Understanding brand loyalty - The case of the eSports consumer from a relationship quality perspective

Abstract

Recently, the market of eSports continues to grow in popularity and value. Due to its rapid development a better understanding of the consumers' side of the eSports markets is important to meet its changing demands. In that context, it is especially important to gain a better understanding of loyalty towards eSports brands. Based on assumptions of the relationship quality perspective, we attempt to improve the understanding of loyalty towards an eSports brand using relationship quality (i.e., trust, identification, reputation, affect) and its antecedents (i.e., age, consumer similarity, attractiveness). Accordingly, we collected a sample with 216 eSports consumers to explain consumer loyalty using a structural equation modelling approach. Results show support for the postulated meaningfulness of relationship quality and attractiveness of the brand as predictors of consumer loyalty. Additionally, we reveal that consumer similarity and attractiveness have the potential to explain relationship quality in a positive fashion, contrary to age.

Keywords

• eSports consumers
• brand loyalty
• relationship quality

Introduction

During the last decade, the phenomenon of eSports – which can be understood as playing video games competitively [ 1 ] – moved into the public eye and enjoys greater popularity on younger audiences. Its corresponding economic and societal meaningfulness can be read off different numbers. The market of eSports had a value of nearly 900 million U.S. dollars in 2018 and is still on the rise. With an impressive audience growth over the last years and growing up to 453.8 million worldwide in 2019 [ 2 ].

Surprisingly, empirical research on the eSports market has been rather scarce [ 3–5 ] leaving several questions with relevance for both theory and practice unanswered. One such instance, is the loyalty of consumers towards a brand, which reflects the long-term relationship between them [ 6 ]. So far, brand loyalty is a largely uninvestigated dependent variable in the context of eSports, although it has proven to be of critical concern in neighboring disciplines (e.g., marking) for decades [ 7 ]. We aim to illuminate one of the existing blind spots by contributing to a better understanding of brand loyalty in the context of eSports, which is a highly relevant question for organizations involved in eSports. To achieve this, we consult literature from marketing, information systems, and consumer engagement and use the relationship quality perspective for the first time in the context of eSports [ 8–10 ]. Our research object is the LEC League of Legends Season 2019, consisting of ten different eSports brands (organizations). We make use of structural equation modelling and propose relationship quality and brand attractiveness as predictors of brand loyalty. The study is guided by the research question: Research Question: How can loyalty with an eSports brand be explained? By answering the research question, we aim to make the following contributions. First, our findings will allow academia to better understand the eSports market and extend the external validity of previously derived results in neighboring domains to the context of eSports. Second, it will provide practical implications, with the opportunity for eSports brands and organizations to increase their revenue and position themselves in an advantageous market position. 2 2.1

Related work

The eSports markets

eSports as a market is composed of different smaller markets, which are related to specific video games. The most popular games are online multiplayer games such as League of Legends (LoL), Counter-Strike (CS), Overwatch, Fortnite, and Player Unknown’s Battlegrounds (PUBG) [ 10 ]. To understand the monetary potential and societal success of eSports better, different unique characteristics and idiosyncrasies can be considered. The domain of eSports holds different unique features that make it worth a distinct and innovative topic of research. First, the eSports consumption is highly digitized and may happen from almost any remote location [ 1 ]. Second, eSports is still an emerging industry and the groups of consumers are exceptionally young [ 4 ]. Third, eSports is (opposed to older domains like traditional sports) fast changing allowing consumers a substantial amount of influence [ 1 ]. Fourth, the consumption good of eSports is oftentimes co-produced and the roles of passive watchers and active players are not as distinct as in older domains [ 11 ]. Accordingly, it is indicated that loyalty in the eSports context has different drivers compared to current marketing research that were already part of previous research [ 12 ]. Taken together, it seems indicated that eSports as a context will enjoy even greater popularity in the future, which underlines the importance of upcoming research. 2.2

Brand loyalty

Brand loyalty can be understood as the consumer's commitment to repurchase or continue to use the brand [ 13 ]. Additionally, it can be demonstrated by repeated positive behaviors such as word of mouth advocacy towards the brand. In neighboring disciplines brand loyalty was already examined from an attitudinal perspective emphasizing consumers’ commitment and emotional attachment to brands [ 14 ]. Previous research studied oppositional brand loyalty (e.g., the degree to which consumers express negative opinions on rival brands) [ 15 ], brand loyalty building in social media [ 16 ], the influence of consumers’ community participation on brand loyalty [ 17 ], and experience consumption [ 18 ]. Looking at these studies, we observe that only few of them examine the impacts of relational factors like the relationship between consumers and brands. For the purpose of our study, we understand brand loyalty as the consumption of brand related content (e.g., streams and videos) and the purchase of eSports merchandise, while assuming that brand loyalty can be best explained using a relational approach. 2.3

Relationship quality perspective

As the main predictor of brand loyalty in our study, we propose the quality of the relationship with eSports brands to be particular meaningful. The relationship quality perspective postulates that brand loyalty is primarily determined by relationship qualities that evaluates the strength or closeness of the relationship between the consumer and a brand [ 19 ]. Previous research suggests that organizations attempt to establish and maintain long-term relationships with consumers in order to manage uncertainties and reduce transaction costs [ 20 ]. From the consumer’s point of view, high relationship quality indicates that a consumer has faith in a company’s (or a brand’s) future performance due to prior (positive) interactions. Earlier research has shown that relationship quality has the potential to produce many positive outcomes, including sales effectiveness, firm performance, word of mouth, repurchase behavior, consumer retention, and loyalty [ 10, 21–24 ]. Another predictor that already showed its potential to predict brand loyalty in previous research in other domains is the attractiveness of a brand [ 8 ]. We postulate that attractiveness of a brand is a particularly meaningful predictor of brand loyalty because it represents a significant share of the positive appearance of a brand from a consumers perspective. Accordingly, we propose the subsequent hypothesis: Hypothesis 1: Relationship quality has a positive effect on brand loyalty. Hypothesis 2: Attractiveness of a brand has a positive effect on brand loyalty. Relationship quality is usually conceptualized in the literature as a multidimensional construct with different measurements [ 25 ]. For the purpose of our paper, we capture the construct of relationship quality with the following four variables.

First, we use trust as a part of the construct, which is the consumer’s psychological state that depicts trusting beliefs (e.g., honesty and reliability) towards a brand or other entities [ 26 ]. Second, we insert identification with a brand, which describes the psychological relatedness to the brand referring to one’s ongoing desire to sustain a relationship with the brand [ 27 ]. As a third part, we use brand reputation, which describes the belief of a consumer that a brand has a particular characteristic [ 28 ]. Fourth, the affective relation towards a brand already proved its potential to explain brand loyalty [ 29 ]. Given the importance of relationship quality in previous research, a number of studies have investigated the antecedents of relationship quality. For our purpose, we follow the references from Gounaris and Stathakopoulos, who posited that consumer, social, and brand characteristics are critical for developing consumer and brand relationships, which can cover different relational statements [ 30 ]. Concretely, we build on previous findings that seem to be especially relevant in the context of eSports and aim to increase their external validity. First, based on the young age and vulnerability of consumers in the context of eSports, we propose age to have an negative effect on relationship quality because of the lack of young consumers experiences and knowledge in terms of consumption in general [ 31 ]. Second, we test the perceived similarity to other consumers, which is a well-established social characteristic in previously well researched contexts [ 32–34 ]. We propose similarity to have a positive effect on relationship quality based on the idea that consumers join and form stronger ties with brands that convey similar characteristics to themselves. Third, we postulate that the attractiveness as a brand characteristic has a disjunctive positive effect (besides the direct effect on loyalty) on relationship quality as well [ 8 ]. The corresponding postulate is based on previous findings that indicated positive influences of meaningful brand associations (i.e., attractivity) on consumers. Accordingly, we propose the following hypotheses: Hypothesis 3: The age of consumers has a negative effect on relationship quality. Hypothesis 4: Consumer similarity has a positive effect on relationship quality. Hypothesis 5: Attractiveness of a brand has a positive effect on relationship quality. 3 3.1

Methodology

Research design

To answer the hypotheses and the research questions of our study, we used a crosssectional survey and collected self-reports of eSports consumers with an online questionnaire. Accordingly, we analyzed the data with covariance-based statistics and structural equation modelling to get insights into explanations of loyalty towards an eSports brand using relationship management as its main predictor (see figure 1). After informed consent was obtained, the first part of the online questionnaire instructed participants to select one of the ten professional organizations of the LEC League of Legends Season 2019 (i.e., Splyce, Fnatic, Team Vitality, G2 eSports, Rogue, SK Gaming, Excel eSports, FC Schalke 04 eSports, Misfits, Origen) that they were most familiar with. Those were followed by independent variables (predictors of relationship quality), and consumer similarity and attractiveness. Second, the participants filled out the scales for the variable relationship quality consisting of trust, identification, reputation, and affect towards the brand. Third, the dependent variable franchise loyalty of an eSports brand was presented. Finally, participants filled out the demographic data and received a code for their payment. To increase the quality of our data obtained, we inserted multiple attentions check items (i.e., “pick a number smaller than two”) to exclude inattentive respondents. 3.3

Participants

We surveyed 216 eSports consumers, by using the crowdsourcing marketplace Mechanical Turk (MTurk). All the participants received $1.09 as compensation for taking part in the study. The majority of the sample consisted of males (161 males, 55 females), and the average age was around 30 years (M = 30.98, SD = 7.36). Most participants were Americans (158), followed by Indians (47), and more than half of the participants stated that they already completed their bachelor’s degree (54%) in their respective countries. Additionally, the participants reported that they had started consuming eSports a little over three years ago (M = 3.35, SD = 1.86). 3.4

Construct measures

Unless otherwise stated, all items were measured on a seven-point Likert scale, ranging from 1 “strongly disagree” to 7 “strongly agree”. Afterwards, we aggregated the items to an evenly distributed sum-score.

Dependent variable. We measured our dependent variable franchise loyalty by adapting a validated scale to the context of our study [ 6 ]. The measurement comprises four items (e.g., “I will watch this brand the next time I watch eSports”; M = 5.33, SD = 1.27, α = .89).

Mediating variables. We followed recommendations from previous literature and measured relationship quality with different well-established constructs [ 9 ]. First, we used an existing scale [ 26 ] measuring trust consisting of five items (e.g., “The brand is honest”; M = 5.18, SD =.99, α = .78). Second, we measured brand identification using two items from previous literature [ 27 ]. A visual item, which included a series of Venn diagrams indicating the extent of overlap between the consumers’ self and the brand identity, and the respondents were required to choose the level of overlap that best reflected their relationship with the brand. The verbal item asked the consumers about their accordance with one statement (i.e., “My self-image overlaps with the image of the brand”) and aggregated both items to a single value (M = 4.82, SD = 1.40, α = .67). Third, we used a well-established measurement [ 28 ] of reputation with fifteen items (e.g., “The brand reduce its profits to ensure a clean environment”; M = 5.44 SD = .85, α = .93). Finally, we measured the affect towards the eSports brand with three items from previous literature [ 29 ].

Independent variables. First, we asked participants of our study to state their age in an open text field (M = 30.89, SD = 7. 63). Second, we measured consumer similarity with three items from previous literature (e.g., "The typical consumer of the brand is similar to me"; M = 5.02, SD = 1.29, α = .89) [ 32 ]. Third, we measured attractiveness of the brand with four items (e.g., "I like what the brand stands for"; M = 5.60, SD = .87, α = .82) [ 37 ]. 4 4.1

Results

Preceding analyses

To test if any of the sociodemographic (gender, education, country) or control variables (started watching eSports) had a confounding effect on brand loyalty, we carried out a multiple linear regression analysis and used the sociodemographic and control variables as predictors to explain brand loyalty. The regression equation showed a significant result (F (4, 211) = 9.30, p < .001) and explained 13% of the variance in loyalty. Only the regression weight of education (β = .38, p < .001) showed a significant effect (all others p ≥ .32). Thus, we assumed that we only had to consider the direct effect of education of eSports brand loyalty. 4.2

Hypotheses testing

To test the hypotheses of our study, we inserted the relationships postulated in our research model and the previously identified confound of education in a structural equation model using the software AMOS and applied a maximum likelihood estimation to specify the model. Additionally, we allowed relationships between the identified confound education and other variables as well as correlation between the independent variables following suggested modification indices. The inferential test of the model indicated good fit between the empirical data and the postulated model (χ2 (df = 6; N = 216) = 9.65; p = .14). Additional fit indices confirmed the positive impression (CFI = .99, SRMR = .03, GFI = .98, RMSEA = .05). The subsequent Figure 2 shows the relationships between the constructs of the derived SEM. Using the results presented in Figure 2, we are now able to answer the hypotheses of our study. First, regarding direct predictors of brand loyalty, we can make the subsequent empirical statements. Regarding hypothesis 1, we found the postulated positive relationships between relationship quality as a predictor of brand loyalty (β = .55, p < .001). About hypothesis 2, we found empirical support as well. Accordingly, attractiveness of a brand predicted brand loyalty in a positive fashion (β = .31, p < .001) and the previously identified confound education did not play a meaningful role (β = .08, p = .06). Additionally, the inserted predictors were able to explain .70 % of the variance of the dependent variable brand loyalty. Second, referring to predictors of relationship quality, we can answer the predicted hypotheses as well. Accordingly, age predicted relationship quality in a negative way (hypothesis 3; β = -.11, p < .01) and consumer similarity (hypothesis 4; β = .50, p < .001) and brand attractiveness (hypothesis 5; β = .38, p < .001) contributed in a positive fashion. Additionally, all predictors were able to explain .68% of the variance of the dependent variable relationship quality. The subsequent table summarizes the results of our study. To get further insights into the relationships within our data and derive additional points of reference for future research, we carried out an additional regression analysis to explore which of the constructs we used as an operationalization for relationship quality had the most meaningful impact on brand loyalty. This idea was based on the observation that relationship quality covered a rather big content-related interval. Accordingly, we inserted the four constructs trust, identification, reputation, and affect towards a brand as predictors of brand loyalty, while controlling for the identified confound education. The regression equation showed a significant result (F (5, 210) = 141.39, p < .001) and explained 77% of the variance in brand loyalty. Additionally, the regression weights of identification (β = .10, p < .05), reputation (β = .37, p < .001), affect (β = .49, p < .001) and education (β = .13, p < .01) showed significant results (other p = .47). Accordingly, reputation and affect seem to be most meaningful predictors of loyalty. 5

Discussion

Based on the derived results of our study, we are now able to answer our research question. Relationship quality and brand attractiveness predicted brand loyalty (hypotheses 1 and 2). This finding is in line with previous research [ 8, 19 ]. In addition, and on a more granular level of observation, additional analysis indicated that brand reputation and the affect towards a brand were the most meaningful predictors of consumer loyalty. We understand our findings in a way that organizations involved in the context of eSports should put more effort into building their brand. Fruitful avenues for the realization could be to more holistically build brand images and communicate them in a solid fashion or use tools of strategic management and test the effects of strategies like brand diversification.

With our results, we found empirical support for our postulated predictors of relationship quality. Regarding hypotheses 3, we found a negative effect of age on relationship quality. As an explanation for this finding, we refer to research regarding young consumer vulnerability and developmental psychology, which showed that it is easier to influence younger consumers in a desired way due to their rather insecure self-concept (compared to older consumers) [ 31, 38 ]. Accordingly, older consumers evaluate the relationship quality more critical, which is one way to understand the negative influence of age. We think that this effect is even more important in the context of eSports due to the selective group of young consumers and the rather adolescent image of eSports and video games in general. We understand this finding as a request for eSports organizations to strengthen the use of customer segmentation techniques to ensure a target group specific approach of consumers. Regarding hypotheses 4, consumer similarity significantly predicted relationship quality. Accordingly, we extended the external validity of findings from previous research to the context of eSports [ 33, 34 ]. This finding seems especially interesting, since ordinary marketing tools like segmentation, strategies of consumers regarding the communication strategy are not used fully of their potential by eSports organizations, which indicates so far unexploited potential. With regard to hypothesis 5, attractiveness was a significant predictor of relationship quality as well, which supports findings from neighboring disciplines [ 8 ]. Accordingly, we encourage organizations involved in eSports to use tools of market research and explore aspects increasing brand attractiveness from a consumer’s perspective.

Like every empirical study, our approach includes limitations as well. Subsequently, we will name some of them and illustrate potential ways how future research can deal with them. First, we had to decide what dimensions to include in the higher-level concept relationship quality. We do not claim that other measurements would not be able to illustrate relationship quality in an appropriate way. We understand our study as a first step to show that relationship quality as a construct holding potential to better understand eSports consumers. However, we recommend comparing other dimensions of relationship quality (e.g., satisfaction) in future studies. Second, we are aware that a large number of variables can be connected to the perception of relationship quality and our approach is only one meaningful possibility. Nonetheless, we encourage future studies to holistically explore the relationship between potential antecedents and relationship quality. This can be achieved building on insights from previous research in neighboring contexts [ 12 ]. One fruitful avenue would be the integration of (more) relational and social value components as predictors of relationship quality. 6

Conclusion

Since eSports as a phenomenon is innovative, fast changing, and undergoes rising levels of popularity, the need for understanding the contemporary picture is crucial for academia and practice. An important question in this regard concerns the explanation of brand loyalty from a consumer’s perspective. The major contribution of our study is that we were able to show that the relationship quality perspective holds a lot of potential to get insights into an explanation of brand loyalty. Accordingly, we extended the external validity from findings from neighbouring disciplines for the first time to the context of eSports, which promise a variety of fruitful avenues for future research.

References

1. Scholz , T. : Esports is Business: Management in the World of Competitive Gaming . Springer Berlin Heidelberg, New York, NY ( 2019 ).

2. Newzoo: Global Esports Economy Will Top $1 Billion for the First Time in 2019 , https://newzoo.com/insights/articles/newzoo -global-esports-economy-will-top-1-billionfor-the- first- time-in-2019/, last accessed 2019 /10/02.

3. Weiss , T. , Schiele , S. : Virtual Worlds in Competitive Contexts: Analyzing eSports Consumer Needs . Electronic Markets . 23 , 307 - 316 ( 2013 ).

4. Hamari , J. , Sjöblom , M. : What is eSports and Why Do People Watch it ? Internet Research. 27 , 211 - 232 ( 2017 ).

5. Hallmann , K. , Giel , T.: eSports - Competitive Sports or Recreational Activity? Sport Management Review . 21 , 14 - 20 ( 2018 ).

6. Chaudhuri , A. , Holbrook , M.B. : The Chain of Effects from Brand Trust and Brand Affect to Brand Performance: The Role of Brand Loyalty . Journal of Marketing . 65 , 81 - 93 ( 2001 ).

7. Oliver , R.L. : Whence Consumer Loyalty? Journal of Marketing . 63 , 33 - 44 ( 1999 ).

8. Elbedweihy , A.M. , Jayawardhena , C. , Elsharnouby , M.H. , Elsharnouby , T.H. : Customer Relationship Building: The Role of Brand Attractiveness and Consumer-Brand Identification . Journal of Business Research . 69 , 2901 - 2910 ( 2016 ).

9. Zhang , X. , Fang , Y. , He , W. , Zhang, Y.: Epistemic Motivation and Knowledge Contribution Behaviors in Wiki Teams: A Cross-Level moderation Model . In PACIS ( 2016 ).

10. Palmatier , R.W. , Dant , R.P. , Grewal , D. , Evans , K.R. : Factors Influencing the Effectiveness of Relationship Marketing: A Meta-Analysis . Journal of Marketing . 70 , 136 - 153 ( 2006 ).

11. Andrews , D.L. , Ritzer , G.: Sport and Prosumption . Journal of Consumer Culture . 18 , 356 - 373 ( 2018 ).

12. Gremler , D.D., Van Vaerenbergh , Y. , Brüggen , E.C. , Gwinner , K.P. : Understanding and Managing Customer Relational Benefits in Services: A Meta-Analysis . Journal of the Academy of Marketing Science . ( 2019 ).

13. Dick , A.S. , Basu , K. : Customer Loyalty: Toward an Integrated Conceptual Framework . Journal of the Academy of Marketing Science . 22 , 99 - 113 ( 1994 ).

14. Shang , R.-A., Chen , Y.-C. , Liao , H.-J.: The Value of Participation in Virtual Consumer Communities on Brand Loyalty. Internet Research . 16 , 398 - 418 ( 2006 ).

15. Kuo , Y.-F. , Feng , L.-H.: Relationships Among Community Interaction Characteristics, Perceived Benefits, Community Commitment, and Oppositional Brand Loyalty in Online Brand Communities . International Journal of Information Management . 33 , 948 - 962 ( 2013 ).

16. Laroche , M. , Habibi , M.R. , Richard, M.-O.: To Be or Not to Be in Social Media: How Brand Loyalty is Affected by Social Media? International Journal of Information Management . 33 , 76 - 82 ( 2013 ).

17. Casaló , L. , Flavián , C. , Guinalíu , M. : Impact of Participation in Virtual Brand Communities on Consumer Trust and Loyalty: The Case of Free Software . Online Information Review . 31 , 775 - 792 ( 2007 ).

18. Yang , X. , Mao , H. , Peracchio , L.A. : It's Not Whether You Win or Lose, It's How You Play the Game? The Role of Process and Outcome in Experience Consumption . Journal of Marketing Research . 49 , 954 - 966 ( 2012 ).

19. Zhang , K.Z.K. , Benyoucef , M. , Zhao , S.J. : Building Brand Loyalty in Social Commerce: The Case of Brand Microblogs. Electronic Commerce Research and Applications . 15 , 14 - 25 ( 2016 ).

20. Morgan , R.M. , Hunt , S.D. : The Commitment-Trust Theory of Relationship Marketing . Journal of Marketing . 58 , 20 - 38 ( 1994 ).

21. Athanasopoulou , P. : Relationship Quality: A Critical Literature Review and Research Agenda . European Journal of Marketing . 43 , 583 - 610 ( 2009 ).

22. Crosby , L.A. , Evans , K.R. , Cowles , D. : Relationship Quality in Services Selling: An InterPersonal Influence Perspective . Journal of Marketing . 54 , 68 - 81 ( 1990 ).

23. Zhang , J., Bloemer , J.M.: The Impact of Value Congruence on Consumer-Service Brand Relationships . Journal of Service Research . 11 , 161 - 178 ( 2008 ).

24. Sanchez-Franco , M , Ramos , A. , Velicia , F. : The Moderating Effect of Gender on Relationship Quality and Loyalty Toward Internet Service Providers . Information & Management . 46 , 196 - 202 ( 2009 ).

25. Liang , T.-P., Ho , Y.-T., Li , Y.-W. , Turban , E.: What Drives Social Commerce: The Role of Social Support and Relationship Quality . International Journal of Electronic Commerce . 16 , 69 - 90 ( 2011 ).

26. Gefen , D. , Karahanna , E. , Straub , D.W. : Trust and TAM in Online Shopping: An Integrated Model . MIS Quarterly . 27 , 51 - 90 ( 2003 ).

27. Kordyaka , B. , Hribersek , S. : Crafting Identity in League of Legends - Purchases as a Tool to Achieve Desired Impressions . In Proceedings of the HICSS ( 2019 ).

28. Walsh , G. , Beatty , S.E. , Shiu , E.M.K. : The Customer-Based Corporate Reputation Sale: Replication and Short Form . Journal of Business Research . 62 , 924 - 930 ( 2009 ).

29. Kenning , P. , Viktoria Rampl , L. : Employer Brand Trust and Affect: Linking Brand Personality to Employer Brand Attractiveness . European Journal of Marketing . 48 , 218 - 236 ( 2014 ).

30. Gounaris , S. , Stathakopoulos , V. : Antecedents and Consequences of Brand Loyalty: An Empirical Study . Journal of Brand Management . 11 , 283 - 306 ( 2004 ).

31. Pechmann , C. , Moore , E.S. , Andreasen , A.R. , Connell , P.M. , Freeman , D. , Gardner , M.P. , Heisley , D. , Lefebvre , R.C. , Pirouz , D.M. , Soster , R.L. : Navigating the Central Tensions in Research on At-Risk Consumers: Challenges and Opportunities . Journal of Public Policy & Marketing . 30 , 23 - 30 ( 2011 ).

32. Sirgy , M.J. : Self-Concept in Consumer Behavior: A Critical Review . Journal of Consumer Research . 9 , 287 - 300 ( 1982 ).

33. Hoffner , C. , Buchanan , M. : Young Adults' Wishful Identification with Television Characters: The Role of Perceived Similarity and Character Attributes . Media Psychology . 7 , 325 - 351 ( 2005 ).

34. Al-Natour , S. , Benbasat , I. , Cenfetelli , R.T.: The Role of Design Characteristics in Shaping Perceptions of Similarity: The Case of Online Shopping Assistants . Journal of the Association for Information Systems . 7 , 821 - 861 ( 2006 ).

35. Bathurst : The Average Age of Esports Viewers is Higher Than You May Think, Says GameScape , LLC, https://esportsobserver.com /average-age-esports-viewers-gamescape/ , last accessed 2019 /10/01.

36. Bergami , M. , Bagozzi , R.P. : Self‐Categorization , Affective Commitment and Group SelfEsteem as Aspects of Social Identity in Organizations . British Journal of Social Psychology . 39 , 555 - 577 ( 2000 ).

37. Bhattacharya , C.B. , Sen , S. : Consumer-Company Identification : A Framework for Understanding Consumers' Relationships with Companies . Journal of Marketing . 67 , 76 - 88 ( 2003 ).

38. Batat , W.: How Do Adolescents Define Consumer Vulnerability? Toward a Youth-Centric Approach . ACR North American Advances. ( 2012 ).