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The one to watch: Heuristic Determinants of Viewership among Influential Twitch Streamers

Abstract

Twitch users watched over 1.2 billion hours of streaming video in a single month in 2020, with the vast majority of these hours devoted to videogames. The most popular streamers who create this content are often powerful influencers in a rapidly growing industry, and many industries now see videogame influencer marketing as a key aspect of their marketing mix. However, while some streamers have amassed incredible popularity on Twitch, the factors that drive live-streaming viewership remain poorly understood. This study empirically examines a large population of Twitch streamers to explore this existing gap in the current research and explain how potential viewers make the decision to patronize a Twitch streamer. Using panel data on the actions and characteristics of Twitch streamers combined with other sources, the study identifies the heuristic cues most associated with successful Twitch streamers. Ultimately, the study identifies and evaluates multiple heuristics around Twitch content delivery practices, with significant implications for any live-streaming context.

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Notes

  1. http://www.sullygnome.com Accessed June 10th, 2020.

  2. Faktenkontor, & Institut für Management- und Wirtschaftsforschung, 2017.

  3. https://store.steampowered.com/ Accessed June 19th, 2020.

  4. https://help.twitch.tv/s/article/achievements Accessed May 20th 2020.

References

  1. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, 100886. https://doi.org/10.1016/j.elerap.2019.100886

    Article  Google Scholar 

  2. Johnson, M. R., & Woodcock, J. (2019). ‘It’s like the gold rush’: the lives and careers of professional video game streamers on Twitch. tv. Information Communication & Society, 22(3), 336–351

    Article  Google Scholar 

  3. Wohn, D. Y., Jough, P., Eskander, P., Siri, J. S., Shimobayashi, M., & Desai, P. (2019). Understanding Digital Patronage: Why Do People Subscribe to Streamers on Twitch? In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (pp. 99–110)

  4. Establés, M. J., Guerrero-Pico, M., & Contreras-Espinosa, R. S. (2019). Gamers, writers and social media influencers: professionalisation processes among teenagers. Revista Latina de Comunicación Social, 74, 214–236

    Google Scholar 

  5. Woodcock, J., & Johnson, M. R. (2019). Live streamers on Twitch. tv as social media influencers: Chances and challenges for strategic communication. International Journal of Strategic Communication, 13(4), 321–335

    Article  Google Scholar 

  6. Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018). Social motivations of live-streaming viewer engagement on Twitch. Computers in Human Behavior, 84, 58–67. https://doi.org/10.1016/j.chb.2018.02.013

    Article  Google Scholar 

  7. Hu, M., Zhang, M., & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in Human Behavior, 75, 594–606

    Article  Google Scholar 

  8. Sjöblom, M., & Hamari, J. (2017). Why do people watch others play video games? An empirical study on the motivations of Twitch users. Computers in Human Behavior, 75, 985–996

    Article  Google Scholar 

  9. Hamilton, W. A., Garretson, O., & Kerne, A. (2014). Streaming on twitch: fostering participatory communities of play within live mixed media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1315–1324)

  10. Zhang, L., Peng, T. Q., Zhang, Y. P., Wang, X. H., & Zhu, J. J. (2014). Content or context: Which matters more in information processing on microblogging sites. Computers in Human Behavior, 31, 242–249

    Article  Google Scholar 

  11. Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological review, 79(4), 281

    Article  Google Scholar 

  12. Gigerenzer, G. (2004). Fast and frugal heuristics: The tools of bounded rationality. Blackwell handbook of judgment and decision making, 62, 88

    Google Scholar 

  13. Dogruel, L., Joeckel, S., & Bowman, N. D. (2015). Choosing the right app: An exploratory perspective on heuristic decision processes for smartphone app selection. Mobile Media & Communication, 3(1), 125–144

    Article  Google Scholar 

  14. Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193–225

    Article  Google Scholar 

  15. Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105–5131

    Article  Google Scholar 

  16. Xiao, M., Wang, R., & Chan-Olmsted, S. (2018). Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model. Journal of Media Business Studies, 15(3), 188–213

    Article  Google Scholar 

  17. Lou, C., & Yuan, S. (2019). Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media. Journal of Interactive Advertising, 19(1), 58–73. https://doi.org/10.1080/15252019.2018.1533501

    Article  Google Scholar 

  18. Jin, S. V., & Muqaddam, A. (2019). Product placement 2.0: “Do Brands Need Influencers, or Do Influencers Need Brands?”. Journal of Brand Management, 26(5), 522–537. https://doi.org/10.1057/s41262-019-00151-z

    Article  Google Scholar 

  19. Veirman, M. D., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828. https://doi.org/10.1080/02650487.2017.1348035

    Article  Google Scholar 

  20. Merz, J. (2019). From Trusted Friend to Trusted Brand? Influencer Marketing Between Trust and Mistrust. In T. Osburg, & S. Heinecke (Eds.), Media Trust in a Digital World: Communication at Crossroads (pp. 117–126). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-30774-5_8

    Chapter  Google Scholar 

  21. Liu, S., Jiang, C., Lin, Z., Ding, Y., Duan, R., & Xu, Z. (2015). Identifying effective influencers based on trust for electronic word-of-mouth marketing: A domain-aware approach. Information sciences, 306, 34–52

    Article  Google Scholar 

  22. More, J. S., & Lingam, C. (2019). A SI model for social media influencer maximization. Applied Computing and Informatics, 15(2), 102–108

    Article  Google Scholar 

  23. Zhang, Y., Lin, Y., & Goh, K. H. (2018). Impact of Online Influencer Endorsement on Product Sales: Quantifying Value of Online Influencer. In PACIS (p. 201)

  24. Park, H. S., Lee, H. Y., & Song, Y. H. (2014). A study on the social network influencer for long-tail marketing: Focusing on the Korean film industry. In Proceeding of Spring Conference on The Korea Society of Management information Systems (pp. 774–787)

  25. Booth, N., & Matic, J. A. (2011). Mapping and leveraging influencers in social media to shape corporate brand perceptions. Corporate Communications: An International Journal, 16(3), 184–191

    Article  Google Scholar 

  26. Gross, J., & Wangenheim, F. V. (2018). The Big Four of Influencer Marketing. A Typology of Influencers. Marketing Review St Gallen, 2, 30–38

    Google Scholar 

  27. Goldstein, D. G., Gigerenzer, G., Hogarth, R. M., Kacelnik, A., Kareev, Y., Klein, G. … Schlag, K. H. (2001). Why and when do simple heuristics work?. Bounded rationality: The adaptive toolbox. Dahlem Workshop Report (pp. 173–190). MIT Press

  28. Sjöblom, M., Törhönen, M., Hamari, J., & Macey, J. (2019). The ingredients of Twitch streaming: Affordances of game streams. Computers in Human Behavior, 92, 20–28

    Article  Google Scholar 

  29. Gigerenzer, G. (2008). Why heuristics work. Perspectives on psychological science, 3(1), 20–29

    Article  Google Scholar 

  30. Kahneman, D. (2011). Thinking, fast and slow. Macmillan

  31. Chen, R., Gaia, J., & Rao, H. R. (2020). An examination of the effect of recent phishing encounters on phishing susceptibility.Decision Support Systems,113287

  32. Vishwanath, A. (2017). Getting phished on social media. Decision Support Systems, 103, 70–81

    Article  Google Scholar 

  33. Chen, Y. C., Shang, R. A., & Kao, C. Y. (2009). The effects of information overload on consumers’ subjective state towards buying decision in the internet shopping environment. Electronic Commerce Research and Applications, 8(1), 48–58

    Article  Google Scholar 

  34. Subramaniam, M., Taylor, N. G., Jean, B. S., Follman, R., Kodama, C., & Casciotti, D. (2015). As simple as that?: Tween credibility assessment in a complex online world.Journal of Documentation

  35. Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of communication, 60(3), 413–439

    Article  Google Scholar 

  36. Sundar, S. S., Oeldorf-Hirsch, A., & Xu, Q. (2008). The bandwagon effect of collaborative filtering technology. In CHI’08 extended abstracts on Human factors in computing systems (pp. 3453–3458)

  37. Madden, M., & Fox, S. (2006). Riding the waves of “Web 2.0.”Pew internet and American life project, 5

  38. Hamari, J., & Sjöblom, M. (2017). What is eSports and why do people watch it? Internet research

  39. Lipkin, N. (2013). Examining Indie’s Independence: The meaning of” Indie” Games, the politics of production, and mainstream cooptation.Loading…, 7(11)

  40. Parker, F., Whitson, J. R., & Simon, B. (2018). Megabooth: The cultural intermediation of indie games. new media & society, 20(5), 1953–1972

    Article  Google Scholar 

  41. Harvey, A., & Fisher, S. (2013). Making a name in games: Immaterial labour, indie game design, and gendered social network markets. Information Communication & Society, 16(3), 362–380

    Article  Google Scholar 

  42. Mayzlin, D. (2006). Promotional Chat on the Internet. Marketing Science, 25(2), 155–163. https://doi.org/10.1287/mksc.1050.0137

    Article  Google Scholar 

  43. Lou, C., & Yuan, S. (2018). Understanding social media influencer marketing and its influence on consumer behavior: a theoretical framework and empirical evidence. In American Academy of Advertising. Conference. Proceedings (Online) (pp. 146–146). American Academy of Advertising

  44. Audrezet, A., de Kerviler, G., & Guidry Moulard, J. (2018). Authenticity under threat: When social media influencers need to go beyond self-presentation. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2018.07.008

    Article  Google Scholar 

  45. van Driel, L., & Dumitrica, D. (2020). Selling brands while staying “Authentic”: The professionalization of Instagram influencers.Convergence,1354856520902136

  46. Djafarova, E., & Trofimenko, O. (2019). ‘Instafamous’–credibility and self-presentation of micro-celebrities on social media. Information Communication & Society, 22(10), 1432–1446

    Article  Google Scholar 

  47. Hou, M. (2019). Social media celebrity and the institutionalization of YouTube. Convergence, 25(3), 534–553

    Article  Google Scholar 

  48. Kay, S., Mulcahy, R., & Parkinson, J. (2020). When less is more: the impact of macro and micro social media influencers’ disclosure. Journal of Marketing Management, 36(3–4), 248–278

    Article  Google Scholar 

  49. Jin, S. A., & Phua, J. (2014). Following celebrities’ tweets about brands: The impact of twitter-based electronic word-of-mouth on consumers’ source credibility perception, buying intention, and social identification with celebrities. Journal of advertising, 43(2), 181–195

    Article  Google Scholar 

  50. Lu, Y., Zhao, L., & Wang, B. (2010). From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention. Electronic Commerce Research and Applications, 9(4), 346–360. https://doi.org/10.1016/j.elerap.2009.07.003

    Article  Google Scholar 

  51. McCaffrey, M. (2019). The macro problem of microtransactions: The self-regulatory challenges of video game loot boxes. Business Horizons, 62(4), 483–495

    Article  Google Scholar 

  52. Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of pragmatics, 59, 210–220

    Article  Google Scholar 

  53. Mark, N. P. (2003). Culture and competition: Homophily and distancing explanations for cultural niches.American sociological review,319–345

  54. Brechwald, W. A., & Prinstein, M. J. (2011). Beyond homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21(1), 166–179

    Article  Google Scholar 

  55. Noon, E. J., & Meier, A. (2019). Inspired by Friends: Adolescents’ Network Homophily Moderates the Relationship Between Social Comparison, Envy, and Inspiration on Instagram. Cyberpsychology Behavior and Social Networking, 22(12), 787–793

    Article  Google Scholar 

  56. Sokolova, K., & Kefi, H. (2020). Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions.Journal of Retailing and Consumer Services, 53

  57. Nagy, P., & Koles, B. (2014). The digital transformation of human identity: Towards a conceptual model of virtual identity in virtual worlds. Convergence, 20(3), 276–292

    Article  Google Scholar 

  58. Lee, S. Y. (2015). Homophily and social influence among online casual game players. Telematics and Informatics, 32(4), 656–666

    Article  Google Scholar 

  59. Williams, K. D. (2010). The effects of homophily, identification, and violent video games on players. Mass Communication and Society, 14(1), 3–24

    Article  Google Scholar 

  60. Bainbridge, W. S. (2012). The Warcraft civilization: Social science in a virtual world. MIT Press

  61. Geraci, R. M. (2014). Virtually sacred: Myth and meaning in world of warcraft and second life. OUP Us

  62. Sjöblom, M., Törhönen, M., Hamari, J., & Macey, J. (2017). Content structure is king: An empirical study on gratifications, game genres and content type on Twitch. Computers in Human Behavior, 73, 161–171

    Article  Google Scholar 

  63. Hernandez, P. (2018, July 16). The Twitch streamers who spend years broadcasting to no one. The Verge. Retrieved June 19, 2020, from https://www.theverge.com/2018/7/16/17569520/twitch-streamers-zero-viewers-motivation-community

  64. Cox, J. (2014). What makes a blockbuster video game? An empirical analysis of US sales data. Managerial and Decision Economics, 35(3), 189–198

    Article  Google Scholar 

  65. Johnson, D., Watling, C., Gardner, J., & Nacke, L. E. (2014). The edge of glory: the relationship between metacritic scores and player experience. In Proceedings of the first ACM SIGCHI annual symposium on Computer-human interaction in play (pp. 141–150)

  66. O’hara, R. B., & Kotze, D. J. (2010). Do not log-transform count data. Methods in ecology and Evolution, 1(2), 118–122

    Article  Google Scholar 

  67. Frome, E. L., & Checkoway, H. (1985). Use of Poisson regression models in estimating incidence rates and ratios. American journal of epidemiology, 121(2), 309–323

    Article  Google Scholar 

  68. Lawless, J. F. (1987). Negative binomial and mixed Poisson regression. Canadian Journal of Statistics, 15(3), 209–225

    Article  Google Scholar 

  69. Wooldridge, J. M. (1990). A unified approach to robust, regression-based specification tests.Econometric Theory,17–43

  70. Hausman, J. A. (1978). Specification tests in econometrics.Econometrica: Journal of the econometric society,1251–1271

  71. Pop, R. A., Săplăcan, Z., Dabija, D. C., & Alt, M. A. (2021). The impact of social media influencers on travel decisions: The role of trust in consumer decision journey.Current Issues in Tourism,1–21

  72. Hossain, M. A., Dwivedi, Y. K., Chan, C., Standing, C., & Olanrewaju, A. S. (2018). Sharing political content in online social media: A planned and unplanned behaviour approach. Information Systems Frontiers, 20(3), 485–501

    Article  Google Scholar 

  73. Mackiewicz, J. (2010). Assertions of expertise in online product reviews. Journal of Business and Technical Communication, 24(1), 3–28

    Article  Google Scholar 

  74. Odrowska, A. M., & Massar, K. (2014). Predicting guild commitment in World of Warcraft with the investment model of commitment. Computers in Human Behavior, 34, 235–240

    Article  Google Scholar 

  75. Ge, J., & Gretzel, U. (2018). Emoji rhetoric: a social media influencer perspective. Journal of Marketing Management, 34(15–16), 1272–1295

    Article  Google Scholar 

  76. Sukmayadi, V., & Yahya, A. H. (2019). Impression Management within Instagram Stories: A Phenomenological Study.The Open Psychology Journal, 12(1)

  77. Weiser, E. B. (2015). # Me: Narcissism and its facets as predictors of selfie-posting frequency. Personality and Individual Differences, 86, 477–481

    Article  Google Scholar 

  78. Kim, E., Lee, J. A., Sung, Y., & Choi, S. M. (2016). Predicting selfie-posting behavior on social networking sites: An extension of theory of planned behavior. Computers in Human Behavior, 62, 116–123

    Article  Google Scholar 

  79. Erz, A., Marder, B., & Osadchaya, E. (2018). Hashtags: Motivational drivers, their use, and differences between influencers and followers. Computers in Human Behavior, 89, 48–60. https://doi.org/10.1016/j.chb.2018.07.030

    Article  Google Scholar 

  80. Curran, J. (2002). Media and power. Psychology Press

  81. Tang, Q., Gu, B., & Whinston, A. B. (2011). Content contribution under revenue sharing and reputation concern in social media: The case of YouTube

  82. Wilson, L., & Wu, Y. W. (2019). A little bit of money goes a long way: Crowdfunding on Patreon by YouTube sailing channels. Available at SSRN 2919840

  83. King, D. L., & Delfabbro, P. H. (2020). The convergence of gambling and monetised gaming activities. Current Opinion in Behavioral Sciences, 31, 32–36

    Article  Google Scholar 

  84. Zendle, D., Meyer, R., Cairns, P., Waters, S., & Ballou, N. (2020). The prevalence of loot boxes in mobile and desktop games. Addiction

  85. Zendle, D., Cairns, P., Barnett, H., & McCall, C. (2020). Paying for loot boxes is linked to problem gambling, regardless of specific features like cash-out and pay-to-win. Computers in Human Behavior, 102, 181–191

    Article  Google Scholar 

  86. Jenner, M. (2017). Binge-watching: Video-on-demand, quality TV and mainstreaming fandom. International Journal of Cultural Studies, 20(3), 304–320

    Article  Google Scholar 

  87. Matrix, S. (2014). The Netflix effect: Teens, binge watching, and on-demand digital media trends. Jeunesse: Young People Texts Cultures, 6(1), 119–138

    Article  Google Scholar 

  88. Zhou, J., Zhou, J., Ding, Y., & Wang, H. (2019). The magic of danmaku: A social interaction perspective of gift sending on live streaming platforms. Electronic Commerce Research and Applications, 34, 100815

    Article  Google Scholar 

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Church, E. The one to watch: Heuristic Determinants of Viewership among Influential Twitch Streamers. Electron Commer Res (2022). https://doi.org/10.1007/s10660-022-09589-x

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Keywords

  • Twitch
  • Live streaming
  • Influencer
  • Fast and frugal heuristics
  • Poisson Regression