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Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content

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  • Liu-Thompkins, Yuping
  • Rogerson, Michelle

Abstract

With the explosive growth of online user-generated content and the desire by marketers to better utilize this space, it is beneficial to understand the viral diffusion of such content and to identify messages that are most likely to achieve popularity. In this paper, we combine network analysis and the diffusion literature to study the spreading of user-generated videos online. We identify three groups of factors that affect diffusion outcomes: network structure, content characteristics, and author characteristics. Using a proportional rates model, we analyze the diffusion of a sample of videos on YouTube. Our results show that it is preferable to have many subscribers who each has a few friends than to have a few subscribers with many connections. Furthermore, a curvilinear relationship exists between subscriber network connectivity and diffusion rate such that diffusion is at its highest under moderate connectivity. Examining content characteristics, we show that entertainment and educational values affect diffusion but production quality does not matter. Moreover, we find that quality as manifested by user ratings influences diffusion more than innate content quality. Not surprisingly, an author's past success carries over to the current content, and content from younger authors is more popular.

Suggested Citation

  • Liu-Thompkins, Yuping & Rogerson, Michelle, 2012. "Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 71-82.
  • Handle: RePEc:eee:joinma:v:26:y:2012:i:2:p:71-82
    DOI: 10.1016/j.intmar.2011.11.003
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    3. Katharina Baum & Annika Baumann & Katharina Batzel, 2024. "Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(3), pages 335-355, June.
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    5. Hautz, Julia & Füller, Johann & Hutter, Katja & Thürridl, Carina, 2014. "Let Users Generate Your Video Ads? The Impact of Video Source and Quality on Consumers' Perceptions and Intended Behaviors," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 1-15.
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    7. Schwenzow, Jasper & Hartmann, Jochen & Schikowsky, Amos & Heitmann, Mark, 2021. "Understanding videos at scale: How to extract insights for business research," Journal of Business Research, Elsevier, vol. 123(C), pages 367-379.
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    10. Li, Lei & Zhang, Jiayang & An, Xun, 2023. "Using social media for efficient brand marketing: An evaluation of Chinese Universities using Bilibili," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    11. Rietveld, Robert & van Dolen, Willemijn & Mazloom, Masoud & Worring, Marcel, 2020. "What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 20-53.
    12. Labrecque, Lauren I. & vor dem Esche, Jonas & Mathwick, Charla & Novak, Thomas P. & Hofacker, Charles F., 2013. "Consumer Power: Evolution in the Digital Age," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 257-269.
    13. Jurui Zhang & Raymond Liu, 2017. "Popularity of digital products in online social tagging systems," Journal of Brand Management, Palgrave Macmillan, vol. 24(1), pages 105-127, January.
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