Two Formulas for Success in Social Media: Learning and Network Effects
Open accessAbstract
Recent years have witnessed an unprecedented explosion in information technology that enables dynamic diffusion of user-generated content in social networks. Online videos, in particular, have changed the landscape of marketing and entertainment, competing with premium content and spurring business innovations. In the present study, we examine how learning and network effects drive the diffusion of online videos. While learning happens through informational externalities, network effects are direct payoff externalities. Using a unique data set from YouTube, we empirically identify learning and network effects separately, and find that both mechanisms have statistically and economically significant effects on video views; furthermore, the mechanism that dominates depends on the video type. Specifically, although learning primarily drives the popularity of quality-oriented content, network effects also make it possible for attention-grabbing content to go viral. Theoretically, we show that, unlike the diffusion of movies, it is the combination of both learning and network effects that generate the multiplier effect for the diffusion of online videos. From a managerial perspective, providers can adopt different strategies to promote their videos accordingly, that is, signaling the quality or featuring the viewer base depending on the video type. Our results also suggest that YouTube can play a much greater role in encouraging the creation of original content by leveraging the mu…
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Social media platforms collect masses of data that they display to content creators as performance metrics, such as the number of views, video ratings, and the number of subscribers.
“social media platforms collect masses of data that they display to content creators as performance metrics, such as the number of views, video ratings, and the number of subscribers”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Qiu et al. 2015
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Hödl, T., & Myrach, T. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00808-9 · Cited by 51 · see citing works
- Authors
- Qiu, L., Tang, Q., & Whinston, A. B.
- Affiliations
- University of Florida (US)
- Singapore Management University (SG)
- The University of Texas at Austin (US)
- Venue
- Journal of Management Information Systems
- Publisher
- Informa UK Limited
- Published
- 2015-10-02
- Volume
- 32
- Issue
- 4
- Pages
- 78-108
- ISSN
- 0742-1222 (ISSN-L)
- 1557-928X
- OpenAlex
- W3122497464
- Cited by
- 92
- References
- 8
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 11 · 2023: 9 · 2022: 9 · 2021: 10 · 2020: 7 · 2019: 10 · 2018: 13 · 2017: 5 · 2016: 2 · 2015: 1 · 2014: 1
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Management Information Systems (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- social media, learning effects, network effects, information externalities, payoff externalities, viral content, video views, YouTube, user-generated content, multiplier effect
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