Master's Dissertation - The Effectiveness of Online Brand Communities and User Engagement on Influencing Purchase: A YouTube Case Study

King’s College
University of London
The Effectiveness of Online Brand Communities
and User Engagement on Influencing Purchase:
A YouTube Case Study
Zachary B. Miller - T06422
7SSMM511: Dissertation
Words: 11,688
Dissertation Supervisor: Professor Jayne Heaford
Date of Submission: 28th August, 2014
Programme: MSc International Marketing

i. Abstract
Social media has forever changed how marketers communicate with an audience. Gone
are the days of one-way communication from the powerful firm to the passive consumer.
With the assistance of Web 2.0, consumers have a new found power in being able to
communicate to not only firms, but also to each other. Never before have the consumers
voice travelled so far nor a firm been able to get such a wealth of feedback. This free flowing
communication comes during an age when consumers are overwhelmed by intrusive
marketing messages. In the digital age, marketers must adapt by offering engaging content
and inspiring brand community or face irrelevancy.
The purpose of this paper is to examine how effective these tactics are in predicting
positive purchasing behaviour. This research will be accomplished by examining the case of
Rooster Teeth, a company who produces video game content on YouTube. This company is
an ideal choice for study as they provide engaging content and have focused on creating
community since their inception. Furthermore, this company features video game content as a
third party; therefore, the concept of brand trust is also tested.
This paper takes a quantitative approach that builds upon past qualitative research in the
fields of online brand community and user engagement. It is from these theoretical
underpinnings that the concepts were deconstructed into user characteristics and tested
against purchase behaviour. With the data compiled, a series of regressions were run to
extract valuable insight from the 1,591 respondents who had various levels of involvement
with Rooster Teeth.
The findings largely conform to previous work on both user engagement and online
brand communities, however some differences such as the importance of member interactivity
were found. This research has important implications theoretically and managerially as this
medium is largely untested.

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ii. Table of Contents
Abstract. ........................................................................................................................... i
Table of Contents ............................................................................................................ ii
List of Tables .................................................................................................................. iii
List of Figures................................................................................................................. iv
1.0 Introduction ............................................................................................................. p.8
1.1 Significance of the Study ..................................................................................... p.09
1.2 Purpose of the Paper ............................................................................................ p.09
1.3 Outline of the Paper ............................................................................................. p.10
2.0 Literature Review .................................................................................................. p.11
2.1 Online Brand Communities ................................................................................. p.11
2.1.1 The Fall of Communities ........................................................................... p.11
2.1.2 Defining Online Brand Communities ........................................................ p.12
2.1.3 Online Brand Community Characteristics ................................................. p.13
2.1.4 Brand Communities and the Individual ..................................................... p.15
2.2 User Engagement ............................................................................................... p.17
2.2.1 Defining User Engagement ....................................................................... p.18
2.2.2 User Engagement Characteristics .............................................................. p.18
2.2.3 Pull Marketing and Engaging Content ....................................................... p.19
2.2.4 Engaged Users and the Co-Creation of Value ............................................ p.21
2.3 YouTube ............................................................................................................ p.21
2.3.1 Demographics of YouTube ....................................................................... p.22
2.3.2 Native Advertising on YouTube ................................................................ p.22
2.3.3 Rooster Teeth: The Professional User ....................................................... p.23
2.4 Literature Review Summary ............................................................................... p.24
3.0 Statement of Research and Hypotheses ................................................................ p.24
4.0 Methodology .......................................................................................................... p.26
4.1 Research Design ................................................................................................. p.26
4.2 Methodology Review .......................................................................................... p.26
4.3 Questionnaire Design .......................................................................................... p.27
4.4 Data Collection ................................................................................................... p.30
4.5 Ethics.................................................................................................................. p.30
4.6 Statistical Analysis.............................................................................................. p.31

5.0 Results .................................................................................................................... p.32
5.1 Respondent’s Profile ........................................................................................... p.32
5.2 Testing Hypothesis 1: Brand Communities on Purchase ...................................... p.33
5.2.1 Online Brand Community Characteristic: Consciousness of Kind ............. p.33
5.2.2 Online Brand Community Characteristic: Shared Rituals & Traditions…..p.34
5.2.3 Online Brand Community Characteristic: Moral Responsibility ................ p.36
5.2.4 Hypothesis 1: Conclusion .......................................................................... p.36
5.3 Testing Hypothesis 2: Engagement on Purchase .................................................. p.37
5.3.1 Engagement Characteristic: Focused Attention.......................................... p.38
5.3.2 Engagement Characteristic: Endurability ................................................... p.38
5.3.3 Engagement Characteristic: Novelty ......................................................... p.39
5.3.4 Engagement Characteristic: Control .......................................................... p.40
5.3.5 Engagement Characteristic: Trust .............................................................. p.41
5.3.6 Engagement Characteristic: Motivation ..................................................... p.42
5.3.7 Engagement Characteristic: Conclusion .................................................... p.43
5.4 Testing Hypothesis 3: Online Brand Community & Engagement on Purchase….p.44
5.5 Testing Hypothesis 4: Influence and Trust .......................................................... p.45
6.0. Discussion ............................................................................................................. p.46
6.1 Online Brand Community ................................................................................... p.47
6.2 User Engagement ................................................................................................ p.49
6.3 Bridging Online Brand Community and User Engagement ................................. p.51
6.4 Rooster Teeth: Trust and Influence ..................................................................... p.52
7.0 Conclusion ............................................................................................................. p.54
7.1 Managerial Implications ..................................................................................... p.54
7.2 Limitations and Future Research ......................................................................... p.56
8.0 Appendices ............................................................................................................ p.59
9.0 Bibliography ......................................................................................................... p.72
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iii. List of Tables
Table 1 – Questionnaire Design..................................................................................... p.29
Table 2 – Consciousness of Kind Regression ................................................................ p.34
Table 3 – Shared Rituals and Traditions Regression ...................................................... p.35
Table 4 – Moral Responsibility Regression ................................................................... p.36
Table 5 – Online Brand Community Regression ............................................................ p.37
Table 6 – Focused Attention Regression ........................................................................ p.38
Table 7 – Endurability Regression ................................................................................. p.39
Table 8 – Novelty Regression........................................................................................ p.40
Table 9 – Control Regression ........................................................................................ p.41
Table 10 – Trust Regression .......................................................................................... p.42
Table 11 – Motivation Regression ................................................................................. p.43
Table 12 – Engagement Regression ............................................................................... p.44
Table 13 – Community and Engagement Regression ..................................................... p.45
Table 14 – Rooster Teeth Trust and Influence Regression ............................................. p.46

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iv. List of Figures
Figure 1 – Online Brand Community Member Characteristics ....................................... p.14
Figure 2 – Engagement Characteristics .......................................................................... p.19
Figure 3 – Respondents: Age ......................................................................................... p.32
Figure 4 – Respondents: Country of Residence ............................................................. p.32
Figure 5 – Respondents: Occupation ............................................................................. p.33
Figure 6 – Consumer Engagement Process in Virtual Brand Communities .................... p.52
Figure 7 – Transfer of Meaning Process ........................................................................ p.53
Figure 8 Process to Increase Share of Watch ............................................................... p.55

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1.0 Introduction
In the past few years, marketing practitioners all over the world have begun fixating
themselves on all things digital. Terms such as “Web 2.0”, “user engagement”, “online brand
communities”, “native advertising”, “pull marketing”, and “crowdsourcing” have dominated
the vocabulary of marketers seeking to create a new source of value and a competitive
advantage over rivals. Successful companies must adapt faster than their competitors less
they find themselves obsolete. As a result, today’s modern company must focus significant
resources into these techniques to capitalize on the new rules of marketing. Therefore, the
research question is: Does online brand community membership and/or user engagement
predict purchasing behaviour via YouTube and to what extent does a third-party affect
trust?
Researchers hurriedly produce knowledge about the evolving rules of online marketing.
Much work has been done in understanding online consumer behaviour, but significant
advances must still be made. Of great challenge to pioneering digital researchers is that the
web evolves at a blisteringly fast pace. New information becomes outdated faster than ever
before. Regardless, researchers are able to identify important trends and establish concepts
that are highly valued by firms. As will be highlighted in the literature review, researchers
largely agree with one another that these digital marketing concepts are crucial in capturing
and retaining today’s tech-savvy consumer. However, these digital theory advances have yet
to be tested on the number one video website in the world, YouTube, and if they can actually
predict an increase to purchasing behaviour.
This study seeks to test the significance of two key topics in digital marketing – online
brand communities and user engagement. Specifically, to what degree do these activities
predict positive purchasing behaviour? This work will be completed through the case study of
Rooster Teeth, a company that produces video game content and publishes it on YouTube.

Given that YouTube is largely under-researched, this paper will also seek to better understand
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Rooster Teeth’s unique position as a third-party by testing for consumer trust.
1.1 Significance of Study
Firms increasingly spend more money on Internet advertisements year-to-year than any
other form of media (see Appendix A). This research is significant to Rooster Teeth in that it
provides the firm a clearer understanding of their advertising options in the face of a shifting
media landscape. Additionally, the firm will be better able to understand how to take
advantage of YouTube and develop tactics that result in increased sales. Finally, this data
provides Rooster Teeth with in-depth market research about their consumers and ways to
improve current practices.
This paper finds its place amongst the literature by taking the deconstructed
characteristics of online brand community and user engagement and determining their value
as predictors of purchasing behaviour. By testing each characteristic of these concepts the
paper stands to add a wealth of in-depth knowledge about these fields. Furthermore, by
testing these concepts on a YouTube channel the paper seeks to determine past works
applicability to this untested medium. Finally, user-generated content has become a
significant force and a defining characteristic of Web 2.0. Although this concept is well-documented
in the literature, the issue of consumer trust to this content is not. It is through
Rooster Teeth that the concept of trust will be added to the discussion.
1.2 Purpose of the Paper
The main purpose of this research is to investigate if online brand community
membership and/or user engagement could predict an increase in purchasing behaviour.
Assuming existing models of online brand community and user engagement characteristics,
this research sought to test those models through Rooster Teeth Productions. Additionally,

this research aimed to provide detailed insight of these states by breaking them down to the
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characteristics that comprise them.
Rooster Teeth was chosen because it is no. 37th most subscribed channel on YouTube
(Socialblade.com, 2014) they provide engaging content, users report traits of community, and
an established consumerist culture based upon video games. Additionally, they are an ideal
candidate for study as they are the first wave of companies that began as user-generated
content and have become a full-fledged professional brand generating millions of pounds a
year. Moreover, the users of YouTube, demographically speaking, are the same as those who
play video games. Video game players are already predisposed to engaging with media. The
similarities of user engagement through warm media (i.e. media you interact with) make this
an ideal choice for study.
1.3 Outline of the Paper
This paper builds strength to the argument that online brand community and user
engagement are important concepts in the digital age of marketing. The extensive literature
review compiles the most recent information available about these two fields and provides
theoretical underpinnings for the current study. It is through the quantitative methodology
that the paper seeks to build upon previous theory. New insights into these fields are offered
by testing each individual characteristic and its relationship to purchasing behaviour. The
discussion will allow the paper to directly compare and contrast previous work. The new
insight developed will prompt recommendations for Rooster Teeth, limitations of the present
study, and ideas for future research.

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2.0 Literature Review
The purpose of this section is to provide background information and a foundation for
this study. This will be accomplished by reviewing the three major fields of study: online
brand community, user engagement, and YouTube. The objectives of each subsection is to
review the historical research origins, evaluate various definitions as created by pioneering
researchers, examine the characteristics that make up these terms, and detail relevant work
done on how it all applies to the individual. It is through the literature review that this paper
will be effectively positioned to discuss findings and answer the research question.
2.1 Online Brand Communities
The popularization of the Internet has allowed companies to transcend beyond the
physical limitations of the brick and mortar store and look for new, innovative ways to
interact with and engage consumers. The added benefits of the Internet to today’s modern
firm cannot be overstated. Firms that connect with consumers on the Internet can take
advantage of a myriad of benefits such as a wealth of customer data, ease of communication,
crowdsourcing, new product development ideas, etc. (Pitt et al., 2002; Howe, 2006).
However, it has only been the marriage of physical brand communities and Web 2.0 that has
allowed communication to flow effectively in all directions adding value to both firm and
consumer alike.
2.1.1 The Fall of Communities
Communities have existed far longer than its current Internet form. The origins of
community are historically situated in critiques off modernity (Muniz & O’Guinn, 2001).
During the 19th century many sociologists were gravely concerned that community was being
replaced by a mass-produced society. In Ferdinand Tonnie’s (1887) ‘Community and
Society’, the author formally distinguishes the terms community and society. Community is
defined as a customary, familial, emotionally rural group of people often determined by

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geographical location. Society, however, is described as much more mechanical,
individualistic, and rationally urban. The general discourse is that community, which is
portrayed as more natural and real, is being replaced by a more depersonalized, mass-produced
human experience via society.
Commercial consumption played a large role in the change from community to society
(Lasch, 1991). This seismic change was made possible through the rise of modern
communication systems (Muniz & O’Guinn, 2001). Mass advertising only became possible
through the popularization of technologies such as radio and television. These advances in
communication allowed for brands to transcend geographical limitations and create a shared
brand meaning across a much larger group of people than previously possible. These
innovations of mass media created what is now known today as a consumer culture. The
growing centrality of the individual consumer is said to be critical in the loss of community.
Indeed, branded products were significant to the shift between a pre-modern community and
modern society (Leiss, Kline, & Jhally, 1990; Marchand, 1985).
2.1.2 Defining Online Brand Community
An online brand community is defined as “affiliative groups whose online interactions
are based upon a shared enthusiasm for, and knowledge of, a specific consumption activity or
related group of activities” (Kozinets, 1999, p. 254). Muniz & O’Guinn (2001) added to this
definition by stating, that online brand community is “a specialized non-geographical bound
community, based on a structured set of social relations among admirers of a brand” (p.412).
This new definition highlighted the crucially important asset of “non-geographically bound”
which was previously missing.
Online brand community vary from traditional (physical) brand communities in many
ways. An online brand community is a type of brand community that takes place in a virtual

setting in which the members’ interaction is primarily Internet-mediated (Fuller, Jawecki &
Muhlbacher, 2007). Of significance, an online brand community exponentially enhances the
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ease by which members of a community can find one another and communicate.
Consequently, the ability to have a centralized online meeting place significantly decreases
fragmentation such as the geographical limitations of local brand communities. For example,
IkeaFans.com allows users from across the globe to discuss the furniture retailer Ikea. It is in
this centralised location that Ikea online brand community members can discuss new
products, ways to customise products, and share home decoration techniques (Ikeafans.com,
2014). Indeed, having a centralized online place for a community to converse eases virtually
all aspects of building and maintaining an online brand community.
2.1.3 Online Brand Community Characteristics
Muniz & O’Guinn (2001) claim three commonalities (markers) of online brand
communities that are always present: consciousness of kind, shared rituals and traditions, and
moral responsibility. As described by the researchers, consciousness of kind is the most
important marker as it is the connection members feel toward the brand, but even more
importantly, to one another. This feeling of “we-ness” allows members to have a sense of
how they differ from non-members (Bender, 1978). Additionally, Anderson (2006)
documents that communities larger than small villages are, to some extent, sustained by
notions of imagined, understood others. This concept of imagined others plays an important
role here. An Internet community that is “non-geographically bound” can have millions of
members; far beyond the number of people it is possible to actually know. Therefore, an
aspect of being part of an online brand community is imagining that others are similar to the
user and that they adhere to the communities’ accepted behaviour.

The next marker, rituals and traditions, exist to perpetuate the shared history, culture,
and meaning of the online brand community. Rituals “serve to contain the drift of meanings;
they are conventions that set up visible public definitions” and social solidarity (Douglas and
Isherwood, 1996, p.65; Durkheim, 1965). These rituals and traditions centre on shared
consumer experiences with the brand. When tradition is understood and reciprocated it
validates the members understanding of their community. Moreover, celebrating the brand
through tradition enforces why community members are devoted. These traditions serve to
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reduce dissonance and further enhance positive brand meaning.
The final marker, moral responsibility, is the felt sense of duty or obligation to the
community and to individual members (Muniz & O’Guinn, 2001). This sense of
responsibility by existing members can help integrate and retain members, which is necessary
for survivability of the community as interaction between members is vital. In addition,
McAlexander et al. (2002) found that moral responsibility is the reason community members
seek help from one another. This type of dissemination of information results in additional
discourse which bolsters overall community health. Figure 1 summarises the characteristics
of an online brand community as defined by Muniz & O’Guinn.
Figure 1 – Online Brand Community Member Characteristics (Author generated)

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2.1.4 Brand Communities and the Individual
Membership to an online brand community can have many effects on the individual.
They offer value in that they are used as an important reference group. Furthermore, many
researchers report that people develop friendships based on their common interests and
passions which can further enhance the trust established of an online reference group (Bickart
and Schindler, 2001; Constant, Sproull and Kiesler, 1996; Kozinets, 2002). In the following
paragraphs online brand communities and the individual will be detailed including reference
groups, process of assimilation, and various roles community members take on.
In general, consumers attach importance to the opinions of others while making
purchase decisions (De Valck, Van Bruggen and Wierenga, 2009). Consequently, word-of-mouth
has been a constant factor for consumers throughout the ever-changing landscape of
marketing tactics. A reference group is any person or group of people who significantly
influence an individual’s behavior (Bearden and Etzel, 1982). For many decades, marketers
have crafted media messages to target opinion-leaders rather than a passive mass audience
(Katz and Lazarsfeld, 1955). In today’s digital climate, Internet personalities have become
opinion-leaders and can have massive reach challenging even that of traditional celebrities
(Griffith, 2014; Rose, 2014).
Today, a new term is used to describe the sharing of information by online reference
groups: word-of-mouse (Helm, 2000). Given the ease of access to information granted by the
Internet, the opinions of others is more crucial now than ever before. A wealth of research has
been conducted on online reference groups and the effects on the individual. Bickart and
Schindler (2001) found that, in the context of online bulletin boards, information produced by
other consumers is considered more credible and relevant than any marketing communication
from the firm. In a study of over 1,000 consumers, customer reviews were ranked as the most
important social media tool having a positive to significant impact on buying behaviour.

Furthermore, a third of consumers (33%) cited Amazon.com as a source of information when
seeking to purchase a new product (Businesswire.com, 2010). Information shared by other
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consumers has significant effects on purchasing decisions.
The literature demonstrates that the primary reason for consumers to seek out reference
groups is to avoid uncertainty. Uncertainty avoidance theory suggests that online consumer-to-
consumer communications may serve an important role in moving a consumer closer to a
positive purchase decision (De Valck, Van Bruggen and Wierenga, 2009). Moreover,
consumers are more likely to search for and accept negative online word-of-mouth
communication in a situation where they lack information and experience; especially when
perceived risk is high (Herr, Kardes & Kim, 1991; Richins and Bloch, 1991; Rogers, 1983).
Indeed, online brand communities have the greatest influence on an individual as a reference
group. This is especially true during the information search of the consumer decision process
(De Valck, Van Bruggen and Wierenga, 2009) (see Appendix B). Hagel (1999) accurately
stated that virtual communities owe their very existence to information exchange between
members.
When people first enter an online brand community they are unfamiliar with the
environment, the other members, and the rituals and traditions (Kozinets, 2002). Knowledge
on these aspects and assimilation takes time (Rothaermel and Sugiyama, 2001).
Consequently, the length of time someone is part of an online brand community greatly
affects the value the individual receives from it. Walther (1995) found that people typically
progress from using a group for simple information gathering and eventually evolve to social
activities with other like-minded individuals. Moreover, Okleshen and Grossbart (1998)
found in their study of Usenet groups that if consumers consider themselves to be members
of an online community they are more apt to being influenced in their purchasing behaviour.

Advertising research theorised that incidental exposure to marketing messages is not
enough for consumers to recall information. It is only through repetition can any significant
learning be achieved (Fletcher, 1980; Wicks, 1992); this is known as the truth effect (Begg,
Anas and Farinacci, 1992). The same can be said of individuals and an online brand
community. De Valck et al. (2009) explains that the frequency with which someone visits an
online brand community and the duration of each visit likely affect the extent of community
influence. Moreover, De Valck et al. goes on to state that younger, less educated members of
a community are much more influenced than expert peers. Keeping in line with Walther’s
(1995) findings, an individual evolves from simple information search to social interaction if
community exposure is extended. In summary, researchers are largely in agreement that as
the value of the community increases for the individual, the influence on the consumer
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decision process is greater.
Online brand community members can interact with each other and the brand in various
ways. De Valck et al. (2009) explain that “receivers” or people who seek to obtain
information do so to learn about the behaviour and choices of others. Consequently, this may
enhance enthusiasm, knowledge reservoir, and reduce cognitive dissonance (Festinger, 1962).
Thus, the authors hypothesize that those members retrieving information (stage two of the
consumer decision process) are the most likely to be influenced by community input.
2.2 User Engagement
User engagement has become a topic of interest to practitioners and consultants across
a diverse number of industries (Sashi, 2012). It is through user engagement that marketers
hope to keep consumers’ attention for longer and, in doing so, lead to higher loyalty and
purchase (Calder, Malthouse and Schaedel, 2009). This user engagement sub section of the
literature review will provide various definitions, characteristics, the change from push to pull
marketing, and how users are co-creating value.

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2.2.1 Defining User Engagement
User engagement as a term has evolved over the past few decades. In 1991, Laurel
defined it as “the state of mind that we must attain in order to enjoy a representation of an
action” so that we may experience computer worlds “directly, without mediation or
distraction” (p112-3). In 1996, Jacques elaborated on this definition to include the effect on
the individual - “Engagement is a user’s response to an interaction that gains, maintains, and
encourages their attention, particularly when they are intrinsically motivated” (p.103). Over a
decade later, O’Brien & Toms (2008) supplemented the working definition by stating that
engagement is the user’s overall evaluation of the experience. Finally, Sutcliffe (2010) added
to the definition through the lens of the source of engagement: “Engagement explain[s] how
and why applications attract people to use them” (p.3). These definitions have and continue to
evolve as new interactive technology emerges.
2.2.2 User Engagement Characteristics
This section seeks to dismantle the concept of user engagement into characteristics.
These characteristics are vital to review as it not only provides more depth as to what the
concept actual means, but provides this paper will a foundation for measuring and testing
user engagement as it predicts purchasing behaviour.
The first characteristic of user engagement is focused attention (Webster & Ho, 1997;
O’Brien, 2010). According to these researchers users must be focused on the experience in
order to be engaged. Endurability is the second characteristic of user engagement (Read,
MacFarlane, & Casey, 2002; O’Brien, 2010) and refers to the reflection of enjoyable, useful,
engaging experiences that people want to repeat. Endurability is measured in how likely users
are to recommend the experience to others. Novelty is the third characteristic of user
engagement (Webster & Ho, 1997; O’Brien, 2010). In these works researchers have found
that surprise, unfamiliarity, and the unexpected appeal to users’ curiosity, promoting repeat

engagement. The fourth characteristic of user engagement is control (Webster & Ho, 1997).
Webster & Ho argue that a user of electronic media must feel they are in control (as opposed
to the software, program, etc.) in order to become engaged. The fifth characteristic is
reputation, trust, and expectation as they are a necessary condition for user engagement. This
characteristic is especially important as consumers must feel comfortable that companies will
protect their information. In addition, customers are unable to engage if they feel the
information they are receiving is incorrect or misleading (Attfield et al., 2011). Finally, the
last characteristic of user engagement is motivation and interest (Jacques et al., 1995;
O’Brien & Toms, 2008). In order for someone to be engaged they need to have some sort of
motivation or interest in consuming the experience, otherwise engagement is unlikely. Figure
2 provides a summary of the characteristics of engagement as defined through the above
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researchers.
Figure 2 - Characteristics of Engagement (Author generated)
2.2.3 Pull Marketing and Engaging Content
In the past few decades, human-computer interaction studies have underlined the need
to move beyond usability and to understand and create more engaging experiences
(Hassenzahl & Tractinsky, 2006). The value of providing these types of experiences is well

documented. Sedley & Perks (2010) claimed that user engagement is both a strategic
imperative and a source of competitive advantage. Neff (2007) also specified that user
engagement is a primary driver of sales growth. Although the value of user engagement is
clear, a significant shift in marketing tactics had to occur for practitioners to take advantage
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of these benefits.
For many years, and to some degree still today, outbound marketing, a goods-dominant
logic that views the consumer as external in the value-creation process of the firm, was
commonplace (Lusch and Vargo, 2009). A goods-dominant logic focuses on tangible
resources and transactions. However, there is an inherent conflict with goods-dominant logic
as the firm is viewed as the active source of expertise and therefore create marketing
programmes to create products in a factory (Vargo & Lusch, 2004). Vargo and Lusch state
that with a goods-dominant logic the consumer is not considered as part of the value creation
process and is therefore an outsider (see Appendix C1). Combine this with the over-abundance
of mass marketing messages and consumers have begun filtering out these
advertisements. It is with the advent of Web 2.0 that a new model of marketing could be
established.
Inbound marketing, a service-dominant logic, focuses on co-creation of value with the
consumer (see Appendix C2). This can be done in various ways such as crowdsourcing for
new product development ideas, Kickstarters to raise funds (Kickstarter.com, 2014),
empowering brand ambassadors to help other consumers, etc. However, in order to change
from an outbound to inbound marketing programme practitioners must engage potential
consumers through pull marketing tactics. The paper advocates doing this by providing
engaging content that will give the consumer additional value.

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2.2.4 Engaged Users and the Co-Creation of Value
In order to successfully achieve metrics such as profitability, market share, and sales
volume that reflect seller needs, customer needs must first be met. However, it cannot be
classified as consumer engagement when a single purchase is made or even if satisfaction
occurs in the post-purchase evaluation phase. Indeed, consumer engagement can only occur
when a loyal customer attaches emotional significance to a brand, product, or company
(Sashi, 2012).
Sashi (2012) suggests that consumer engagement focuses on satisfying customers by
providing superior value than competitors to build trust and commitment in long-term
relationships. Moreover, Parent, Plannger, & Bal (2011) argue that, in light of Web 2.0 and
social media, willingness to participate is of great importance in creating those long-term
relationships with consumers via value co-creation. Indeed, much of the more recent
literature and research argues in favour of a more engaged consumer through inbound
marketing tactics.
2.3 YouTube
YouTube, a video-sharing service established in 2005 (YouTube.com, 2005) and
bought by Google in 2006 (News.bbc.co.uk, 2006) is the third most visited website in the
world. It has over 1 billion unique users monthly, over six billion hours of video are
consumed each month (YouTube.com, 2014), and handles 10% of all Internet traffic (Pike,
2012). Moreover, YouTube is the third biggest driver of traffic to websites, next to Facebook
and Stumbleupon (Cayer, 2012). YouTube is unique to other social media sites in its focus on
sharing user-generated videos. This thriving digital platform has grown exponentially and its
visitors use it for a variety of reasons such as information seeking, entertainment, co-viewing,
social interaction (Haridakis and Hanson, 2009), to create and share content, and even make a

living (Kim, 2012). YouTube is an ideal vehicle for this research because of its immense
popularity, offerings of engaging content, interactivity, and the ability to apply past research.
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2.3.1 Demographics of YouTube
YouTube provides a wealth of information for advertisers and brand managers thinking
about expanding their marketing efforts to this digital platform. In a research series titled
‘Gen V Research’ Google has released information about men age 18-34 and women age 25-
49 who use YouTube (Gen V Research Men 18-34, 2012; Gen V Research Women 25-49,
2012). The reports explain that generation v is a psychographic profile that cuts across
demographic groups. Furthermore, generation v are drastically changing the media landscape
through altered consumption patterns. Both reports claim that men 18-34 and women 25-49
are quickly adopting on-demand television as opposed to traditional television. Moreover,
both sets of consumers are increasingly seeking their content on-the-go via smart phones.
Finally, the report highlights that users often share content they find on YouTube throughout
various social networks and watch with family. Indeed, similarly to the work done on online
brand communities and user engagement, the media landscape is shifting and practitioners
must adapt not only what they say (and offer) to consumers, but where they say it.
2.3.2 Native Advertising on YouTube
In the case of digital content providers, such as Rooster Teeth, native advertising is an
ethical concern. Native advertising is marketing messages that are built into the design of
user content effectively blurring the line between what is and is not advertising (Lovell,
2014). The Federal Trade Commission (FTC) of the United States have recently convened
about native advertising and have no clear direction on how to police it (AdWeek, 2013).
Given the increased utilisation of native advertising and the blurred line between paid
endorsement and content the question must be asked if this affects consumers’ perception of
biasness by advertising.

The video game community on YouTube has recent experience with this type of
deceptive advertising. Microsoft asked popular YouTube video game channel, Machinima, to
promote the Xbox One. Machinima did not declare that they were receiving advertising
dollars when they released ‘reviews’ of Microsoft’s newest console. Users who based
purchasing decisions on the review were, in turn, deceived by the content. This incident drew
the attention of the FTC who has since opened an investigation (Peterson, 2014). Given the
increasing prevalence of this type of advertising, this paper also tests trust of Rooster Teeth as
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a third-party.
2.3.3 Rooster Teeth: the Professional User
Rooster Teeth Productions is a company based out of Austin, Texas, United States that
specialize in creating online videos (Rooster Teeth, 2014). The company was officially
founded in 2003 and is known for their award-winning, long-running video game web-series
Red vs Blue. This web series, the first of its kind, has voiced-over gameplay videos of the
popular Halo franchise. The company created a YouTube channel on July 11th, 2006 and has
since created other spin-off channels such as Let’s Play, The Know, GameFails, and AH
Community. Each channel, and the personalities that star on them, specialise in different
types of content. For example, the actual Rooster Teeth channel has a variety of content
spanning from gameplay to podcasts. Let’s Play, however, is solely focused on gameplay
videos and has different, reoccurring personalities. For the purposes of this paper, the
channels Rooster Teeth and Let’s Play will be the focus given their high subscriber base, cast
of expert users, and emphasis on video games.
According to SocialBlade.com (2014), Rooster Teeth is the 37th most subscribed
channel and the 12th most viewed channel in the United States. Let’s Play is the 208th most
subscribed channel and 360th most viewed in the United States. It is estimated that these two
channels make £432.5k - £3.5m yearly in advertising revenue on YouTube.

Rooster Teeth was selected as it meets all of the requirements that this study seeks to
better understand. They are an established community with various places where community
members can virtually congregate, they have been an established brand for nearly as long as
YouTube has been available, and they often feature content from a third-party. Furthermore,
video game play is an activity that requires deep engagement from users. The characteristics
displayed during play are remarkably similar to that of watching content of games being
played in that they trigger many of the same experiences for users. Rooster Teeth has
established themselves as professional users and as such a position of authority. For these
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reasons, Rooster Teeth and its community are ideal for study.
2.4 Literature Review Summary
This paper has reviewed the most recent literature on all relevant fields including online
brand communities, user engagement, and YouTube. As the literature illustrates, consumer
attention is indeed shifting in the face of the new media landscape. The research provided
thus far defines and highlights the value of these fields to the modern marketing practitioner,
but fails to explain if the being part of an online brand community and/or being engaged
actually predicts sales on the rapidly evolving platform of YouTube. YouTube is under
researched for testing the selected independent variables and their ability to predict sales. By
applying the work done on online brand communities and user engagement this paper will
build upon that limited knowledge.
3.0 Statement of Research and Hypotheses
The research question that this paper seeks to answer is:
Does Online brand community membership and/or user engagement predict purchasing
behaviour via YouTube and to what extent does a third-party affect trust?
The research problem will be answered through four hypotheses:

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Hypothesis One
H1: Being part of the Rooster Teeth online brand community predicts an increased likelihood
to purchase.
H0: Being part of the Rooster Teeth online brand community does not predict an increased
likelihood to purchase.
Hypothesis Two
H2: Being engaged by Rooster Teeth content predicts an increased likelihood to purchase.
H0: Being engaged by Rooster Teeth content does not predict an increased likelihood to
purchase.
Hypothesis Three
H3: Being part of the Rooster Teeth community and being engaged predicts are independent
variables in predicting likelihood to purchase.
H0: Being part of the Rooster Teeth community and being engaged are not independent
variables in predicting likelihood to purchase.
Hypothesis Four
H4a: Rooster Teeth’s influence predicts an increase in purchasing behaviour.
H0: Rooster Teeth’s influence does not predict an increase in purchasing behaviour.
H4b: Trusting Rooster Teeth predicts an increase in purchasing behaviour.
H0: Trusting Rooster Teeth does not predict an increase in purchasing behaviour.

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4.0 Methodology
This section will describe the research approach and justify the chosen methodology
used for the course of this study. It will provide the different steps of the research process
conducted in order to investigate the research problem.
4.1. Research Design
As evident in the literature review, both fields have created a wealth of knowledge.
These pioneering researchers, through their qualitative work, have established definitions and
methodologies for future researchers. As such, this paper implements a quantitative
methodology in an effort to build upon the theories that have already been crafted. More
specifically, many of the questions in the survey used in this work were made from other
researchers qualitative methods and compliment their findings. A quantitative approach
allows this paper to test these theories at scale and create an accurate reflection of the
population of interest.
This paper uses a cross-sectional research design. A cross-sectional research design is
the collection of data on relevant variables from a variety of cases at a single point in time.
This allows for the examination of relationships and detection of patterns of association
between variables which is ideal for the purposes of this study (Bryman and Bell, 2011). In
addition, this design choice was selected as it allowed the paper to collect a much more
massive scale than would have been possible if attempting a longitudinal design. Finally, a
longitudinal design was ruled out given overall time constraints.
4.3 Methodology Review
The fields of user engagement and online brand community are relatively new;
however, researchers are incorporating traditional methodologies in their work. As covered in
the literature review, researchers in both fields largely agree with the effectiveness of online

brand communities and user engagement, however, there is no universal methodology for
these evolving fields. This section will describe and evaluate methodologies utilised by the
research papers that provide a foundation for the methodologies chosen for the current work.
Self-reported measures (i.e. questionnaires, interviews, reports, etc.) are a prevalent
methodology chosen by researchers hoping to measure user engagement and belonging to an
online brand community. Researchers use self-reporting measures as it emphasises the
individuals’ subjective experiences with technologies (Lalmas, O'Brien and Yom-Tov, 2013).
Furthermore, self-report methods may be discrete, dimensional, and free response
(Lopatovska & Arapakis, 2011). Some of the advantages for selecting a self-reporting
measure include convenience to the research, participant anonymity, enable statistical
analysis and standardization, and function well in large-sample research studies (Fulmer &
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Frijters, 2009).
O’Brien & Toms (2008), in an attempt to better define the characteristics of user
engagement, utilised semi-structured interviews because it matched their exploratory research
design and allowed consumers to express their thoughts, behaviours, and feelings. In a later
research report, O’Brien (2010) went on to fully incorporate the use of a mass online survey
in order to better test the validity of the findings from the 2008 study. This evolution from
qualitative to quantitative methods by these researchers is mirrored by the present study.
4.4 Questionnaire Design
The questionnaire was developed following the constructs of the theoretical
frameworks developed by the fields of online brand communities and user engagement. To
ensure face and content validity, all questions were carefully modified from past work from
researchers such as Muniz & O’Guinn, O’Brien & Toms, and Attfield et al. The present study
consulted with previous quantitative work and adhered to the findings. Additionally, the
survey was constructed using identical or slightly adapted questions that previous researchers

had used. The design choice of maintaining consistency with previous work ensures validity
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as the paper seeks to build upon these particular findings.
Muniz & O’Guinn (2001) developed the characteristics of online brand communities.
The three characteristics are consciousness of kind, shared rituals and traditions, and moral
responsibility. A total of ten questions sought to measure these characteristics. Measuring
user engagement required the amalgamation of several works. This paper recognizes six
engagement characteristics – focused attention, endurability, novelty, control, trust, and
motivation. A total of 12 questions were asked to test these characteristics (See Table 1).
The dependent variable was comprised of four purchase related questions. Two of the
questions seek to determine actual purchase behaviour after content was viewed. The other
two questions inquire about past information searches as a result of watching a Rooster Teeth
video. The information search was included as it is the second stage of the consumer
purchase decision process and may lead to an increase in overall purchase intentions
(Appendix B) (De Valck et al., 2009). Between the two categories of purchase and
information search a distinction was made to establish prior knowledge of the game. Table 1
provides specific details as to what questions were asked and what characteristic they
measured.

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Table 1 - Questionnaire design
In order to evaluate the feasibility of the framework a pilot study was conducted. A
pilot study is a small trial of a larger work to ensure procedures and methods work as
intended (Walsh and Wigens, 2003). The questionnaire was distributed to ten people through
convenience sampling. This pilot study resulted in additional possible answers for Q9 and
clarification on Q8.
In total, the questionnaire was made up of 18 close-ended questions and four
demographic questions. For example, in order to test for focused attention, a characteristic of

measuring user engagement (Webster & Ho, 1997; O’Brien, 2010), the question “When a
new video is posted, how likely are you to watch the video in its entirety?” utilised a 5-point
Likert scale ranging from “Very unlikely” to “Very likely”. This chosen design allowed for
quantifiable results which could be compared with other questions seeking to measure
different characteristics. The questionnaire in its entirety can be found in Appendix D.
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4.5 Data Collection
The survey was created through the online research company Qualtrics. The survey was
published and distributed beginning on June 27th, 2014 and was closed on July 9th, 2014. The
link to the survey was distributed to websites with the intention to attract those who are
familiar with Rooster Teeth. As such, the link was posted to the Rooster Teeth Facebook
page, Twitter account, Reddit page, and three Rooster Teeth YouTube videos.
In total, 1,591 responses were collected resulting in a good power of this study.
Furthermore, only 11 respondents had missing data. Due to the very low percentage of people
with missing data, list-wise deletion was used. The responses were collected from people
who visit Rooster Teeth sponsored webpages and therefore the data collected may not be
representative of the entire YouTube population. However, the large size of the probabilistic
sample chosen aims to establish the representativeness of the sample for the population
understudies.
4.6 Ethics
This research project followed all standards set forth by the College Research Ethics
Committee. Furthermore, no names or identifying attributes were collected during this
process. The data collected will only be used for the purposes of this research paper and will
not be released or reused for any other purposes. Additionally, respondents under the age of

16 were asked not to complete the questionnaire. Copies of the information sheet, consent
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form, and ethics approval are listed in Appendix E, F, and G, respectively.
4.7 Statistical Analysis
Descriptive statistics were analysed first as a way to see if any questions had
significantly less responses. Next, continuous variables were examined using the mean and
standard deviation while categorical variables were examined using frequencies.
Subsequently, a series of simple and multiple logistic regressions were performed to examine
which of the independent variables were significant predictors of any of the four dependent
variables.
Each individual characteristic was tested through a binomial logistical regression. This
test was best suited to test for association as “binomial logistical regression attempts to
predict the probability that an observation falls into one of two categories of a dichotomous
dependent variable based on one or more independent variables” (Statistics.laerd.com, 2014).
Given the fact that all four dependent variables were dichotomous this test was an ideal
choice to predict probability.
As an example of this process, the control characteristic was tested for association with
each dependent purchase variable. As a result, a simple binary logistical regression was run
four times. Ultimately, in an effort to answer the hypotheses, each characteristic of online
brand community or user engagement were calculated together (adjusting all questions to the
correct scale) and a multiple logistical regression was run.
In addition to the regressions run, the odds ratio and Nagelkerke R2 were also noted for
each test run. An odds ratio quantifies the strength of association between the independent
and dichotomous dependent variables (Bland & Altman, 2000). This information was vital in
that the strength of association could be compared to other tests run allowing for a more in-

depth understanding of how important each characteristic is. In addition, the Nagelkerke R2
results were also included as they prove a goodness-of-fit for the test run. All statistical
Country of Residence
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analyses were processed through SPSS.
5.0 Results
5.1 Respondent’s Profile
The sample consisted of 1,223 males (82.2%) and 265 females (17.8%). Respondents
presented with a mean age of 19.84 years. Indeed, 47% of respondents claimed to be 21 years
old or younger (see Figure 3)
Figure 3 – Age of Respondents Figure 4 - Country of Residence
Age of Respondents
18%
29%
11%
8%
20%
14%
16 - 18
19 - 21
22 - 24
25 - 27
28 - 30
31 +
>1%
>1%
2%
Questionnaire respondents were largely from English speaking countries such as the
United States, United Kingdom, Australia, and Canada. A total of 97% of respondents reside
in these four countries (See Fig 4). These results are unsurprising given that Rooster Teeth
content is unavailable in languages other than English.
When considering occupation status, 76% of respondents stated to have a main
occupation status of “student” while the remaining 24% stated that they were employed (See
Fig. 5). Moreover, no respondents claimed to be retired.
62%
18%
1%
11%
6%
USA
UK
France
Germany
Spain
Australia
Canada
Other

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Occupation
4% 0% 2%
76%
18%
5.2 Testing Hypothesis 1: Brand Communities on Purchase
Hypothesis 1 states;
H1: Being part of the Rooster Teeth community predicts an increase in the likelihood to
purchase.
Hypothesis 1 aims to explore the relationship between the characteristics of online
brand community membership to likelihood of purchase. As described in the methodology
section, questions were drawn and adapted from Muniz & O’Guinn’s (2001) work. In
keeping with the characteristics that define online brand community membership, the
following sections test for each characteristic through a simple binary logistical regression.
5.2.1 Online Brand Community Characteristic: Consciousness of Kind
Consciousness of kind was divided into two separate factors – Self-reported
membership and interactivity with other online brand community members. A binary logistic
regression was run to determine if consciousness of kind is a significant predictor of purchase
intentions and if probability could be determined between these two factors and purchase
intentions (Table 2).
Student
Employed:
Part time
Employed:
Full Time
Retired
Figure 5 - Occupation

As evident by the test, there is a statistical significance (p < 0.05) between self-reported
membership identification and all aspects of purchase intentions. However, statistical
significance could not be established between respondents desire to converse with other
viewers as a predictor of purchase intentions. Furthermore, those who identify as being part
of the Rooster Teeth online brand community had an odds ratio that ranged from 2.3 to 2.5
for the different dependent variables. This means users are that much more likely to display
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purchasing behaviour than those who do not identify as part of the community.
5.2.2 Online Brand Community Characteristic: Shared Rituals & Traditions
Shared rituals and traditions are a critical aspect of maintaining consistent meaning of
an online brand community. The questionnaire measured this by determining if the users visit
various brand sites/pages (i.e. Rooster Teeth website, Twitter, Facebook) on a monthly basis
in addition to participation in community events. A series of binary logistical regressions

were run to determine if these factors were significant predictors of purchase intentions
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(Table 3).
The results show that monthly visitation to a Rooster Teeth sponsored page is a
statistically significant predictor of increased likelihood to purchase. Additionally,
participation in events or content creation was a statistically significant predictor of purchase
intentions at the p <0.001 level. The odds ratio reveals that monthly visitation to these various
sites is associated with a range of 1.419 to 1.551 times increased likelihood of purchasing
behaviour. Users who participate in community events and create content, show even higher
odds ratios for purchase intentions (ranging from 1.664 to 3.000) meaning an even further
increased likelihood of purchasing intentions.

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5.2.3 Online Brand Community Characteristic: Moral Responsibility
The last characteristic of online brand community is moral responsibility. Moral
responsibility was tested by asking if the respondent comments to assist other users. A binary
logistical regression was run to determine if a prediction of probability existed between moral
responsibility and purchase intentions (Table 4).
The regression reveals that only two of the four dependent variables tested were
statistically significant as predictors of purchase behaviour. It can be predicted that those who
comment to help others (display moral responsibility behaviour) are more likely to purchase
games they were unaware of and to look up information about a game they were previously
aware of after watching the game featured in a Rooster Teeth video. The odds ratio predicts
that those who display moral responsibility through commenting are 1.365 times more likely
to display the significant purchasing behaviours listed above.
5.2.4 Hypothesis 1 – Conclusion
In order to answer the hypothesis it is necessary to combine all characteristics. A
multiple logistical regression was run to determine if the combined characteristics were a
statistically significant predictor of purchasing behaviour (Table 5).

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The regression reveals that the combination of all online brand community
characteristics was statistically significant at the p<0.001 level as predictors of purchase
behaviour. Users who display characteristics of being part of an online brand community are
more likely to initiate purchase behaviour than those who do not show these characteristics.
The odds ratio elaborates on these findings predicting an increased likelihood to purchase
between 1.316 and 1.387. Given these findings this paper rejects the null hypothesis and
accepts a positive correlation between online brand community characteristics and purchasing
behaviour.
5.3 Testing Hypothesis 2: Engagement on Purchase
H2: Being continuously engaged by Rooster Teeth predicts an increase in purchasing
behaviour.
Hypothesis 2 aims to explore the relationship between the characteristics of user
engagement and likelihood of purchase. As described in the methodology section, the
characteristics were drawn from an amalgamation of researchers. Similarly to the above
section, user engagement utilises multiple binary logistical regressions to test for association
between characteristics and purchasing behaviour.

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5.3.1 Engagement Characteristic: Focused Attention
The first characteristic of user engagement is focused attention. This characteristic was
tested for by asking respondents to identify how often they watch, if they watch the video in
its entirety, and how intently they watch. A binary logistical regression was run to determine
if a prediction of probability existed between focused attention and purchase intentions
(Table 6).
The regression reveals that one out of four tests proved to be statistically significant.
Users who report that they watch longer videos uninterrupted are more likely to have
purchased a game they were not knowledgeable about prior to watching it on Rooster Teeth.
The odds ratio predicts those with higher levels of focused attention are 1.096 times more
likely to purchase games they did not know about prior to the video than those with lower
levels of focused attention.
5.3.2 Engagement Characteristic: Endurability
Endurability was tested by asking respondents to declare how long they had been
subscribers to Rooster Teeth and how many videos they watch a week. A simple binary

logistical regression was run to determine if a prediction of probability existed between
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endurability and purchase intentions (Table 7).
Endurability proved to be a statistically significant predictor of all purchase behaviours.
More specifically, there is a significant correlation at the p<0.001 level between longer length
memberships and history of purchasing games following a Rooster Teeth video regardless of
prior knowledge to the product. In addition, tenured subscribers are more likely to look up
information about a game after watching it featured in a Rooster Teeth video. The odds-ratio
test predicts that longer tenured members are between 1.022 and 1.441 times more likely to
have purchase behaviour than those who have been subscribed for less time.
5.3.3 Engagement Characteristic: Novelty
Novelty was tested by asking respondents to rate on 5-point Likert scale how unique
they felt the content produced by Rooster Teeth was. A simple binary logistical regression
was run to determine if a prediction of probability existed between novelty and purchase

The regression revealed the existence of a statistically significant positive correlation
between novelty and purchase behaviour in three out of the four dependent variables. Users
who found Rooster Teeth content more unique were predicted to be more likely to purchase
regardless of knowledge of the product before the video, and to look up information on a
game they were previously aware of. The odds ratio reveals that for all significant findings
users were between 1.158 and 1.362 times more likely to initiate purchasing behaviour than
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those who found Rooster Teeth content less unique.
5.3.4 Engagement Characteristic: Control
Control is a characteristic of engagement that argues a user must feel they are in control
rather than the software or webpage (Webster & Ho, 1997). In other words, the user must feel
ease of use and ability to do exactly as they intend. This characteristic is different than the
others in that the platform, YouTube, is the subject of the test. A binary logistical regression
was run to determine if a prediction of probability existed between control and purchase

The regression revealed the existence of a statistically significant positive correlation
between the control characteristic and all four dependent variables. The odds ratio predicted
that those who claimed the highest levels of control were between 1.324 and 1.601 times
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more likely to have purchasing behaviour.
5.3.5 Engagement Characteristic: Trust
Trust was tested through a series of 5-point Likert scales. The three questions asked
trust, biasness, and affected perception. A binary logistical regression was run to determine if
there was a predictable correlation between user trust and purchase behaviour (Table 10).

Trust is significant as a predictor of all purchase behaviour variables at the p <0.001
level. Simply put, users who reported hirer levels of trust in Rooster Teeth were more likely
to have purchasing behaviour. The odds ratio elaborates on these findings by predicting that
users with high trust are between 1.225 and 1.372 times more likely to have purchasing
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behaviour than those with lower trust.
5.3.6 Engagement Characteristic: Motivation
The motivation for a user to engage in content is unique and much more qualitative by
nature. This characteristic was tested by providing options on the type of content users watch.
However, 61.7% of respondents reported to watch all Rooster Teeth content equally.
Although all types of content were tested, this answer proved to be the only one with any
significance. Therefore, this paper has opted to only include this option. Complete data was
collected on this answer for 980 people. A simple binary logistical regression was run to
predict a relationship between motivation and purchase behaviour (Table 11).

The motivation characteristic proved to be a statistically significant predictor of
purchase behaviour. The odds ratio predicted an increased likelihood of purchase for those
who watch all content equally between 2.077 and 4.240. These results mean that those who
watch all content equally are much more likely to have purchasing behaviour than those who
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watch select content.
5.3.7 Engagement: Conclusion
In order to answer hypothesis 2 this paper combined all engagement characteristics and
adjusted for scale. Therefore a multiple logistical regression was run (Table 12).

The results reveal that user engagement is significant in predicting positive purchasing
behaviour. Users who display characteristics of engagement are more likely to initiate
purchase behaviour than those who do not show these characteristics. The odds ratio reveals
that engaged users are between 1.096 and 1.116 times more likely to initiate purchase
behaviour than those who are not engaged. Given these findings this paper rejects the null
hypothesis and accepts a positive correlation between user engagement characteristics and
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purchasing behaviour.
5.4 Testing Hypothesis 3: Online Brand Community & Engagement on Purchase
H3: Being part of the Rooster Teeth community AND being regularly engaged increases
likelihood to purchase.
This paper sought to determine if online brand community membership and user
engagement predicted purchasing behaviour. With these two results determined, this paper
now seeks to answer if these marketing tactics are independent of one another. As such, a
multiple logistic regression was run to compare these two states (Table 13).

Both online brand community membership and user engagement are significant at the
p<0.001 level in predicting positive purchase behaviour for all dependent variables. When
controlling for one another they remain statistically significant. Given that all tests proved to
be statistically significant this paper will now turn to the odds ratio for more detail. The
largest difference between any two odds ratios was under the purchased but unaware of game
category with a difference of .211. In conclusion, both states are indeed significant and
therefore this paper rejects the null hypothesis and accepts that both online brand community
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membership and user engagement are predictors of positive purchasing behaviour
independently of one another.
5.5 Testing Hypothesis 4: Influence and Trust
H4a: Rooster Teeth’s influence predicts an increase in purchasing behaviour.
H4b: Trusting Rooster Teeth predicts an increase in purchasing behaviour.

The final hypotheses were created to determine if influence and/or trust could predict
positive purchasing behaviour. These hypotheses were created in an effort to address the fact
the Rooster Teeth is a third party with regards to video games, therefore, they are in a unique
position. A binary logistical regression was run to determine if a prediction of probability
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existed between influence / trust and purchase intentions (Table 14).
Influence and trust are both significant as predictors of all purchase behaviour variables
at the p <0.001 level. With regard to influence, the highest odds ratios related to actual
purchasing and ranged from 2.122 to 2.144. Trust, however, had the highest odd ratios
relating to information search and ranged from 1.323 to 1.372. Therefore, this paper rejects
both null hypotheses and accepts that trust and influence are predictors of positive purchasing
behaviour.
6.0 Discussion
Prior literature has not attempted to determine the value of online brand community and
user engagement from a deconstructed theoretical standpoint. By testing individual
characteristics as opposed to online brand community and user engagement concepts as

wholes this research gives much greater depth into the value of each characteristic. This, in
and of itself, contributes to the literature greatly. Moreover, this paper seeks to expand upon
the literature by applying these theories to Rooster Teeth. Rooster Teeth delivers its brand of
content (i.e. personalities, meaning, expert use) through video games as a third party. The fact
that they are more of a branded channel than a company who features their own products
drastically changes the dynamics of interaction between firm and consumer. The findings
show that, as a whole, both online brand community membership and user engagement
predict positive purchasing behaviour. However, it is in keeping with the theme of critical
analysis of deconstructed concepts which allow this paper to offer increased depth. The
following discussion highlights the most significant findings and how they conform to or
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counter current literature.
6.1 Online Brand Communities
Self-reported membership, a question testing for consciousness of kind, proved to be
significant in predicting positive purchase behaviour. The findings conform to researchers
such as Okleshen & Grossbart (1998) who state that consumers who report being part of an
online brand community are more likely to be influenced on purchasing decisions.
Furthermore, self-reported membership was the greatest predictor of purchasing behaviour
and therefore adds weight to Muniz & O’Guinn’s argument that consciousness of kind is the
most significant characteristic of online brand communities. The present findings contribute
to the literature through confirming consistency across this new tested platform.
When testing for shared rituals and traditions all results proved to be significant
predictors of positive purchase behaviour. De Valck et al. (2009) found that repetition of
visits and length of stay ultimately extends community influence. These findings were
mirrored in that monthly visitation to a Rooster Teeth sponsored webpage is a significant

predictor of purchasing behaviour. Furthermore, the odds ratio reveals that, when controlling
for scale, those who participate in events or create Rooster Teeth content are even more likely
to have positive purchase behaviour than passive monthly website visits. These finding also
conform to researchers such as Parent, Plangger and Bal (2011) who provide a model of the
levels of participation which state that “providing” content is three stages higher than simply
“viewing” which is only stage one (See Appendix H). These findings contribute to the
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literature in that they conform to the evolution from viewing to content creation as
membership characteristics. In addition, given that YouTube is a platform specifically
designed for user-generated content the ease with which community members share content
is much greater. As a result, the community can have a greater overall impact and
involvement than studied companies who seek user generated content.
Moral responsibility, the final tested characteristic of online brand community, had two
significant dependent variables – purchasing of video games the user was previously unaware
of and an information search about a game the user was previously aware of. A frequency test
revealed that only 760 respondents ever comment which is less than half of total respondents.
In establishing the three markers of online brand community, Muniz & O’Guinn stated that
the moral responsibility characteristic is the motivation users must have to help others and is
necessary to long term community health. If communication via the comment section is an
indicator of moral responsibility, these findings contradict their work. These results are
mostly inconclusive and do not adhere to the findings of Bickart & Schindler (2001),
Constant, Sproull & Kiesler (1996), and Kozinets (2002). These researchers all found that
interaction between community members result in friendships, increased trust in the brand,
and ultimately influence in purchasing decisions. In that way, based on commenting alone,
friendships could not be established as communication is limited. Therefore, further value
cannot be established to the user which diminishes the ability for increased trust in the brand.

These results contribute to the literature in that communication among community members
is not always a necessity and is highly dependent on the platform with which the community
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thrives.
By reviewing the work of other researchers the subjects have always been brand
communities who form around a product or company. Rooster Teeth, however, is much more
similar to a branded channel in that it features content that is not owned by the company. As a
third-party Rooster Teeth benefits from an impartial stance. The difference can best be
described through Friestad & Wright’s (1994) Persuasion Knowledge Model (Appendix I).
This model demonstrates the ability of a user to resist persuasion tactics through their past
experience with advertising tactics. However, as Rooster Teeth is a third party the critical
analyse and persuasion defences for users may be lowered as they are seen as nothing more
than opinion leaders.
In conclusion, the results allow this paper to reject the null and accept that online brand
community membership does predict positive purchasing behaviour. The findings largely
conform to the literature completed on different fields. However, what was found that
contradicts previous findings is the importance of community members conversing. As a
result, this work contributes to the literature in that the Rooster Teeth community has
different characteristics than other types of communities and as such previous work is not all
encompassing.
6.2 User Engagement
All engagement characteristics tested, with the exception of trust and motivation,
predicted increased significance for actual purchase, regardless of previous knowledge of the
featured game, over simple information search. Aside from the control characteristic which
tested the ease of use for the YouTube platform, endurability had the highest odds ratios in

the purchase category of all engagement characteristics tested. This could be explained by the
idea that continuous engagement over time leads to greater trust which, in turn, leads to
simply purchasing rather than completing an information search; as is the case with fast-moving
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consumer goods.
Of all characteristics tested for engagement, motivation (tested through the type of
content users watch) proved to be the most significant in predicting positive purchase
behaviour across all dependent variables. As discussed in the finding, the majority of people
surveyed reported watching all content equally. This finding suggests that the focus of the
content is not the games, but rather, Rooster Teeth itself. These findings conform to Sashi
(2012) in that consumer engagement can only occur when a user attaches emotional
significance to a brand, product or company. Motivation as a characteristic tested so highly
because those who watch all content equally do so as they identify with the Rooster Teeth
brand and are therefore more likely to positively accept meaning that is transferred to a game.
Sashi (2012) also suggests that consumer engagement focuses on satisfying customers
by providing superior value than competitors. Competitors to Rooster Teeth are other
YouTube channels vying for the same audiences’ attention. With the understanding that
many different channels play the same games the vital question becomes, ‘how does Rooster
Teeth provide superior value?’ The answer is that Rooster Teeth provides value (and
engagement) through their brand meaning and personality. The distinction is hugely
important as it gives insight into why consumers would chose to be engaged on one channel
versus another. Furthermore, this gives insight into the peculiar results of increased likelihood
to purchase over an information search. In summary, what this means for purchasing
decisions is that the values Rooster Teeth infuses into the game are arguably more important
to those engaged than what the product itself.

In conclusion, the findings and principles of user engagement are largely still applicable
to Rooster Teeth. The engaged user is brand loyal and is much more likely to purchase based
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on the recommendation of the company.
6.3 Bridging Online Brand Community and User Engagement
Online brand community and user engagement both tested significant as predictors of
positive purchase behaviour. However, it is through the multiple logistical regressions that
revealed that neither state is significantly independent of one another for any of the purchase
categories. The odds ratio in each category confirms that those who have online brand
community characteristics are more likely to purchase than those who are engaged. The
conclusion to be drawn from these findings is that user engagement is a step in becoming part
of an online brand community.
As a result of this researchers and practitioners should not think of user engagement as
something separate from online brand community membership, but rather, a step towards it.
In a recent article by Brodie et al. (2013), they sought to establish a formal connection
between user engagement and online brand community. They state the reasoning for their
qualtative study: “Despite the extensive use of the term “engagement” in the context of brand
communities, the theoretical meaning and foundations underlying this term remain
underexplored in the literature to-date“(pg.1). As a result, they developed the following
model which summarizes the consumer process from user engagement to community
membership (See Figure 6).

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Figure 6 - Consumer Engagement Process in Virtual Brand Communities
(Brodie et al., 2013)
The findings of hypothesis 3 conform to this model. User engagement as it predicts
positive purchasing behaviour is less likely than those who report online brand community
characteristics. This contributes to the literature by quantitatively validating Brodie et al.
findings.
6.4 Rooster Teeth: Trust and Influence
A unique aspect of this work is applying established theory to an untested medium. The
major differentiating factor between Rooster Teeth and other tested firms is that Rooster
Teeth works more as a branded channel by featuring video game content they did not
produce. As such, the content meaning is no longer dictated by the video game publisher and
is given new meaning by Rooster Teeth. As a third party, consumers may believe Rooster
Teeth is impartial and be more likely to trust their opinion when considering purchase. This
drastically changes the scope of promotion and can positively or negatively affect a video
game.

Parent, Plangger, and Bal (2011) offer the 6C Model of Social Media Engagement
which seeks to explain the process of consumer engagement. In this model, the company
pushes out content to a community which ultimately results in adaptation and the production
of user generated content. For the purposes of this work, this model is effective for explaining
the transfer of meaning from firm to consumer. Past research has focused on the perspective
of the company and how meaning can be altered. However, this paper argues that Rooster
Teeth is actually part of the community and is an opinion leader. Figure 7 adapts the 6C
model to the current study. It is from this model that Rooster Teeth’s position as a third-party
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becomes clearer.
Figure 7 - Transfer of Meaning Process
Adapted from Parent, Plangger, and Bal, 2011
The results from hypothesis 4 reveal that trust and influence are statistically significant
at the p < 0.001 level in predicting positive purchasing behaviour. Although marketers are
increasingly utilising the ethically controversial method of native advertising (see section
2.2.3.), these results show this is not a concern for consumers of Rooster Teeth. However, as
native advertising becomes more prevalent third party branded channels such as Rooster
Teeth may become under more scrutiny.

52 | P a g e
7.0 Conclusion
7.1 Managerial Implication
Rooster Teeth is extraordinary at supporting community and encouraging participation.
Their efforts include maintaining an official community channel, inclusion of user-generated
content such as game modes, game suggestions, art, and video. In addition they actively
respond to community across a variety social media channels. These reasons have helped
establish a highly loyal online brand community. However, the results indicate Rooster Teeth
must do more to get users to comment more often. Therefore, the company can encourage
users who are doing a simple information search to comment about their experience with the
game and connect to other YouTube users. As stated by Walther (1995), this is the natural
process for someone to become part of an online brand community. Rooster Teeth should
encourage participation to comment by asking direct questions of the audience such as,
“What do you think of this game? Leave a comment below”.
Additionally, the concept of share of wallet (Keiningham et al., 2011), the idea of
getting a percentage of customers reoccurring expenses, can be adapted to Rooster Teeth and
engagement. As such, this paper introduces the concept of share of watch. Share of watch
will be defined as a firm continuously providing engaging entertainment as to ensure
reoccurring time spending. The meaning works on two levels as watch can refer to amount of
time or amount of video content consumed. In order for Rooster Teeth to gain more share of
watch this paper recommends taking advantage of the high levels of trust users reported.
They can do this by encouraging purchase of particular games and then featuring those games
soon after that recommendation. By following this process Rooster Teeth will assist in
creating positive post-purchase evaluation, reinforce their brand image as a professional user,
and increase their share of watch. Moreover, this process is ultimately cyclical in that users

gain additional value from this process which strengthens the use of Rooster Teeth as a
53 | P a g e
reference group (See Figure 8).
Figure 8 - Process to Increase Share of Watch
Increase user
reliance on Rooster
Teeth as reference
group
Rooster Teeth would also do well to encourage more participation in community events
as those who reported doing so were much more likely to purchase games than other users.
Aside from simply offering more events for community members to be involved in, Rooster
Teeth should increase the likelihood of members playing in the event by having brand
personalities play as well. Additionally, brand personalities should encourage those playing
in those events to attend their annual Rooster Teeth convention. By connecting with
consumers on multiple levels (in game, YouTube, person-to-person) Rooster Teeth can create
synergy which increases overall value of the brand to consumers. This concept is tried and
proven successful by companies like Disney who successfully reinforce their brand through a
variety of mediums (Olson, 2004).
Video game
recommendation
New videos
featuring
recommended
game
Creates positive
post-purchase
evaluation
Reinforce brand
image as
professional user

Finally, the results highlight how important trust is for both online brand community
and user engagement. Furthermore, Rooster Teeth find themselves in a highly influential
position amidst a large following. Researchers Katz and Lazarsfeld (1955) stated that
advertisers have constantly targeted opinion leaders rather than mass marketing. Given the
changing advertising environment (i.e. the increase attention on native advertising) it is likely
that Rooster Teeth will be approached to promote a video game, if they already have not
been. The findings clearly indicate that trust is a major factor for both online brand
community participation and user engagement. The findings combined with past research
urge Rooster Teeth not to participate in native advertising as it may jeopardize established
54 | P a g e
trust.
In conclusion, Rooster Teeth has amassed a massive following and are highly
influential to their audience. This paper recommends that Rooster Teeth promote more use of
the comment section through a direct call to action, increase share of watch by following a
five-step model, encourage cross-platform participation through synergy tactics, and to avoid
participation in native advertising as it jeopardizes brand trust which decreases characteristics
of both online brand community and user engagement.
7.2 Limitations and Future Research
Self-reported measures have challenges and disadvantages as compared to other
research approaches. Kobayashi & Boase (2012) reveal that, of the greatest concern to
researchers taking this approach are issues of communication and misunderstanding. Issues
such as wording, rapport between interviewer and interviewee, and participants’ varied
definition of categories can lead to issues of reliability and validity. Although the survey did
have a pilot test, some questions could have been interpreted in a way not intended by the
researcher.

This study used a cross-sectional design in that it only tested respondents at one point in
time. This design is can generate substantially biased estimates of longitudinal parameters
(Maxwell, Cole and Mitchell, 2011). Also, Wright and Grant (2010) cited that cross-sectional
designs generally do not enable casual direction to be established. This form of research
55 | P a g e
design caters to weaker internal validity.
The external validity of the data also needs to be considered when trying to generalise
the results to other populations. Section 5.1, the respondent’s profile, reveals that the average
age of respondents was 19.84 years of age. Additionally, the respondents were predominately
male (82.2%). It is likely that if the group tested were more representative of a general
population the results may have been different. In addition, the external validity may be
questionable in that those who took the test were likely already part of the Rooster Teeth
online brand community. A frequency test supports this limitation as self-reported
membership to the Rooster Teeth online brand community revealed that 1,071 respondents
(67.4%) claimed to be members.
This study took a case study approach and focused on Rooster Teeth. According to
YouTube.com (2014), Rooster Teeth has 7,735,698 subscribers as of August 18th, 2014.
Given that this study was a pioneering one it was necessary to use a case with a large
community fan base. However, it is recommended that future research test the independent
variables ability to predict positive purchase decisions on smaller YouTube brand
communities.
On a similar note, Rooster Teeth produces video game content which mirrors the
demographics of YouTube users. A future study could apply the methodology presented in
this study to a different channel that features content aimed at an older, more diverse
demographic. This would allow for more generalizable results that may be applicable to other
industries.

Finally, a potential future study would be to compare these findings with YouTube
channels that are owned by the video game producers. For example, by applying this
methodology to the Sony PlayStation YouTube Channel, 237th most subscribed channel on
YouTube (Socialblade.com, 2014a), the results can be compared. The issue of trust is of
significance to both online brand community and user engagement and a comparative
analysis of the results between a third party and a first party would add to this literature.
56 | P a g e

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8.0 Appendices
Appendix A: Global Ad Spend Trends, 2014
(Warc.com, 2014)

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Appendix B: Consumer Decision Process
Need
Recognition
Author Generated as adapted from Engal, Kollar, and Blackwell, 1968
Post-purchase
Evaluation
Information
Search
Evaluation of
Alternatives
Purchase
Decision

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Appendix C1: Outbound Marketing
Lusch & Vargo, 2009
Appendix C2: Inbound Marketing
Lusch & Vargo, 2009

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Appendix D: Distributed Questionnaire

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Appendix E: Information Sheet for Participants

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Appendix F: Consent Form for Participation in Online Survey

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Appendix G: Ethics Approval from King’s College London

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Appendix H: Levels of Participation
Parent, Plangger and Bal, 2011

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Appendix I: Persuasion Knowledge Model
Friestad and Wright, 1994

Appendix J1: SPSS Output Example: Community Membership x Purchase: did not
know about before
Appendix J2: SPSS Output Example: Control x Info: did not know about before
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