Network Size and Content Generation on Social Media Platforms

• 成果类型: Article
• 署名作者: Wei, Zaiyan; Xiao, Mo; Rong, Rong
• 署名单位: Purdue University System; Purdue University; University of Arizona; University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13328
• 发表日期: 2021
• 页码: 1406-1426
• 关键词: social media; user‐ generated content; peer effects; network analytics

摘要

Social media has been increasingly integrated into firm operations. Past literature documented the operational value of the content generated by social media users but paid little attention to the users' incentives to generate and share content. We fill in the gap by linking a user's social network to her content contribution. Specifically, we distinguish the role of the followee network (the group of people being followed by the user) from the role of the follower network. When a user follows more people, she may spend more time in consuming content than generating content (the substitution effect); she may gain more conflicting information from her followees, obfuscating her incentives to generate content (the information overload effect). Conversely, gathering more information from her followees may facilitate her own content generation (the information sharing effect). Through different identification strategies using multiple datasets from two influential social media platforms, we find that the effects of followees and followers are asymmetric in signs and different in magnitudes. Most notably, a user generates less content with a larger followee network, especially when she faces more time constraints. Our findings suggest social media platforms and companies leveraging social media in their operations incorporate network analytics to promote their user engagement.