Network Size and Content Generation on Social Media Platforms

Zaiyan Wei, Mo Xiao, Rong Rong

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1406-1426
Number of pages21
JournalProduction and Operations Management
Volume30
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • network analytics
  • peer effects
  • social media
  • user-generated content

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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