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

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 multiplier effect.

Key words and phrases:

• learning
• network effects
• social contagion
• social media
• user-generated content

Acknowledgments

The authors would like to thank Jesse Shore, Gal Oestreicher-Singer, Shun-Yang Lee, and the participants of the 2013 Workshop on Information Systems and Economics (WISE) for helpful feedback. The authors are grateful to the editor and the three anonymous reviewers for their constructive comments that have led to the significant improvement of this paper.

Supplemental File

Supplemental data for this article can be accessed on the publisher’s website at http://dx.doi.org/10.1080/07421222.2015.1138368

Notes

1. See http://www.comscore.com/insights/Press_Releases/2013/7/comScore_Releases_June_2013_US_Online_Video_Rankings.

2. Following Banerjee [12], the timing of consumption is exogenously given, and we do not consider the strategic behavior of delaying the decision-making process to obtain more feedback.

3. To make our results generalizable to unpopular providers and to compare with top providers, we also identified 2,236 new providers who joined YouTube during January 2012. The basic results are robust.

4. An alternative explanation is that, in contrast to a tech video, consumers are more likely to watch a music video without asking for others’ opinions because once they find it is a good music video, they can watch it more than once in future.

5. In other words, if learning plays a dominate role in the diffusion, the negative disconfirmation will not significantly affect the word-of-mouth process on video content. For instance, when one’s friends recommend a new trending video, they are more likely to talk about the video content instead of the in-stream ads.

Additional information

Notes on contributors

Liangfei Qiu

Liangfei Qiu (corresponding author; [email protected]) is an assistant professor of information systems at the Warrington College of Business Administration, University of Florida. He received his Ph.D. in economics from the University of Texas at Austin. His research focuses on analytical and empirical studies of location-based social networks, smart mobile data pricing, prediction markets, procurement auctions, and applied game theory. His work has appeared in Journal of Management Information Systems and Decision Support Systems.

Qian Tang

Qian Tang is an assistant professor of information systems at the School of Information Systems, Singapore Management University. She received her Ph.D. in management information systems from the University of Texas at Austin. Her research interests include economics of information systems, social media, social networking, online and offline markets, and economics of cyber security. Her work has appeared in Journal of Management Information Systems, Decision Support Systems, and Information and Management.

Andrew B. Whinston

Andrew B. Whinston is Hugh Cullen Chair Professor in the Information, Risk, and Operation Management Department at the McCombs School of Business at the University of Texas at Austin. He is also the director of the Center for Research in Electronic Commerce. His recent papers have appeared in Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Management Science, Marketing Science, Journal of Marketing, and Journal of Economic Theory. He has published over 300 papers in the major economic and management journals and has coauthored 27 books.