Alternative Title
Social Learning and Network Effects in Social Media: Evidence from YouTube
Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2015
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 specific video type. Specifically, although learning primarily drives the popularity of quality-oriented content, network effects make it also 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 multiplier effect.
Keywords
Learning, Network Effects, User-Generated Content, Social Contagion, Social Media
Discipline
Computer Sciences | Databases and Information Systems | Social Media
Research Areas
Information Systems and Management
Publication
Journal of Management Information Systems
Volume
32
Issue
4
First Page
78
Last Page
108
ISSN
0742-1222
Identifier
10.1080/07421222.2015.1138368
Publisher
Taylor & Francis (Routledge): SSH Titles
Citation
QIU, Liangfei; Qian TANG; and WHINSTON, Andrew B..
Two formulas for success in social media: Learning and network effects. (2015). Journal of Management Information Systems. 32, (4), 78-108.
Available at: https://ink.library.smu.edu.sg/sis_research/3295
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07421222.2015.1138368