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King’s College 
University of London 
The Effectiveness of Online Brand Communities 
and User Engagement on Influencing Pu...
i. Abstract 
Social media has forever changed how marketers communicate with an audience. Gone 
are the days of one-way co...
2 | P a g e 
ii. Table of Contents 
Abstract. ...............................................................................
5.0 Results .................................................................................................................
4 | P a g e 
iii. List of Tables 
Table 1 – Questionnaire Design.............................................................
5 | P a g e 
iv. List of Figures 
Figure 1 – Online Brand Community Member Characteristics ..................................
6 | P a g e 
1.0 Introduction 
In the past few years, marketing practitioners all over the world have begun fixating 
them...
Given that YouTube is largely under-researched, this paper will also seek to better understand 
7 | P a g e 
Rooster Teeth...
this research aimed to provide detailed insight of these states by breaking them down to the 
8 | P a g e 
characteristics...
9 | P a g e 
2.0 Literature Review 
The purpose of this section is to provide background information and a foundation for ...
10 | P a g e 
geographical location. Society, however, is described as much more mechanical, 
individualistic, and rationa...
setting in which the members’ interaction is primarily Internet-mediated (Fuller, Jawecki & 
Muhlbacher, 2007). Of signifi...
The next marker, rituals and traditions, exist to perpetuate the shared history, culture, 
and meaning of the online brand...
13 | P a g e 
2.1.4 Brand Communities and the Individual 
Membership to an online brand community can have many effects on...
Furthermore, a third of consumers (33%) cited Amazon.com as a source of information when 
seeking to purchase a new produc...
Advertising research theorised that incidental exposure to marketing messages is not 
enough for consumers to recall infor...
16 | P a g e 
2.2.1 Defining User Engagement 
User engagement as a term has evolved over the past few decades. In 1991, La...
engagement. The fourth characteristic of user engagement is control (Webster & Ho, 1997). 
Webster & Ho argue that a user ...
documented. Sedley & Perks (2010) claimed that user engagement is both a strategic 
imperative and a source of competitive...
19 | P a g e 
2.2.4 Engaged Users and the Co-Creation of Value 
In order to successfully achieve metrics such as profitabi...
living (Kim, 2012). YouTube is an ideal vehicle for this research because of its immense 
popularity, offerings of engagin...
The video game community on YouTube has recent experience with this type of 
deceptive advertising. Microsoft asked popula...
Rooster Teeth was selected as it meets all of the requirements that this study seeks to 
better understand. They are an es...
23 | P a g e 
Hypothesis One 
H1: Being part of the Rooster Teeth online brand community predicts an increased likelihood ...
24 | P a g e 
4.0 Methodology 
This section will describe the research approach and justify the chosen methodology 
used f...
brand communities and user engagement, however, there is no universal methodology for 
these evolving fields. This section...
had used. The design choice of maintaining consistency with previous work ensures validity 
26 | P a g e 
as the paper see...
27 | P a g e 
Table 1 - Questionnaire design 
In order to evaluate the feasibility of the framework a pilot study was cond...
measuring user engagement (Webster & Ho, 1997; O’Brien, 2010), the question “When a 
new video is posted, how likely are y...
16 were asked not to complete the questionnaire. Copies of the information sheet, consent 
29 | P a g e 
form, and ethics ...
depth understanding of how important each characteristic is. In addition, the Nagelkerke R2 
results were also included as...
31 | P a g e 
Occupation 
4% 0% 2% 
76% 
18% 
5.2 Testing Hypothesis 1: Brand Communities on Purchase 
Hypothesis 1 states...
As evident by the test, there is a statistical significance (p < 0.05) between self-reported 
membership identification an...
were run to determine if these factors were significant predictors of purchase intentions 
33 | P a g e 
(Table 3). 
The r...
34 | P a g e 
5.2.3 Online Brand Community Characteristic: Moral Responsibility 
The last characteristic of online brand c...
35 | P a g e 
The regression reveals that the combination of all online brand community 
characteristics was statistically...
36 | P a g e 
5.3.1 Engagement Characteristic: Focused Attention 
The first characteristic of user engagement is focused a...
logistical regression was run to determine if a prediction of probability existed between 
37 | P a g e 
endurability and ...
The regression revealed the existence of a statistically significant positive correlation 
between novelty and purchase be...
The regression revealed the existence of a statistically significant positive correlation 
between the control characteris...
Trust is significant as a predictor of all purchase behaviour variables at the p <0.001 
level. Simply put, users who repo...
The motivation characteristic proved to be a statistically significant predictor of 
purchase behaviour. The odds ratio pr...
The results reveal that user engagement is significant in predicting positive purchasing 
behaviour. Users who display cha...
Both online brand community membership and user engagement are significant at the 
p<0.001 level in predicting positive pu...
The final hypotheses were created to determine if influence and/or trust could predict 
positive purchasing behaviour. The...
wholes this research gives much greater depth into the value of each characteristic. This, in 
and of itself, contributes ...
predictor of purchasing behaviour. Furthermore, the odds ratio reveals that, when controlling 
for scale, those who partic...
These results contribute to the literature in that communication among community members 
is not always a necessity and is...
the purchase category of all engagement characteristics tested. This could be explained by the 
idea that continuous engag...
In conclusion, the findings and principles of user engagement are largely still applicable 
to Rooster Teeth. The engaged ...
50 | P a g e 
Figure 6 - Consumer Engagement Process in Virtual Brand Communities 
(Brodie et al., 2013) 
The findings of ...
Parent, Plangger, and Bal (2011) offer the 6C Model of Social Media Engagement 
which seeks to explain the process of cons...
52 | P a g e 
7.0 Conclusion 
7.1 Managerial Implication 
Rooster Teeth is extraordinary at supporting community and encou...
gain additional value from this process which strengthens the use of Rooster Teeth as a 
53 | P a g e 
reference group (Se...
Finally, the results highlight how important trust is for both online brand community 
and user engagement. Furthermore, R...
This study used a cross-sectional design in that it only tested respondents at one point in 
time. This design is can gene...
Finally, a potential future study would be to compare these findings with YouTube 
channels that are owned by the video ga...
57 | P a g e 
8.0 Appendices 
Appendix A: Global Ad Spend Trends, 2014 
(Warc.com, 2014)
58 | P a g e 
Appendix B: Consumer Decision Process 
Need 
Recognition 
Author Generated as adapted from Engal, Kollar, an...
59 | P a g e 
Appendix C1: Outbound Marketing 
Lusch & Vargo, 2009 
Appendix C2: Inbound Marketing 
Lusch & Vargo, 2009
60 | P a g e 
Appendix D: Distributed Questionnaire
61 | P a g e
62 | P a g e
63 | P a g e
64 | P a g e 
Appendix E: Information Sheet for Participants
65 | P a g e 
Appendix F: Consent Form for Participation in Online Survey
66 | P a g e 
Appendix G: Ethics Approval from King’s College London
67 | P a g e 
Appendix H: Levels of Participation 
Parent, Plangger and Bal, 2011
68 | P a g e 
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 Ex...
70 | P a g e 
9.0 Bibliography 
 AdWeek, (2013). Native Ad Workshop Leaves FTC Perplexed. [online] Available at: 
http://...
 De Valck, K., Van Bruggen, G. and Wierenga, B. (2009). Virtual communities: A 
71 | P a g e 
marketing perspective. Deci...
Master's Dissertation - The Effectiveness of Online Brand Communities and User Engagement on Influencing Purchase: A YouTu...
Master's Dissertation - The Effectiveness of Online Brand Communities and User Engagement on Influencing Purchase: A YouTu...
Master's Dissertation - The Effectiveness of Online Brand Communities and User Engagement on Influencing Purchase: A YouTu...
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Master's Dissertation - The Effectiveness of Online Brand Communities and User Engagement on Influencing Purchase: A YouTube Case Study

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This is my Master's Dissertation in full. The purpose of my study was to build upon the most recent work in digital marketing research. More specifically, I tested the digital marketing tactics of building online brand communities and user engagement on how they actually influence purchase. In order to do this I chose the company Rooster Teeth as a case study. I chose Rooster Teeth as they are one of the highest subscribed YouTube channels and have a history of digital marketing excellence. I completed a massive cross-sectional research analysis with over 1,500 participants, used SPSS to analyze the quantitative data, and offered new insights to digital marketing researchers as well as an actionable plan to the case study organization.

Published in: Marketing

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

  1. 1. 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
  2. 2. 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.
  3. 3. 2 | P a g e 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
  4. 4. 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 3 | P a g e
  5. 5. 4 | P a g e 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
  6. 6. 5 | P a g e 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
  7. 7. 6 | P a g e 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.
  8. 8. Given that YouTube is largely under-researched, this paper will also seek to better understand 7 | P a g e 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,
  9. 9. this research aimed to provide detailed insight of these states by breaking them down to the 8 | P a g e 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.
  10. 10. 9 | P a g e 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
  11. 11. 10 | P a g e 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
  12. 12. setting in which the members’ interaction is primarily Internet-mediated (Fuller, Jawecki & Muhlbacher, 2007). Of significance, an online brand community exponentially enhances the 11 | P a g e 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.
  13. 13. 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 12 | P a g e 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)
  14. 14. 13 | P a g e 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.
  15. 15. 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 14 | P a g e 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.
  16. 16. 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 15 | P a g e 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.
  17. 17. 16 | P a g e 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
  18. 18. 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 17 | P a g e 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
  19. 19. 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 18 | P a g e 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.
  20. 20. 19 | P a g e 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
  21. 21. 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. 20 | P a g e 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.
  22. 22. 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 21 | P a g e 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.
  23. 23. 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 22 | P a g e 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:
  24. 24. 23 | P a g e 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.
  25. 25. 24 | P a g e 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
  26. 26. 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 & 25 | P a g e 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
  27. 27. had used. The design choice of maintaining consistency with previous work ensures validity 26 | P a g e 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.
  28. 28. 27 | P a g e 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
  29. 29. 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. 28 | P a g e 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
  30. 30. 16 were asked not to complete the questionnaire. Copies of the information sheet, consent 29 | P a g e 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-
  31. 31. 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 30 | P a g e 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
  32. 32. 31 | P a g e 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
  33. 33. 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 32 | P a g e 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
  34. 34. were run to determine if these factors were significant predictors of purchase intentions 33 | P a g e (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.
  35. 35. 34 | P a g e 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).
  36. 36. 35 | P a g e 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.
  37. 37. 36 | P a g e 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
  38. 38. logistical regression was run to determine if a prediction of probability existed between 37 | P a g e 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 intentions (Table 8).
  39. 39. 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 38 | P a g e 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 intentions (Table 9).
  40. 40. 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 39 | P a g e 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).
  41. 41. 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 40 | P a g e 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).
  42. 42. 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 41 | P a g e 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).
  43. 43. 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 42 | P a g e 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).
  44. 44. 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 43 | P a g e 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.
  45. 45. 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 44 | P a g e 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
  46. 46. 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 45 | P a g e 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
  47. 47. 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 46 | P a g e 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.
  48. 48. 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 47 | P a g e 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
  49. 49. 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 48 | P a g e 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.
  50. 50. 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 49 | P a g e 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).
  51. 51. 50 | P a g e 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.
  52. 52. 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 51 | P a g e 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.
  53. 53. 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
  54. 54. 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
  55. 55. 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.
  56. 56. 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.
  57. 57. 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
  58. 58. 57 | P a g e 8.0 Appendices Appendix A: Global Ad Spend Trends, 2014 (Warc.com, 2014)
  59. 59. 58 | P a g e 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
  60. 60. 59 | P a g e Appendix C1: Outbound Marketing Lusch & Vargo, 2009 Appendix C2: Inbound Marketing Lusch & Vargo, 2009
  61. 61. 60 | P a g e Appendix D: Distributed Questionnaire
  62. 62. 61 | P a g e
  63. 63. 62 | P a g e
  64. 64. 63 | P a g e
  65. 65. 64 | P a g e Appendix E: Information Sheet for Participants
  66. 66. 65 | P a g e Appendix F: Consent Form for Participation in Online Survey
  67. 67. 66 | P a g e Appendix G: Ethics Approval from King’s College London
  68. 68. 67 | P a g e Appendix H: Levels of Participation Parent, Plangger and Bal, 2011
  69. 69. 68 | P a g e Appendix I: Persuasion Knowledge Model Friestad and Wright, 1994
  70. 70. 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 69 | P a g e
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