Consequences of Information Feed Integration on User Engagement and Contribution: A Natural Experiment in an Online Knowledge-Sharing Community

心态 用户生成的内容 计算机科学 知识共享 社会化媒体 知识管理 万维网 信息共享 人工智能
作者
Zhenjun Cao,Yichang Zhu,G. X. Li,Liangfei Qiu
出处
期刊:Information Systems Research [Institute for Operations Research and the Management Sciences]
被引量:1
标识
DOI:10.1287/isre.2022.0043
摘要

This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor’s professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions.
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