the games (Crawford, 2012). It has also been studied about the potential of
using games as a way of education. Video games is becoming more and more practical to be
studied in the academic field, but most of the focus in the game industry is largely only relating
to the analysis of the games and players (Egenfeldt-Nielsen, Smith, & Tosca, 2009). In the
video game industry, most of the focus are on the player and video games rather than the impact
of the economy and how it functions. Although the economic aspect could be associated to the
cultural section, but it is still somewhat focusing on the impact of popular culture rather than
the actual economy. It is also worth emphasizing the lack of academic literature providing
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attention to the role of marketing in the video game industry (Nieborg, 2014). A huge
significance of marketing aspect in the video game industry has not received much needed
attention within the academic field of video game studies (Burgess & Spinks, 2014). It is
surprising as the marketing practices has been given little attention to in the video game
industry when the popularity of the industry and revenues is reaching billions in users and
dollars.
One may wonder what are the significant importance of marketing in the video game
industry. As the video game industry is a highly competitive industry with numerous
developers and game titles being released every single day. It may already be considered a
challenge just to catch the consumers’ attention in the already competitive saturated market.
Brand marketing can then be a vital tool for firms to operate successfully in this industry. One
of the most regarded and valuable intangible asset to any firm to gain a competitive advantage
is a brand (Keller, 2013). It can be a competitive advantage for any video game developer to
establish a loyal consumer base. It is a desirable asset to any developers that seeks to thrive in
a highly competitive market. If consumers purchase a new video game based on pleasurable
previous experiences with the brand or out of sheer loyalty, this could help create a competitive
advantage for any developers targeting to be the market leader in the industry.
A literature suggests that customer brand loyalty is the ultimate desirable marketing
outcome in strategic marketing activities (Taylor, 2004). There are many ways to establish and
evoke brand loyalty within consumers, one of the ways is to create a high perceived quality
product that is consistent, reliable, functional and well made. This is then able to satisfy the
consumers by meeting its expectations. This is also known as one of the dimensions of Aaker
(1992) brand equity scale (Aaker 1992). Another dimension that is related to brand loyalty is
differentiation. As products are differentiated from the competitors, consumers may stay loyal
to a product since they will not get similar product satisfaction from other competitors (de
Reus, 2017). Engaging the consumers with the brands often could also increase greater loyalty
to the brand. This is believe to be able to increase the relationship between the customer and
brand (Hollebeek, 2011). Social influence of the brand is also another dimension that could be
able to improve customer loyalty to a brand. By associating with the brand, consumers may be
perceived better by others and make a good impressions on other people, making the consumers
to feel accepted within a community (Baalbaki & Guzmán, 2016). As there are many
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dimensions that could affect the brand loyalty of the consumers, it is unclear which predictor
will play a more significant role in it. Thus, this forms the question of this research:
RQ1: How is brand loyalty influence by social influence, brand engagement,
perceived quality, and differentiation?
RQ2: Which is the most significant predictor of brand loyalty from gamers?
With the following research questions, the research objectives of this study is developed as
follows:
RO1: To examine the influence of Perceived Quality, Differentiation, Brand
Engagement, and Social Influence towards Brand Loyalty.
RO2: To identify the most significant predictor of Brand Loyalty.
1.3 Scope & Significance of Study
This study intends to examine significant predictors of brand loyalty in gamers in the video
game industry. The data collection for this study is to be held physically around the area of
Klang Valley in Kuala Lumpur, Malaysia. Although for online collection, respondents could
originate in different regions and countries. Do keep in mind of the location and nationality of
the required respondent in this study is not a factor as it will not influence the outcome of the
results.
The significance of this study will greatly benefit the game industry by helping game
developers to consider the importance of the role of marketing in the game industry. The
increased demand for video games entertainment also justifies the need for a better method of
capturing a bigger market share by creating brand loyalty within gamers. This study may equip
game developers or publishers with the knowledge of creating brand loyalty to develop and
market their games to effectively reach and retain its customers. Eventually, this will improve
the overall game industry. Moreover, educators in the game industry will also be able to benefit
from this study by understand the business of video games methods and potentially include it
within their teaching syllabus to better educate aspiring game developers. Lastly, groups that
are not in the game industry may also benefit by exploring the brand loyalty frameworks used
6
in this study to implement it in other industries. Academics researchers will also be able to
explore the brand loyalty framework used in this study or even criticize and further enhance it.
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CHAPTER 2
LITERATURE REVIEW
2.0 Chapter Overview
This chapter discusses the literature review of the variables in this study. Literature review of
other research done on the specific area of brand loyalty, perceived quality, differentiation,
brand engagement, and social Influence. After a review of each variables, the hypothesis of the
respective variable is suggested. Moreover, this chapter will also include the proposed
conceptual framework for this research.
2.1 Underpinning theories of brand loyalty
Brand loyalty is known as a consumer’s preference to purchase a specific brand in a product
category. When the consumers perceive a particular brand that offers the perfect product
quality, image, and features, brand loyalty exists within the consumer. Brand loyalty is thus,
related to the preferences and brand attachment of the customers.
There are numerous concept and theories of brand loyalty that defined and discussed of
its implications. However, Aaker’s brand equity model and Oliver’s four-stage loyalty model
is merely discussed for the purpose and relevance of the variables in this study.
2.1.1 Aaker’s Brand Equity Model
Every companies desire to produce maximized profits and gained a sizable market share. In
any industry, achieving financial success is dependent on several crucial factors in the
company. One of the factors may be found in the product of the company’s offerings. To satisfy
the customers, the product has to attain a certain quality of industry standard to meet the ever
changing demands of the market. However, when a product has achieve a certain standard level
of quality, the sustaining success of the business in the market will be eventually be influence
by other additional factors. One key importance of sustaining success in any industry can be
found in the company’s brand and process of branding. Branding could help consumers
organize their knowledge about a product or service in a way that could help make better
decision-making and provide value to the company (Keller, 2013).
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The concept of a brand has no unanimous definition, but a few experts have formed a
definition of their own. According to the American Marketing Association (AMA), the
definition of a brand is: “It is a name, term, design, symbol, or any other feature that identifies
one seller’s good or service as distinct from those of other sellers.” (AMA, 2018). Although
Keller (2013) only see this definition as a simplified definition of branding, he further added a
note and suggested a brand can only be different and distinctive once it has created certain
awareness and reputation in the market.
Figure 2.1: Aaker’s Dimensions of Brand Equity (1992)
The concept of branding has now often been closely associated with the concept of
brand equity introduced by David Aaker (Chaudhuri & Holbrook, 2001). In the concept of
brand equity, Aaker consider that brand loyalty is a source of brand equity, and it is also seen
as the main source of a strong brand equity. As a stronger brand equity will help increase
confidence of consumers in a specific brand rather than other competitors’ brand, and
eventually enhancing the brand loyalty of consumers (Sasmita & Suki, 2015). It is also worth
noting that brand loyalty will also be able to reduced marketing costs and attract more newer
customers through favorable word of mouth and greater resistance to other competitors. As
brand loyalty is the ultimate end goal of branding, it is also known to be a huge contributor to
the overall brand equity of any brands. (Taylor, 2004)
Brand awareness
Perceived quality
Brand association
Brand loyalty
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2.1.2 Oliver’s four-stage Loyalty Model
In many instance, brand loyalty has often been concluded and assumed by consistent
purchasing from customers. It is deem invalid as the repetitive purchase could be linked to
other attributes such as convenience, pricing or availability. To infer brand loyalty in
customers, further analysis of their repeat purchase is required to detect true brand loyalty.
Figure 2.2: Oliver’s four-stage customer loyalty model
To enhance the analysis of brand loyalty in customers, a four phase loyalty model can
assess it through customers’ belief, affection, and intention (Oliver, 1999). The initial phase
begins with the belief that the brand attribute is preferable to competitive offerings, then this
“information” must have an affection preference towards it, and the customer must have high
intention of purchasing compared with other alternatives.
The framework of the four-stage loyalty model developed by Oliver is similar to the
cognition-affect-conation pattern (Oliver, 1999). But it is distinct as the customers can still
become loyal at different stages, accordingly to the depth of customers’ attitude towards the
brand. The first stage of cognitive loyalty is referred to the loyalty based on the mere brand
beliefs. Cognition is based on the prior knowledge or information from recent experiences.
However, if it is a routine transaction, satisfaction may not be processed and the depth of loyalty
is solely transactional.
Affective loyalty is the second phase of loyalty development in Oliver’s framework of
customer loyalty. It is the affection of liking attitude towards the brand from the development
of cumulative satisfying occasions from usage. The loyalty of the customers is shown through
the liking for the brand. Their commitment at this stage is encoded into their mind through
10
cognition and affection from the brand. Despite the deeper loyalty, brand switching is still
evident as data shows that brand defectors stated to have been previously satisfied with their
brand (Oliver, 1999).
Conative loyalty is the next stage of the loyalty development. It is influenced by the
repetition of positive affect toward the brand. This phase will display a more committed brand
specific repurchase and it is the intention of the customer to repurchase a specific brand that is
motivated by the previous stages. In essence, customers desires to repeat their purchase with a
particular brand. On the other hand, the desire is still inadequate without action.
The last stage in the framework is the action loyalty, it is the process of converting the
intentions into actions and it is referred as “action control” (Kuhl & Beckmann, 1985).
Customers at this stage of the loyalty framework will be fully committed to rebuy and they are
also accompanied along with an additional desire to overcome obstacles to prevent their
purchase. Thus, this completes the whole framework of cognitive-affective-conative with an
action stage. By using the brand loyalty theories and models of Aaker and Oliver, it can be
applied to support the study of brand loyalty in the game industry. Hence, brand loyalty will
be used as a variable to examine what are the other dimensions that will effectively increase
the influence of it.
2.2 Perceived Quality
Perceived quality the judgement of the consumers on a product’s overall excellence or
superiority. It is also an asset to distinct brands from different competitor brands and it is also
an important business aspect for many companies interested to enhance its brand equity (Aaker,
1992). Perceived quality is also part of the dimensions of building a brand equity in many
concepts of brand equity. It is also one of the single most important factor in ensuring a good
return on investment. According to Aaker (1992), in a study of 250 business managers who
were asked to point out the sustainable competitive advantage of their business, and the most
chosen factor was the perceived quality of the product. Thus, perceived quality is considered
to be a crucial factor to be accepted as a dimension to influence brand loyalty (Aaker, 1992).
This then leads to our first research hypothesis:
H1: Perceived quality is positively influencing brand loyalty in gamers.
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2.3 Differentiation
The differentiation dimension is known as a brand that is distinctively different from its
competitors in certain aspect of the product offerings. This is also another form of branding by
positioning the product to inform the consumers how unique or different the brand and why
they should purchase it. It is also known that differentiation is a brand image that is consistent
throughout the marketing campaign and consumers should be able to recognize the brand’s
value based on previous experiences with the brand (Keller, 2013). Keller have also identify
differentiation as a dimension marketers need to know to position their brand effectively to
attract the target consumers. Moreover, other than acknowledging as a dimension of brand
positioning. It is also noted by Aaker, differentiation is a branding dimension in the concept of
brand equity under the section of brand associations (Aaker, 1992). As it provides the
consumers a reason to buy a certain product and create a positive feelings towards the brand.
The differentiated attributes could also be a factor that could lead to brand loyalty as it assist
consumers to only choose the differentiated product as other competitors may not provide
similar offerings. A study conducted in Netherlands regarding brand differentiation predicting
brand loyalty found that there is a significant relationship (de Reus, 2017). In another sense, it
also means that the more differentiated a product is perceived by the consumers, the more likely
their brand loyalty will increase. This then form the following hypothesis for the research:
H2: Differentiation is positively influencing brand loyalty in gamers.
2.4 Brand Engagement
The concept of engagement has been given considerable amount of attention across various
academic disciplines. However, engagement concept in marketing literature is only starting to
emerge. It is also promised to be a discipline that may be able to provide a more enhanced
predictions of customer loyalty outcomes (Bowden, 2009). Engagement in marketing is known
as the two-way interaction between the engagement subject, such as the customer and a specific
engagement object, such as the brand. Brand engagement in current research can be defined as
the customer’s individual level of cognitive, emotional and behavioral activity in direct brand
interactions. Direct brand interactions is refer to the customers’ direct, physical contact with a
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brand, rather than indirect brand interaction such as observing the brand through mass
communications. The cognitive of the consumer is related to the activity of concentration or
engrossment on the brand, while the emotional represents the activity related to inspiration and
pride of the brand (Hollebeek, 2011). The customer’s behavioral activity refers to the level of
energy given interacting with the brand. Some of the marketing constructs relative to customer
brand engagement are involvement, interactivity, co-created value, and brand experience
(Bowden, 2009). From the findings of the study, it is found that there is a linear relationship
between the customer brand engagement and loyalty relationship. The levels of customer
interaction and engagement with a brand could favorably impact their satisfaction and loyalty
ratings. Thus, this suggest that brand engagement could be a dimension that it is not often
considered in examining brand loyalty, and this could be a factor that can contribute and
enhance brand loyalty in consumers. This then leads to the next research hypothesis:
H3: Brand Engagement is positively influencing brand loyalty in gamers.
2.5 Social Influence
Social influence is a dimension that many academic researchers has never included it in any
related brand equity scale. It is consumers perceive of how a brand can improves the way they
are perceived by others, such as making a good impression on other people and also giving the
owner or consumer a social approval that could help make them feel accepted (Baalbaki &
Guzmán, 2016). Based on an article that develops a new conceptual brand equity scale called
the consumer-based brand equity scale, it criticized the quantified brand equity scale developed
by Aaker (1991) and Keller (1993). The researcher stated that their brand equity scale did not
take into account of the characteristics of a brand that matters to the consumers, and it lacks
the consumer-perceived factors. In today’s marketplace, consumers are actively participating
in brand identity creation of companies (Vallaster & Wallpach, 2013). Now that consumers are
an active individual in shaping the equity for the brand, it is critical to understand, and measure
the brand equity more effectively. After the researcher has conducted a reliability test on the
proposed dimensions, the final four dimensions of the consumer-perceived brand equity were
validated and one of it were social influence. This dimension describes how will a consumer
gain value by using the brand to achieve social approval and build their own persona
(Thompson & Arsel, 2004). Although this dimension is only associated with brand equity, it is
13
previously mentioned that brand equity is a strong source of contributor by brand loyalty
(Taylor, 2004). Thus, it will be an interesting dimension to include to examine the relationship
between social influence and brand loyalty, especially in the video game industry. This then
leads to the final hypothesis of this research:
H4: Social influence is positively influencing brand loyalty in gamers.
2.6 Conceptual Framework
Figure 2.3: Proposed Conceptual Framework
Based on the hypothesis discussed in the literature review, the conceptual framework is further
developed to examine the attributes and influence on brand loyalty. As perceived product
quality could provide a competitive advantage to satisfy the consumers, it is consider to be a
suitable dimension to examine brand loyalty (Aaker, 1992). Whereas the differentiation of the
brand among different competitors could also provide consumers a reason to purchase a
particular product brand due to the positive feelings towards it. In other concepts of
engagement, it is also discovered to be able to satisfy the customers and lead to a high
prediction of brand loyalty. Moreover, it is not often used in measuring scale of brand loyalty
(Bowden, 2009). Thus, it will be an interesting dimension to be adopted in this study to measure
the significance of brand loyalty. Lastly, social influence is also a dimension that has rarely
been used in academic research as far as the area of branding is concern, and it is discovered
14
to be a reliable dimension used to measure brand equity (Baalbaki & Guzmán, 2016).
Therefore, social influence in also included in the research framework to examine the
relationship towards brand loyalty, especially in the video game industry.
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CHAPTER 3
METHODOLOGY
3.0 Chapter Overview
This chapter discusses the methodology for the study. It includes discussion on the approached
research paradigm, research design, and the sampling required. Moreover, the measurements
used to assess the variables of this study are also discussed, and as well as the items taken from
other research’s measurement. Additionally, the sampling method, sample size, and data
collection procedure will be similarly explained. the analysis of the data, are also explained in
this following chapter. A section will also be dedicated to describe the conduct of pilot study.
Lastly, the tools and software used for the data analysis is explained.
3.1 Research Paradigm
The paradigm approach of this research will be post-positivism as this study’s assumptions of
reality, values, theory and practice is more suited to it. This research seeks to look for a
generalize meaning and findings that can be understood within probability. The purpose of
positivism paradigm is to predict a result, test a theory or find the relationship strength between
variables or even the cause and effect of it (Crotty, 1998). The common research design in the
paradigm of positivism are mainly experimental, correlational, causal comparative and
quantitative. The instrument method of collecting data in this paradigm includes questionnaire,
observations, experiments and test. Hence, positivism is a suitable approach and this then leads
to the design of this study.
3.2 Research Design
Based on the chosen approach of post-positivism in the research paradigm, this research will
be utilizing a quantitative approach to measure the consumer’s brand loyalty that are influence
by the mentioned dimensions. The design chosen for this research is causal, since the objective
of this research is to examine the relationship of brand loyalty between different related
dimensions. Causal will be more of a suitable design for this research, despite its limitation of
not capable of high level of certainty (Reis & Judd, 2000). It can still be used to determine an
inferred causal relation between the variable and dimensions. To conduct the collection of data,
16
a survey method will be use to distribute to respondents to measure their responses. As the
nature of this study, it demands a generalized evidence to understand the cause-and-effect
relationships. Hence, quantitative will be a more preferable and efficient approach in testing
the hypotheses with a large sample size.
3.3 Sampling
The sampling method used in this study will be non-probability sampling, this is due to the
reason of not being able to be confident in generalizing the whole population and the limited
time factor to conduct this whole study. Thus, nonprobability sampling is more feasible and
practical for this study. This then lead us to approach the sampling with purposive sampling.
Purposive sampling is used as the study requires a specific defined group of respondent, whom
play games on a normal or routine basis, there are also known as gamers or game enthusiasts.
Therefore, during data collection, respondent has to ensure and acknowledge they play video
games to proceed with the survey. Moreover, snowball sampling will also be implemented in
this study. Snowball sampling helps increase the sample size by asking a respondent if they
could recommend others they may know who also meet the criteria of a gamer. This research
plan to collect surveys data from 300 number of respondents. This number is achieved by using
a table formula to determine the sample size that is published by the National Education
Association (Krejcie & Morgan, 1970). Thus, the sample size for this research is concluded to
only require 300 respondents to represent the population.
3.4 Measurement
The scale used to measure the respondent responses are interval scaling. The measurement to
be included in the questionnaire will be a 5 point scale to allow the respondent to respond with
agreeableness and disagreeableness towards the given statement. The scale is also known as a
5-point Likert scale (Norman, 2010). Respondent will rate from 1 to 5, which represents
strongly disagree, disagree, neither agree nor disagree, agree, strongly agree respectively. In
Table 3.1, the symbol (X) is the subject to be studied which in the case of this research, it will
be the developer of the game the respondent has played. As this can measure their loyalty
towards the developers.
17
The items used in the questionnaire are adjusted from the past studies with some
retaining its original meanings. The previous studies that adopted the items were mostly
emphasized in other industry such as handheld technology, fast-moving consumer goods
(FMCG), and electronics. Hence, the items chosen in the table accordingly to the variables are
modified for brand loyalty in gamers. For the initial variable, the loading value of the items for
perceived quality ranges from 0.775 to 0.863. Following, the differentiation variable’s items
has a loading value from 0.630 to 0.845. The items for the other independent variable of brand
engagement’s loading value is unavailable as the past studies were conducted in qualitative
methods. Furthermore, the items of social influence has a loading value range from 0.6 to 0.83.
Lastly, the dependent variable of brand loyalty items has a loading value from a range of 0.67
and 0.87.
Variables
Items Description
Source
Perceived
Quality
(PQ)
1. The (X) is of high quality
2. (X) is consistent in the quality it offers
3. The reliability of (X) is very high
4. (X) has an acceptable standard of quality
5. (X) has good workmanship
6. (X) will last a long time
Sweeney & Soutar
(2001), Baalbaki &
Guzmán (2016),
Washburn & Plank
(2002)
Differentiation
(D)
1. (X) is unique
2. (X) is irreplaceable
3. (X) is more superior
4. (X) offer unique experiences
5. (X) offers advantages that others do not
offer
de Reus (2017)
Brand
Engagement
(BE)
1. I feel a personal connection to (X)
2. I can identify with (X)
3. I think about (X) a lot when I play their
games
4. I feel good when I play (X)’s games
5. I feel (X) takes player’s feedback
seriously
6. I feel (X) shares information with me
Habibi, Laroche &
Richard (2014),
Hollebeek, Glynn
& Brodie (2014)
18
Social
Influence
(SI)
1. (X)’s game improves the way I am
perceived by others
2. (X)’s game gives me social approval
3. (X)’s game helps me feel accepted
4. (X)’s game makes me feel important
5. (X)’s game helps me stay in touch with
my friends
6. (X)’s game helps me learn more about
myself and others
Dholakia (2004),
Sweeney & Soutar
(2001)
Brand Loyalty
(BL)
1. I consider myself to be loyal to (X)
2. (X) will be my first choice in gaming
3. I will not buy other brands if (X) is
available at the store
4. I only need (X) when it comes video
games
5. I will purchase games from (X) again
6. I will recommend (X) to others
Washburn & Plank
(2002), Dwivedi
(2015)
Table 3.1: Questionnaire Items
3.5 Data Collection Procedures
As this study aims to understand gamer’s loyalty to a brand, the respondent for this research
will be anyone who plays video game on a normal or routine basis regardless of the game
platforms. The data collection method will be self-administered through online and offline
survey. As self-administered is the lowest cost among other options, requires less staffing, and
could expand geographical coverage. These advantage improves the suitability of this study.
The survey will be distributed offline usually in areas that are potentially more gamers,
such as the Sunway University and College. If it deems possible, the survey will also be
distributed in quarterly gaming related meetups such as Indie Game Dev Hangout, with the
approval of the organizer. Once a potential respondent is approached, respondent will be first
asked if they are a gamer currently playing video games. If so, the potential respondent will be
asked if interested to participate and survey will be handed to fill in.
19
For the online survey, an online questionnaire will be setup using Qualtrics, a data
collection software made for customer experience insight. Respondents could easily access the
online questionnaire through a shorten link or QR (quick response) code to participate the
online survey. To encourage gamers’ full participation and commitment in responding the
questionnaire, a sweepstake reward is utilized. By completing the survey, the participants will
be given a chance to get up to RM30 Steam Wallet Gift Card from a pool of RM100.
Participants do not have to pay anything and only provide their emails to receive the code for
the digital gift card if their respondent number were to be chosen. Due to confidentiality and
privacy, participants can still choose to opt out of this sweepstake by not providing a contact
email as it is stated as optional in the survey. The first section asked in the online survey will
be preliminary questions to determine if respondents meet the necessary criteria to proceed
with the survey. For instance, asking if they play video games or not. If the respond is yes, the
following question will prompt the respondent to list the most favorite recent game they have
been playing. This is to help the respondent to frame and better recall their experiences of
playing the game they had in mind while responding to the questionnaire.
To reach the respondents, a short description about the research and link to the online
survey will be posted on online group chats and Facebook pages that are video game related.
A very popular social platform among gamers is Discord, which mainly consists of dedicated
group chats for different games. The following video game group that will be distributed are
Gameranx, Overwatch, Spellbreak, Apex Legends, PUBG, Dota2, Rocket League and so on.
Moreover, social media page such as Facebook will be reached out to distribute the online
survey. Facebook pages such as Game Development, Game Dev KK, Indie Game Developers,
Unity Developers User Group, Unity 3D Game Developers, Game Developers, Indie Game
Chat, SteamVR, Virtual Reality. Due to the nature of the online chats and social media, pulsing
method of posting will be used to post weekly to ensure the online survey reach a wide range
of audience and also avoid impression of spamming.
Before conducting the online survey, the authority or admin of the online groups and
pages will be contacted to seek approval. Although the online survey may not monitor or
interact with the respondent in case of any clarifications or uncertainties, it is still suitable to
be able to reach a wider range of respondent from different geographical regions that fits the
study’s criteria.
20
3.6 Data Analysis
The analysis of the collect data from the survey will be analyze using Statistical Package for
Social Sciences (SPSS) Statistics by IBM. This is a common software tool used to analyze any
quantitative data. Before the raw data is entered into SPSS, coding will be done to give each
variables’ items a code, such as the items for Perceived Quality will be coded as PQ1, PQ2,
PQ3 and so on. Each respondent will also be given an ID number to track their responses. Data
from the survey will then be entered according to the coding. Before the data is analyze, data
cleaning will be conducted within SPSS to remove any unusable data sets such as the possibility
of out of range value and missing values. Missing values commonly occur when respondent
accidentally skip questions or refuse to answer. Data cleaning will then replace the missing
values. The data analysis procedures that will be conducted for this research includes the
descriptive analysis, factor analysis, reliability test, normality test, and multiple linear
regression analysis.
3.6.1 Descriptive Analysis
Descriptive analysis refers to the transmutation of raw data into a more understandable and
interpretable format (Zikmund, 2010). For this study, the personal details and demographic
variable of the respondent such as gender, age, frequency of playing video games in a week,
and the types of gaming platform used, are tabulated to show a summary of the research
sample’s characteristics.
3.6.2 Reliability Analysis
Reliability test displays the measurement of internal consistency of a scale in a research
conducted, expressed as a number between 0 and 1 (Tavakol & Dennick, 2011). To determine
the reliability of the variables, the value of Cronbach’s alpha is gather through the analysis of
the variables. It is also recommended that the value is well above 0.7 for the relationship
between the variables to be highly reliable and acceptable (Hinton, Brownlow & McMurray,
2014).
21
3.6.3 Factor Analysis
Factor analysis is commonly used to examine concepts that are not easy to evaluate accurately
by minimizing numerous variables into interpretable underlying factors (Rahn, 2008). It is also
sometimes known as dimension reduction which you reduce the dimensions of your data into
one single variable ("What Is Factor Analysis & How Does It Simplify Research? | Qualtrics",
2019). The oblique factor rotation of the results will be observed to determine the coefficient
for the linear combination of the variables. The factor loadings of each item will require a value
of 0.4 and above to be accepted as coefficient in the regression analysis (Arifin & Yusoff,
2016).
3.6.4 Normality Test
Before conducting multiple linear regression analysis, it is crucial to ensure that the sample
data were drawn from a population that follows a normal distribution. Errors will bound to
occur in hypothesis testing if the population data is not normally distributed (Frost, 2018). To
test and determine the normality of the sample data, the skewness and kurtosis of analysis
results will be required to be within a certain proposed value. The value of skewness is always
between 0 and 1, regardless of negative value (Székely & Móri, 2001). Based on the
D’Agostino-Pearson test, kurtosis will always has 2 degrees of freedom, if it is more than 20
sample size. If the kurtosis value is more than 2 or -2, the hypothesis of normality will be
rejected (D'Agostino, 2017).
3.6.5 Multiple Linear Regression Analysis
As the objective of this research is to determine the relationship between brand loyalty and
other dimensions, linear regression will be used to predict the value of the variable based on
the value of another variable. As there are four independent variables in this study. Multiple
regression allow to determine the overall fit of the model and the contribution of each
predictors. This is suitable for the test as this meets the research objective of understanding the
influence and significance of the predictors. To determine the significance of the variables, a
p-value for each variable will be observe in the results of the analysis. Using a 95% confidence
interval, the p-value is required to less than the significance level of 0.05 in order to reject the
null hypothesis (Frost, 2018). The predictor variables are statistically significant if the p-values
22
are equal 0.000. Thus, the multiple linear regression analysis can be used to explain the relation
between the dependent and independent variable in this study.
23
CHAPTER 4
FINDINGS
4.0 Chapter Overview
This chapter will interpret the data collected from the survey for this research. The statistical
software package used to analyze the data is IBM SPSS software.
4.1 Demographic Information
The demographics of this research solely consider the gamers’ brand loyalty towards the video
game’s developers that they play. Hence, the research do not limit any age preference in the
participants, although they have to be a playing video game recently. The geographical location
and nationality of the respondent is also not restricted in the aim of this study. Furthermore,
convenience sampling is adopted to reach more suitable respondents for this study. In total of
311 responses were collected from the survey for this study.
4.1.1 Presentation and Interpretation of Results
Profile
Category
Frequency
Valid
Percent
Valid
Gender
Male
Female
240
37
86.6
13.4
Age
Below 20
20 29
30 39
40 49
50 and above
82
147
26
12
10
29.6
53.1
9.4
4.3
3.6
Platform
Played On
Mobile
PC
Console
29
249
33
9.3
80.1
10.6
24
Frequency of
Playing Video
Games Weekly
Once
Twice
Three times
Four to six times
Everyday
15
18
34
69
141
Valid Total
277
100
Missing
Total
34
311
Table 4.1: Frequency Statistics of the Demographic
As seen in Table 4.1, there are 240 male respondents and 37 female respondents, which gives
a total of 277 respondents. It is also evident that the majority of the respondents were male in
this study. The remaining 34 respondents did not state their gender in the survey, thus it is
impossible to determine the gender of the remaining respondents. Due to the application of
convenience sampling as sampling method, the respondents’ genders were not evenly
distributed. The survey were focus more on distributing to respondents that met the criteria at
the convenience of the researcher.
Moreover, as shown in Table 4.1, the majority of the respondents are aged below 29
years old. This is unsurprising as gamers are mostly consist of this age. However, the age of
the respondents do not affect any of the variables in this study. As the aim of the research is
not intent to study the difference of brand loyalty in different age groups. Moreover, it is also
preferable to collect responses from all age groups as this may not create strong biases if the
age of respondents are not limited. The total collected respondents are 311, but 34 respondents
has refused to state their age. Hence, the age demographic for 34 respondents are unknown and
missing. Again, the data shown in the table above do not represent the age demographics of the
whole population, but only the population in the sample. Based in Table 4.2, the highest age
group in this study is aged between 20 to 29 (53.1), followed by below 20 (29.6), 30 to 39
(9.4%), 40 to 49 (4.3%) and lastly 50 and above (3.6%) respectively.
Following, the Table 4.1 shows the video game platform most popularly played on by
311 respondents in this study. The majority of the respondents prefer to play video games on
25
PC. It is then followed by console with 33 respondents and lastly mobile with 29 respondents.
The frequency shown in this table also do not represent the most popular gaming platform of
the whole population, it is merely to gain insight of preferred gaming platform of this
population of study.
Furthermore, Table 4.1 display the frequency of respondents playing video games in a
week for this study. The highest majority of respondents play video games everyday (50.9%),
followed by respondents that play video games four to six times (24.9%) in a week. Continuing
with three times (12.3%), twice (6.5%), once (5.4%) in a week. The data spread of respondents
playing video games in a week is considerably desired as more than half of the responded
respondents stated that they play video games every day. The effectiveness to understand brand
loyalty from these gamers will be higher as more than 50% are able to better recall their
experiences playing the video games. Again, the frequency of respondents playing video games
is not a factor to study the results. It is merely to understand the population sample better in
this research.
4.2 Reliability Test Results
Variables
No. of
Items
Cronbach’s Alpha
Perceived Quality
6
0.849
Differentiation
5
0.757
Brand Engagement
5
0.783
Social Influence
6
0.872
Brand Loyalty
6
0.806
Table 4.2: Table of Results for Reliability Test
The reliability test shown in the Table 4.5 is the overall reliability of the collected data.
According to Hinton et al. (2004), he has recommended that the value of Cronbach’s alpha of
each variables in the study should be above 0.7 for them to be accepted as highly reliable. The
table above has shown that each variables in the study are well above with Cronbach’s Alpha
value from a range of 0.757 to 0.872. For this research, no items were removed in the
26
questionnaire as every Cronbach’s Alpha value of the variables have met the recommended
accepted value of above 0.7. Thus, this overall display that the independent and dependent
variables for this study has a high reliability.
4.3 Factor Analysis Results
Items
Loading
Perceived Quality
PQ1 (Game X)’s developers is of a high quality
.803
PQ4 (Game X)’s developers has a standard of above average industry
quality
.795
PQ2 (Game X)’s developers is consistent in the quality of experience it
offers
.772
PQ3 Developers of (Game X)’s reliability is high
.758
PQ5 (Game X)’s developers has good details and workmanship
.560
PQ6 (Game X)’s developers will last a long time in the industry
.445
Differentiation
D4 Developers of (Game X) offer a unique experience that others can’t
offer
.770
D2 (Game X)’s developers make games that can’t be replaced
.655
D1 (Game X)’s developers make games unique in its category
.508
D5 Developers of (Game X) offer a competitive edge that others do not
have
.478
D3 (Game X)’s developers have games that are always more superior
than others
.468
Brand Engagement
BE6 I feel that (Game X)’s developers share information with me
.776
BE5 I feel that (Game X)’s developers take player’s feedback seriously
.759
BE2 I can be identified with the developers of (Game X)
.832
BE1 I personally feel a connection to developers of (Game X)
.617
BE4 I feel good when I play games by developers of (Game X)
.498
27
Social Influence
SI2 Games by (Games X) gives me social approval among a community
.906
SI3 Playing games by (Game X)’s developers helps me feel more
accepted in a group
.896
SI5 Playing games by (Game X)’s developers helps me stay in touch with
my friends
.688
SI1 Games by (Game X) developers improves the way I am perceived by
others
.618
SI4 Playing games by (Game X)’s developers makes me feel important in
society
.595
SI6 Games made by (Game X)’s developers help me learn more about
myself and others
.534
Brand Loyalty
BL1 I consider myself to be loyal to (Game X) and other games by the
same developers
.721
BL2 Games made by (Game X)’s developers will be my first choice if I
want to play a game
.663
BL6 I will recommend games made by (Game X)’s developers to others
and encourage them to play
.499
BL5 I will purchase more games made by (Game X)’s developers again
.434
BL3 I will not play other games if (Game X)’s developer has a new game
released
.682
BL4 I will only need to play games made by (Game X)’s developers
.636
Note: item BE3 is deleted due to low factor loading
Table 4.3: Table of Results for Factor Loading
Factor analysis is a dimension reduction method that reduces a large set of data into a few
components, which will helps make it easier to understand and interpret the data (Williams,
Onsman & Brown, 2010). To determine if the output results from the factor analysis will be of
value to the study, the factor loadings of each items will be require to be more than the value
28
of 0.4 (Arifin & Yusoff, 2016). Based on the table above, it is shown that the items were above
0.4, else the loading value will not be presented in the table above. For the items that do not
present any value of factor loadings will be removed from the following analysis. As displayed
in the table above, the factor loadings of all items are well within the validity range of 0.445
and 0.906 and measured by a total of 7 factors. This shows that the factor analysis is suitable
for the data collected in this research.
4.4 Normality Test Results
Before conducting analysis for hypothesis testing, it is essential to be certain that the population
from the sample data are normally distributed. Errors in linear regression analysis will occur if
data are not distributed in a normal manner (Frost, 2018). In the first test of normality, the
results output were in a non-normal distribution. The value of skewness of perceived quality
variable were more than -1 while the value of skewness and kurtosis for other variables were
in the acceptable range of value (Székely & Móri, 2001). The results were convinced to be
affected by outliers that causes the distribution of the data in a non-normal manner. Hence, a
few outliers affecting perceived quality variable were removed from the test. The respondent
number 243 and 279 were removed from the analysis and another normality test is followed.
After a second analysis of normality test, the results display the sample data to be normally
distributed.
Based on Table 4.7, the skewness of the sample data displays that the distribution is
considered approximately symmetric as it is within the value range of -0.5 and 0.5. The value
of skewness of all variables were within the range of -0.465 and 0.271. This depicts a normally
distributed sample population. Based on the D’Agostino-Pearson test, the kurtosis of the
variables will be required to be within a value of 2 and -2 to be accepted the hypothesis of
normality (D'Agostino, 2017). Following the table of normality test results below, the kurtosis
value of each variable is well within the range of the accepted range with the values ranging
from -0.899 to -0.326. The hypothesis of normality in the sample population is accepted to
proceed for linear regression analysis.
29
Variables
Skewness
Kurtosis
Perceived Quality
0.271
-0.899
Differentiation
-0.465
-0.326
Brand Engagement
-0.145
-0.452
Social Influence
-0.276
-0.695
Brand Loyalty
0.155
-0.395
Table 4.4: Table of Results for Normality Test
4.5 Hypothesis Testing
In every quantitative research, you are attempting to answer a list of set research questions and
hypothesis. To evaluate it, a method known as hypothesis testing is used to determine the
significance of the variables against another variable (Lavrakas, 2008). For the purpose of this
study, multiple linear regression analysis will be conducted to examine the relation between
the variables to determine the acceptance of the research hypothesis in this research.
4.5.1 Multiple Linear Regression Analysis Results
Table 4.5: Coefficients of Multiple Linear Regressions
Hypothesis
Relationship
Beta
Std. Error
t-statistic
Sig.
Decision
H1
PQ -> BL
0.113
0.065
2.185
0.030
Supported
H2
D -> BL
0.262
0.054
5.247
0.000
Supported
H3
BE -> BL
0.374
0.055
6.499
0.000
Supported
H4
SI -> BL
0.164
0.044
3.228
0.001
Supported
30
Table 4.6: Collinearity Statistics of Multiple Linear Regressions
In this study, it composed of four independent variables which are perceived quality,
differentiation, brand engagement, and social influence. In total, there are 4 hypothesis made
for each of those independent variables to study their relationship between them and gamer’s
brand loyalty with multiple linear regression analysis. To interpret the results from the analysis,
the tolerance level is required to be over 0.1, while the variance inflation factors (VIF) should
be less than 10 to be acceptable for the multicollinearity of the variables (Rahman, 2015). Table
4.8 has represented the tolerance level for the variables were all above 0.1 and in the range of
0.573 and 0.760. The VIF for every variables were well below the value of 10. Thus, every
variables in this research has met the requirements for the suitable level of multicollinearity.
Moreover, the most important factor that can determine the brand loyalty in gamers is seen to
be brand engagement as the Beta value is 0.374, which is noticeably the highest amongst other
variables.
Model
R
R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1
0.699a
0.489
0.481
0.62470
2.021
Table 4.7: Model Summary of Multiple Linear Regressions
Model
Tolerance
VIF
Perceived Quality
0.710
1.409
Differentiation
0.760
1.315
Brand Engagement
0.573
1.746
Social Influence
0.736
1.359
31
The value of R-square measures the intimacy of data to the regression line. In another sense, it
is the coefficient of multiple determination for multiple regression (Dancer & Tremayne,
2005). The R-squared is always between 0% and 100%. The higher the value of R-squared will
represent the differences between the observed data and fitted values to be smaller (Frost,
2018). However, a small R-squared does not represent a problem as it can still be a perfectly
good model based on several reasons. Some of it include that certain fields of study will have
greater amount of unexplainable variation, such as studying the behaviors of human which has
generally has values less than 50% (Frost, 2018). It is also stated that people are harder to
predict compared to other things such as physical processes. Hence, the R-squared value
displayed in Table 4.5 is 0.489. Which could also suggest that the model of this study explains
48.9% of the variability of the data around its mean.
The value of Durbin-Watson is a measure test of autocorrelation in the residuals of
regression (Tillman, 1975). Autocorrelation is known as the similarity of time series over time
intervals. In linear regression testing, the model to predict future behaviour is based on the past
behaviour. It is used to forecasts when there is correlation between values of time series and
values that come before and after that time. The Durbin-Watson test is to look for a specific
type of autocorrelation in the regression test which can lead to underestimates of standard error
and cause predictors to be significant when they are not (Field, 2009). In Table 4.7, the value
of Durbin-Watson is 2.021, which signify that it is a negative autocorrelation and considered
to be relatively normal. The value is also considered to be common for non-time series data
similar to the data in this study. Thus, the data reports an autocorrelation in the regression of
the analysis that will not lead to underestimates of standard error.
According to Table 4.5, the t-statistics for perceived quality is 2.185 and the p-value is
0.030 (p < 0.05). This specify that there is a significant relationship between perceived quality
and brand loyalty at a 5% significant level. Thus, H1 is supported. This suggest that perceived
quality of the video game by developers will influence the brand loyalty of gamers. However,
the significance of contribution to the dependent variable of brand loyalty is lower compared
to other variables, as the beta value is 0.113.
Based on Table 4.5, the t-statistics for differentiation is 5.247 and the p-value is 0.000 (p <
0.05). This indicates that there is a significant relationship between differentiation and brand
32
loyalty at a 5% level of significance. Therefore, H2 is supported. This indicates that
differentiation of the video game by developers will influence the brand loyalty of gamers.
It is shown in Table 4.5, the t-statistics for brand engagement is 6.499 and the p-value is
0.000 (p < 0.05). This shows that there is a significant relationship between brand engagement
and brand loyalty at a 5% significant level. Hence, H3 is supported. This clarifies that brand
engagement of the video game by developers will influence the brand loyalty of gamers.
Moreover, the contribution to the dependent variable of brand loyalty is the most significance
as it has the highest beta value of 0.374 compared to the other variables.
For the last independent variable, the t-statistics for social influence is 3.228 and the p-
value is 0.001 (p < 0.05). This displays that there is a significant relationship between social
influence and brand loyalty at a 5% significant level. Thus, H4 is supported. This suggest that
social influence of the game will influence the brand loyalty of gamers. However, the
significance of contribution to the dependent variable of brand loyalty is similar to perceived
quality with a beta value of 0.113.
33
CHAPTER 5
DISCUSSIONS
5.0 Chapter Overview
This chapter will discuss on the results of the analysis displayed in the previous chapter. The
findings of each variables that influence the brand loyalty of gamers will be reviewed
comprehensively. The theoretical and managerial implications from the previous chapter will
also be explored in this following chapter. To conclude the overall research, a discussion on
the limitations and recommendations for future research will be included and end with a
conclusion.
5.1 Discussion
5.1.1 Perceived Quality
Based on the findings in Chapter 4, the perceived quality of a video game made by a developer
will have a significant influence on the brand loyalty of the gamers. As it has a p-value of
0.030, which is below the accepted significance value of 0.05. The output results found in this
study can be considered similar to other studies that similarly found a significant relationship
between perceived quality and brand loyalty in other industry. As the study done by other
researcher were in the cellular phone industry, their study demonstrated that the perceived
quality of the product is positively related to the brand loyalty and purchase intention of the
buyer (Hsin, Yeh & Ya, 2009). The results from this study has displayed similar findings and
further enforce the relationship between perceived quality of the product and customers’ brand
loyalty in the brand.
As perceived quality is not necessarily supported by the actual quality of the product,
but rather the mere quality perceptions of the customers (Zeithaml, 1988). The aspects of
quality perception are stated as preference, aesthetics, colour, shape and appearance. These are
some of the important considerations of the customers while deciding on a perceived quality
product (Lang & Crown, 1993). Apart from those attributes, non-physical features of the
product will help improve the perceived product quality, such as the recognition of the product,
price, country or place of origin, packaging, product information and the shop it was distributed
34
from (Bernués, Olaizola & Corcoran, 2003). In the case of the game industry, if the intrinsic
and extrinsic attributes of the video game are suited well to the customers, it will greatly
enhance its perceived quality and eventually lead to a higher brand loyalty in the developer of
the video game.
However, as stated in the analysis in the previous chapter, perceived quality is not the
most significant contributor to brand loyalty as their beta value (0.113) is the lowest compared
to other variables in this study. This can be further explained by a game released in 2019 by
one of the biggest global game publishers, Electronic Arts. The publisher released a new and
most anticipated game on 22nd February 2019, the game is known as Anthem. Anthem was in
development for years and tens of millions were spent during it. It was revealed in some time
around June 2017, with many loyal customers of Electronic Arts and eager gamers were
anticipating for the release of it. Be that as it may, Anthem came out as a disappointment and
many gamers stated that it felt incomplete and contained a list of flaws (Gilbert, 2019). Despite
of the disappointment, Electronic Arts still managed to push Anthem as the top-selling game
in the US in February 2019 (Fingas, 2019). On the contrary to that, the loyal fans of Electronic
Arts are still loyal and anticipating for their other current games and upcoming announce titles
(Pereira, 2019). Thus, from this example, it can be deduced that the perceived quality of the
video game despite of its actual quality is positively influencing the brand loyalty of the
gamers. Moreover, it can also be explained of the lowest variable contributor to brand loyalty
in this study.
5.1.2 Differentiation
Following the data analysis results from Chapter 4 of findings, it is discovered that
differentiation has a significant positive relationship towards the brand loyalty of gamers. It is
supported with a p-value of 0.000, which is well below the significance value of 0.05. Based
on other research done in the context of brand loyalty predictor in the hospitality industry, it is
found that in successfully converting a loyal customer from other brands through promotional
trial, it could retain the customer through the means of differentiation in its offering and further
lead to a higher brand loyalty (Tepeci, 1999). This clearly suggests that consumers will be loyal
to a brand if it is distinctive in their product and brand offerings. This relationship may be
argued to be limited to certain industry.
35
However, this results can be further supported by other study done similarly in the video
game industry. A study in Netherlands has found that brand differentiation of a video game
brand will positively predict brand loyalty among gamers, although the study sample
population mainly consists of Dutch members from Generation Y (de Reus, 2017). It was
concluded with a quantitative method of assessing the respondents attitudes towards three
brands in the game industry, which is Nintendo, Sony PlayStation and Microsoft Xbox. The
test conducted in that particular study indicated a similar linear relationship between brand
differentiation and brand loyalty for all three brands. It is also found that the brand that has the
highest effect is Sony PlayStation where brand differentiation can explain a 55% prediction of
brand loyalty for the brand.
Furthermore, more examples can be suggested to explain the relationship of
differentiation towards brand loyalty. Sony PlayStation has been a pioneer in hardware as well
as video games software, they had remain differentiated by ensuring highly anticipated and
high quality video games to be only played by their gaming hardware ("PS4 Exclusives", 2019).
This give Sony an edge with a list of AAA exclusive games that gamers desire to experience.
Ultimately, this sort of differentiation by Sony has led their customers to a higher brand loyalty
in the game industry. Despite the example was mostly from the perspective of video games’
hardware, it can still be deduced similarly in differentiation of video games software by the
means of its aesthetics, gameplay, preferences, and the themed of the video game. Hence, it
can be concluded that differentiation in the video game will enhance the brand loyalty of the
gamers.
5.1.3 Brand Engagement
According to the analysis of the data in previous chapter, the brand engagement of the video
game developers is found to have a high significant relationship with the brand loyalty of
gamers. The displayed p-value of this relationship is 0.000, similar to the previous variable of
differentiation, but in addition with the highest beta value of 0.374 among other variables
measured. The brand engagement discussed here relate towards more on the engagement of the
developers with the gamers playing video games developed by them. The outcome of this
analysis can be found in similar other studies done in studying the antecedents of brand loyalty
from consumer brand engagement.
36
It is proved with findings that the three dimensions of consumer brand engagement has
an influencing role in brand loyalty (Leckie, Nyadzayo & Johnson, 2016). The dimensions of
it were diffuse into three areas of affection, activation and cognitive processing. While the
affection and activation dimensions of it were positively effecting brand loyalty, the cognitive
processing were only found to have a negative effect on brand loyalty. Although it does not
necessarily explain its negative influence of it, it is suggested with proposed fact that the
relationship between brand engagement and loyalty is non-linear (Hollebeek, 2011). The effect
of brand engagement on loyalty will eventually reach an optimal point and diminish. It is
further explained that highly engaged customers will exhibit a lower levels of loyalty due to
fatigue and burnout from repetitive and highly engaging activities. In previous research, it has
also revealed that high levels of exposure to repetitive message will eventually lead to
aggressive response by receivers (Cacioppo & Petty, 1979).
Proceeding the discussion, the relation between brand engagement and brand loyalty
can be further explained through the involvement of the consumers. The study has indicated
that phone service providers are more likely to experience repeat purchase from customers
when they are highly involved in the brand (Leckie, Nyadzayo & Johnson, 2016). Due to the
nature of mobile phone service provider, consumers participation is merely limited to feedback
and ideas on improving their service. For other context of engaging with the consumers, it
could reach to the extension of co-creation of a business offerings. The suggested explanation
can be emphasize on the consumers being able to take a more active role in the production of
product and consumption of it. From this action, the consumers may be able to feel more
connected to the brand from this personal experiences. Thus, leading to a higher brand loyalty.
To further suggest an explanation within the industry studied, the relationship of brand
engagement and brand loyalty can be seen in many successful freemium mobile games. The
ever increasing number of mobile games in the market has caused developers to not merely
acquire players but also retain them (Bhargava, 2016). As there are also numerous high
perceived quality and differentiated mobile games in the market, developers will need to ensure
players are engaged over time to be retained. This lead to many freemium mobile game
developers such as Supercell and King to create a community for their own respective games,
Clash of Clans and Candy Crush. These form of engagement helps their players to be constantly
engaged with the developers and remain loyal to their games, as well as brand. Some of the
engagement that has been done can be seen in their constant content updates to the game as
37
well as allowing their players to contribute ideas and feedbacks to improve the game ("Candy
Crush Saga", 2019). Both developers are also actively engaging with their players on social
media by posting relevant content that appeal to them. The updates are also a form of reminder
to return to their game after a possible break. Hence, the engagement seen in these developers
has ultimately led their customer to a higher level of brand loyalty. From the suggested
explanation, it can strongly support the analysis result of a strong relationship of brand
engagement and brand loyalty, as well as the high beta value among other variables.
5.1.4 Social Influence
Based on the findings in Chapter 4, the social influence of the video game has a positive
influence on the brand loyalty of the gamers playing the game. The analysis of the results
demonstrated with a p-value of 0.001, which is below the accepted significance value of 0.05.
The social influence referred in this study is the social identity of the gamers playing the video
game of their choice, as well as the brand identity association with the developers. The results
of the analysis between social influence and brand loyalty is similarly supported by previous
study done in understanding the effects of it.
In a research done in testing a social identity model and integrated model that leads to
brand loyalty, it discovered that the brand identification of the customers with the brand do not
only have a direct effects on brand loyalty, it also found a significant indirect effects on
perceived value, customer satisfaction and brand trust, which further enhance the effects of
brand loyalty (He, Li & Harris, 2011). The study continue to understand the relationship
between it by expanding the brand identity model and found that brand identification of the
customers will mediate the effect of the brand identity of the brand on perceived quality,
customer satisfaction and brand loyalty. This findings can be explained with the suggestion
that customers could develop a strong attachment and identification with the brand
(Bhattacharya & Sen, 2003). The social identification of the customers then lead the path to an
increased loyalty towards the brand.
Moreover, other research has also discover similar findings that brand identification
indeed has an effect on brand loyalty, but it is not a direct effect (Kim, Han & Park, 2001). As
there is a positive effect of brand identification on word-of-mouth, and the analysis reported
that word-of-mouth has a significant effect on brand loyalty. Hence, the research concluded
38
that brand identification has a significant indirect relation towards brand loyalty (Kim, Han &
Park, 2001). In addition to the effects, the brand personality of the brand, which also known as
brand identity, has a direct effect to positive word-of-mouth. This similarly enhances the
indirect effect on brand loyalty. The researcher then concluded the existence of strong
significance between social influence and brand loyalty.
To specifically further emphasize the explanation of the results in the video game
industry, the brand identity of a video game played can be seen affecting the loyalty of gamers
towards it. Taking an example of Rage 2, a video game that is published and released by a
pioneer publisher, Bethesda Softworks, the visuals of Rage 2 incorporates a distinctive brand
identity with a personality that is wild, fun and aesthetically punk rock. This gives the video
game a strong brand identity for the game as well as the developers and publishers (Lives,
2018). Hence, gamers whom desired to be identified with the game may display a higher brand
loyalty towards the franchise as well as the developers. If the developers of Rage 2 were to
develop a similar new game in the future, gamers will be incline to repurchase from them.
Therefore, this example stated could be the possible explanation of the positive relationship
between social influence and brand loyalty in the results. However, the significance of this
variable is not as significance as other variables measured in this research. Besides, the beta
value (0.113) of social influence is similar to the variable of perceived quality. It is also resulted
as one of the lowest beta value among other variables. Thus, social influence may not be the
strongest contributor of brand loyalty, but it still has a significant effect towards it.
5.2 Theoretical implications
This study has justify that consumers’ brand loyalty can be linked from different dimensions
such as perceived quality, differentiation, brand engagement, and social influence. This theory
may be able to be applied in the game or any other related industry. As businesses are always
becoming more competitive with many options and choices to choose from, creating brand
loyalty in customers could be the only ultimate answer to retain them from switching brands.
However, brand loyalty in customers are also becoming more challenging to achieve as
additional factors are emerging from an ever changing environment.
Numerous highly reliable theories and models of branding, especially in the context of
brand loyalty, are mostly form before the popular norms of using smartphones and social
39
medias. Thus, these trends has created environments in our society which may have shifted the
consumers’ mind and behaviours to unexpectable outcomes. Studies and research is necessary
to constantly question the existing models and theories of branding that strives to predict the
loyalty behaviours of consumers. Thus, the framework developed for this study may be able to
continue to understand the changing significant predictors of brand loyalty, as much as a
particular industry is concerned.
The results of this study has suggested that one of the most significant predictors of
brand loyalty is found to be the brand engagement between the company and customers.
Additionally, it is more significant than the predictors of perceived quality and differentiation
variables in this study. Hence, researchers may be able to utilize the framework in this study to
measure brand loyalty in customers or re-emphasize their findings, especially for the most
significant predictor of brand engagement.
Despite the framework in this study is developed mainly for the game industry, it may
similarly be conducted or further test to predict brand loyalty in other industry. The predictor
variables of brand engagement and social influence are still considerably new in understanding
brand loyalty. By implementing it in other research, it can experiment the significance of those
variables in other industry. If the significance of it are similar, the framework will have
potential to be further developed and support other studies of brand loyalty.
5.3 Managerial implications
The most evident practical implication from this study is to enhance the branding of the
publishers or game developers and observe the significance of the examined variables. To
highlight some of the value that may be implemented in a marketing strategy from this study,
it is discovered from this study suggest the first and foremost important factor to consider in
customer brand loyalty in the game industry is the brand engagement of the developers or
publishers.
It is interesting to discover that it has the highest predictor of brand loyalty among other
variables in this research. It is understandable as gamers may feel more connected to a company
or brand if they are constantly interacting with them inside and outside of the game. To enhance
the brand engagement of a brand, developers that seeks to build brand loyalty in their players
40
could ensure that their involvement, interactivity and co-creation value is at a desirable level.
Also note that over excessive of engagement could be detrimental to the loyalty and negatively
affect the brand. By utilizing players touchpoints such as website, social media pages and
public events, developers could better understand the players by getting positive feedbacks and
encourage co-creation for the game. The emotions and brand concentration of the players may
enhance as they felt more valued from the positive interaction with the makers of the game that
they play. It then leads to a higher satisfaction towards the brand and create a higher level of
brand loyalty in them.
Despite brand engagement as the highest predictors, other variables are similarly
important to consider in building brand loyalty in gamers. The perceived quality of the game
should also compliment the players engagement from the developers. The game has to be of
industry quality as well as valuable enough to consider the time and effort of the players to be
desirably played. It should also consider to be differentiated from another game since players
are prone to play games that has a different offerings from other games in the market. The more
differentiated a game is, the higher the level of loyalty will be created in them. This can be
reemphasize with the fact that no other publishers are providing the same offerings to compete
in the market. This can further be supported by a study done to predict brand loyalty from brand
differentiation and found that the more differentiated a product is, the more likely their brand
loyalty will increase (de Reus, 2017).
5.4 Limitations and Future Research
Even though the study done in this research strives to be objective and prudent in the chosen
design and data collection methods, it is still constrained by limitations in its execution. One
of the several limitations of the study is the lack of time to source for more journals and findings
to enhance the support of this research. Secondly, the data collection method are mostly done
through online. By doing so, it is unsure if participants are distracted or truly understand the
questionnaires in the survey. This have created limitation to reduce the confusion or distraction
that alter the true responses of the respondents which offline method offer. Thirdly, the findings
of this study is considered to be a generalize results in the game industry. As some games
genres or platforms are different in nature, it may have a different outcome for their predictors
of brand loyalty. This limitation can be further supported by Table X.X, which suggested that
the variables in this study could only measure 48.9% of the predictor of brand loyalty. This
41
means there might be other relevant factors that may influence brand loyalty which is not
included in this study. For the application of the framework suggested, it is only limited to the
game industry as previous study has not been done to test the accuracy of the model.
In addition, there are several recommendations that can be considered for future
research such as the replication of a similar study done in another industry or brand context to
support the consistency of the findings and framework. Moreover, qualitative study can be
applied to further examine customer brand loyalty in the game industry. This may provide a
more in depth findings to predictors which are not considered and also clarify any confusion
or misinterpretation of questions. Other than that, as this research merely examines the
variables as the antecedent variables, future research should examine the antecedents of the
each variables of perceived quality, differentiation, brand engagement, and social influence,
regardless of the context of the industry.
5.5 Conclusion
This study has discussed the significant importance of predicting customer brand loyalty in the
video game industry. Although the customer brand loyalty theories from Aaker’s brand equity
model is not entirely used, some of its dimensions are relevant to predict brand loyalty. To
answer the first research question stated, brand loyalty is indeed influenced by all the variables
chosen for this study. The least significant predictor of gamers’ brand loyalty is found to be
perceived quality, leading on by social influence, differentiation and brand engagement. It is
unsurprising as there are previous studies that similarly support the findings, and it is found to
be similar in the video game industry. Following, the findings has also answered the second
research question in this study, of identifying the second most significant predictor of brand
loyalty in gamers. The results has stated that brand engagement is the most significant and it is
suggested that gamers that are constantly engaged will invest more time to build their brand
loyalty. Hence, it is crucial for developers or publishers to ensure their brand engagement is
sufficient to increase their customers loyalty base. Along with the recent surge and growing
revenues of the global game industry, it is required to conduct more relevant research to better
understand the growing industry for years to come.
42
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49
REFLECTIVE REPORT
Based on the previous initial research proposal, there are various changes and improvements
made to enhance the quality of this research paper. In the overall proposal, no major spelling
or grammar errors were present, but they were several typos and sentences that needs to
paraphrase to better convey the meaning. Some of the major changes made were the formatting
errors that was previously done. For instance, including the page number for each pages, using
the right number labelling for the headings, using the right table format and following the right
referencing format. These changes are extremely crucial to avoid any confusion and enhance
the flow of the research paper.
Other than that, the changes made in chapter 1 of the previous research proposal is
minimal. A section known as ‘Definition of Terms’ was removed and suggested to be removed
or amend it into “List of Abbreviation”. Due to the nature of the terms, a section of abbreviation
was not required and removed as a result. The research questions suggested was also amend as
one of the question’s context was similar to the second question. Hence, it was removed and
the remaining research questions were improved. Apart from it, minor changes were made to
update some of the context such as “After 5 years” to “After 6 years” in chapter 1.
Following, there were some notable changes made in the section of chapter 2. The
improvements made were the addition of an underpinning theory which is the Oliver four-stage
loyalty model. The theory of brand loyalty is further supported by the additional theories which
can help better explain the dependent variable of brand loyalty. To improve the flow of chapter
2, the theories are sub-headed by a section known as “Underpinning Theories of Brand
Loyalty”. Furthermore, the literature of brand engagement were improved by adding additional
support from other literature. For example, the sentence statement “The cognitive of the
consumer is related to the activity of concentration or engrossment on the brand, while the
emotional represents the activity related to inspiration and pride of the brand.” is included and
additionally supported by another reference. Other than that, the hypothesis statements for each
variables were rephrase to improve the statement. Such as changing “Perceived quality will
positively influence brand loyalty in gamers.” to “Perceived quality is positively influencing
brand loyalty in gamers.”.
50
Besides, the most significant changes made to the initial research proposal is within
chapter 3 of the research. The first amendments made were the sampling method of the number
of respondents plan to collect for the survey, it was reduced from 380 to 300. This is due to the
fact that is it unnecessary to collect 380 as 300 is sufficient based on a table formula. The
measurement used to measure the responses are also improved by supporting the measurement
methods with references and item loading range for each items. This is to further enhance the
credibility of the measurement items. The data collection procedure is also updated with more
detailed descriptions as demanded by the supervisor. As the distribution of the survey was
online, the name of the social media pages and group chats are listed specifically. For instance,
the Facebook page such as Game Development, Game Dev KK, SteamVR, and so on. Further,
the methods of data analysis is added to clarify any doubts in the previous research proposal.
Each analysis is given a sub-section to discuss on the purpose and interpretation of the output
data. For example, reliability analysis’s purpose is explained by stating “displays the
measurement of internal consistency of a scale in a research” and stated its interpretation
methods with “It is also recommended that the value is well above 0.7 to be highly reliable and
accepted”.
Throughout the research, it gave me a chance to study more about the industry which I
had an interest in, which is the game industry. It also gave me the chance to better understand
different analysis method and the purpose of it. It showed the importance of such processes to
interpret and ensure the integrity of the analysis of the data collected. This research paper has
also allowed me to apply everything I had learned throughout the 3 years in my studies and
completing a thesis which I would never do without any requirement.
To conclude, I had learned every necessary thing to conduct a research project from the
beginning to the end of it. It was challenging for me as reading countless journals was not an
easy task to get used to, but eventually I had gotten familiar and comfortable with reading it.
Collecting 300 respondents for this project is also not an easy feat as time and budget is
constrained. Hence, this research project has taught me the importance of scheduling personal
deadlines and challenging myself to achieve my goals.
51
APPENDICES
Appendix 1: Reliability Statistics of Perceived Quality
Appendix 2: Reliability Statistics of Differentiation
Appendix 3: Reliability Statistics of Brand Engagement
Appendix 4: Reliability Statistics of Social Influence
Appendix 5: Reliability Statistics of Brand Loyalty
52
Appendix 6: Regression Scatterplot
Appendix 7: Questionnaire
Dear Sir / Madam,
I am an undergraduate student currently pursuing B.Sc. (Hons) Marketing at Sunway
University Business School. I am undertaking this research as part of requirement for my
degree. In this research, I am trying to identify the determinants of brand loyalty from gamers
in the video game industry. The purpose of this questionnaire is to get your views on various
aspect of your attitude towards the developers of the video game you play. Your participation
in this study is of an immense value to me and is very much appreciated. Kindly be assured
that all the information provided by you will remain anonymous and will be used for academic
purpose only.
If you wish to decline participation in this survey, you may do so now or at any point of this
survey. However, if you choose to be part of the survey, please cooperate with me by
responding to all the questions asked. Should you have any enquiries, please feel free to contact
the researcher at 15062326@imail.sunway.edu.my.
Your sincerely,
Student Name: Bernard Yap Yunkang
53
Student ID: 15062326
Section A Preliminary Questions
Do you play video games?
Yes
No
Please identify the video game that you played the most (Choose ONLY 1)
Please specify : ______________________________
Please identify the video game platform you played the game on
Mobile
PC
Console
Keeping the video game that you have written in mind, please express your honest and frank
views for all the statements given in the questionnaire.
Please answer the questions below according to your own perception towards the developers
of the video game that you have mentioned in the previous section. Game X will represent the
video game you have mentioned.
Section B
Kindly circle or mark to indicate your disagreement or agreement with each of the following
statements using the five-point Likert scale
(1 = “disagree”, 2 = “somewhat disagree”, 3 = “neither agree nor disagree”, 4 = “somewhat
agree”, 5 = “agree”)
1. Perceived Quality
Disagree
Somewhat
Disagree
Neither
Agree nor
Disagree
Somewhat
Agree
Agree
1
(Game X)’s
developer is of a
high quality
1
2
3
4
5
2
(Game X)’s
developer is
consistent in the
quality of
experience it
offers
1
2
3
4
5
3
Developers of
(Game X)’s
reliability is high
1
2
3
4
5
4
(Game X)’s
developer has a
standard of above
average industry
quality
1
2
3
4
5
54
5
(Game X)’s
developer has
good details and
workmanship
1
2
3
4
5
6
Developers of
(Game X) will
last a long time in
the industry
1
2
3
4
5
2. Differentiation
Disagree
Somewhat
Disagree
Neither
Disagree
nor Agree
Somewhat
Agree
Agree
7
(Game X)’s
developers make
games unique in its
category
1
2
3
4
5
8
(Game X)’s
developers make
games that can’t be
replaced.
1
2
3
4
5
9
(Game X)’s
developers have
games that are
always more
superior than
others.
1
2
3
4
5
10
Developers of
(Game X) offer a
unique experience
that others can’t
offer.
1
2
3
4
5
11
Developers of
(Game X) offer a
competitive edge
that others do not
have.
1
2
3
4
5
3. Brand Engagement
Disagree
Somewhat
Disagree
Neither
Disagree
nor Agree
Somewhat
Agree
Agree
12
I personally feel a
connection to
developers of (Game
X).
1
2
3
4
5
13
I can be identified
with the developers of
(Game X).
1
2
3
4
5
14
I think about the
developers of (Game
X) a lot when I play
their games.
1
2
3
4
5
55
15
I feel good when I
play games by
developers of (Game
X).
1
2
3
4
5
16
I feel that (Game X)’s
developers take
player’s feedback
seriously.
1
2
3
4
5
17
I feel that (Game X)’s
developers share
information with me.
1
2
3
4
5
4. Social Influence
Disagree
Somewhat
Disagree
Neither
Disagree
nor
Agree
Somewhat
Agree
Agree
18
Games by (Game X)
developers improves the
way I am perceived by
others
1
2
3
4
5
19
Games by (Game X)
gives me social approval
among a community
1
2
3
4
5
20
Playing games by (Game
X)’s developers helps
me feel more accepted in
a group
1
2
3
4
5
21
Playing (games by Game
X)’s developers makes
me feel important in
society
1
2
3
4
5
22
Playing games by (Game
X)’s developers helps
me stay in touch with my
friends
1
2
3
4
5
23
Games made by (Game
X)’s developers help me
learn more about myself
and others.
1
2
3
4
5
5. Brand Loyalty
Disagree
Somewhat
Disagree
Neither
Disagree
nor
Agree
Somewhat
Agree
Agree
24
I consider myself to be
loyal to (Game X) and
other games by the same
developers.
1
2
3
4
5
25
Games made by (Game
X)’s developers will be
my first choice if I want
to play a game.
1
2
3
4
5
56
26
I will not play other
games if (Game X)’s
developer has a new
game released.
1
2
3
4
5
27
I will only need to play
games made by (Game
X)’s developers.
1
2
3
4
5
28
I will purchase more
games made by (Game
X)’s developers again.
1
2
3
4
5
29
I will recommend games
made by (Game X)’s
developers to others and
encourage them to play.
1
2
3
4
5
Section C Demographic Characteristics
Gender:
Male
Female
Age:
Below 20
20 29
30 39
40 49
50 and above
Frequency of playing video games in a week:
Once
Twice
Three times
Four to Six times
Everyday
This is the end of the survey.
Thank you for your participation. Your response is greatly appreciated towards my research.
57
Appendix 8: Supervisory meeting forms
58
59
60
61
62
63
64
65
Appendix 9: Turnitin Report
66
67
68
69
70
71
72
... According to a recent study at Sunway University in Malaysia, there is a significant relationship between brand loyalty in games with perceived quality and brand engagement (Yap, 2019). This means that incumbent players who have successfully shipped a hit game and continuously engaged with consumers are more likely to have a loyal brand following. ...
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