Two formulas for success in social media: Learning and network effects

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...

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Main Authors: QIU, Liangfei, Qian TANG, WHINSTON, Andrew B.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/sis_research/3295
https://ink.library.smu.edu.sg/context/sis_research/article/4297/viewcontent/1301633.pdf
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spelling sg-smu-ink.sis_research-42972020-01-11T15:08:32Z Two formulas for success in social media: Learning and network effects QIU, Liangfei Qian TANG, WHINSTON, Andrew B. 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. 2015-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3295 info:doi/10.1080/07421222.2015.1138368 https://ink.library.smu.edu.sg/context/sis_research/article/4297/viewcontent/1301633.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Learning Network Effects User-Generated Content Social Contagion Social Media Computer Sciences Databases and Information Systems Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Learning
Network Effects
User-Generated Content
Social Contagion
Social Media
Computer Sciences
Databases and Information Systems
Social Media
spellingShingle Learning
Network Effects
User-Generated Content
Social Contagion
Social Media
Computer Sciences
Databases and Information Systems
Social Media
QIU, Liangfei
Qian TANG,
WHINSTON, Andrew B.
Two formulas for success in social media: Learning and network effects
description 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.
format text
author QIU, Liangfei
Qian TANG,
WHINSTON, Andrew B.
author_facet QIU, Liangfei
Qian TANG,
WHINSTON, Andrew B.
author_sort QIU, Liangfei
title Two formulas for success in social media: Learning and network effects
title_short Two formulas for success in social media: Learning and network effects
title_full Two formulas for success in social media: Learning and network effects
title_fullStr Two formulas for success in social media: Learning and network effects
title_full_unstemmed Two formulas for success in social media: Learning and network effects
title_sort two formulas for success in social media: learning and network effects
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/sis_research/3295
https://ink.library.smu.edu.sg/context/sis_research/article/4297/viewcontent/1301633.pdf
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