Digital Content Creation: An Analysis of the Impact of Recommendation Systems

Kun Qian () and Sanjay Jain ()
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Kun Qian: Department of Marketing at the College of Business, Southern University of Science and Technology, Shenzhen 518055, China; Marketing Department at the Isenberg School of Management, University of Massachusetts at Amherst, Amherst, Massachusetts 01003
Sanjay Jain: Department of Marketing at the Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080

Management Science, 2024, vol. 70, issue 12, 8668-8684

Abstract: The success of digital content platforms, such as YouTube, relies on both the creativity of independent content creators and the efficiency of content distribution. By sharing advertising revenue with content creators, these platforms can motivate creators to exert greater effort. Most platforms use recommendation systems to deliver personalized content recommendations to each consumer. As creators’ revenues are contingent on their demand, the demand allocation criteria inherent in the recommendation system can influence their content creation behavior. In this paper, we investigate the influence of a platform’s recommendation system on revenue-sharing plans, content creation, profits, and welfare. Our results show that a platform could benefit by biasing recommendations, that is, recommending content that is not an ideal match to a consumer’s preference, to incentivize creators to produce better-quality content. We refer to this as a biased recommendation strategy. Interestingly, we find that such a biased recommendation strategy may lead to a win-win in which the platform, consumers, and content creators can benefit. Our study also shows that consumers may be worse off when they are more knowledgeable and less dependent on the recommendation system. In addition, the platform, consumers, and creators can benefit when the platform has more accurate information on consumer preferences.

Keywords: content creation; digital platform; recommendation system; biased intermediation; revenue sharing; data privacy; game theory (search for similar items in EconPapers)
Date: 2024
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