Cited by 92Digital Platforms and EconomicsDigital Marketing and Social MediaInnovation Diffusion and Forecasting

Two Formulas for Success in Social Media: Learning and Network Effects

Open access
Qiu, Liangfei, Tang, Qian, Whinston, Andrew B.·2015·Journal of Management Information Systems

Abstract

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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Crossref · 10.1080/07421222.2015.1138368

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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”

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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
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
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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