Author List: Qiu, Liangfei; Tang, Qian; Whinston, Andrew B.;
Journal of Management Information Systems, 2015, Volume 32, Issue 4, Page 78-108.
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. > >
Keywords: learning network effects; social contagion; social media ;user-generated content
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#131 0.296 media social content user-generated ugc blogs study online traditional popularity suggest different discourse news making anonymity marketing videos choices page
#285 0.160 effects effect research data studies empirical information literature different interaction analysis implications findings results important set large provide using paper
#112 0.110 services service network effects optimal online pricing strategies model provider provide externalities providing base providers fee complementary demand offer derive
#95 0.098 learning mental conceptual new learn situated development working assumptions improve ess existing investigates capture advanced proposes types context building acquisition
#249 0.080 network networks social analysis ties structure p2p exchange externalities individual impact peer-to-peer structural growth centrality participants sharing economic ownership embeddedness
#49 0.067 adoption diffusion technology adopters innovation adopt process information potential innovations influence new characteristics early adopting set compatibility time initial current