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Singapore Management University

Communication

Research Collection School Of Computing and Information Systems

YouTube

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Full-Text Articles in Business

Content Contribution For Revenue Sharing And Reputation: A Dynamic Structural Model, Qian Tang, Bin Gu, Andrew B. Whinston Oct 2012

Content Contribution For Revenue Sharing And Reputation: A Dynamic Structural Model, Qian Tang, Bin Gu, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

This study examines the incentives for content contribution in social media. We propose that exposure and reputation are the major incentives for contributors. Besides, as more and more social media Web sites offer advertising-revenue sharing with some of their contributors, shared revenue provides an extra incentive for contributors who have joined revenue-sharing programs. We develop a dynamic structural model to identify a contributor's underlying utility function from observed contribution behavior. We recognize the dynamic nature of the content-contribution decision-that contributors are forward-looking, anticipating how their decisions affect future rewards. Using data collected from YouTube, we show that content contribution is …


Content Contribution Under Revenue Sharing And Reputation Concern In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston Dec 2011

Content Contribution Under Revenue Sharing And Reputation Concern In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

A key feature of social media is that it allows individuals and businesses to contribute contents for public viewing. However, little is known about how content providers derive payoffs from such activities. In this study, we build a dynamic structural model to recover the utility function for content providers. Our model distinguishes short-term payoffs based on ad revenue sharing from long-term payoffs driven by content providers’ reputation. The model was estimated using a panel data of 914 top 1000 providers and 381 randomly selected providers on YouTube from Jun 7th, 2010, to Aug 7th, 2011. The two different sets of …