Pathways to effective network governance: A fuzzy-set QCA study of tripartite collaboration efficiency with Chinese official Weibo data - Yuanzhuo Wu; Yifen Xia (PLoS ONE, 2025)
In response to the insufficient research on multiagent collaboration mechanisms in existing network governance studies, this paper identifies the key influencing factors of the tripartite collaboration efficiency using the Grey Relational Analysis (GRA) method, and employs Fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal the collaborative pathways of multiple factors. The 2022 Government Affairs Index Weibo Influence Report issued by the Chinese government is used as the primary data source, selecting five dimensions: platform support, public participation, service level, response capacity, and social influence. Grey Relational Analysis (GRA) is then applied to verify the correlation between these five dimensions and the tripartite collaboration efficiency in government-platform-public interaction. The study finds that: (1) A positive social influence is a necessary condition for achieving high efficiency tripartite collaboration. A lack of interaction with netizens will result in low efficiency in government management of online public opinion. (2) If the government maintains proactive response capabilities, engages in high interaction with stakeholders such as netizens and the media, and improves the service level of both software and hardware in cyberspace, this represents the optimal combination pathway for enhancing tripartite collaboration efficiency. (3) If the government fails to focus on public satisfaction and social influence, even if local governments increase investments in software and hardware and improve service levels, effective management outcomes will not be achieved. This study’s innovation lies in the combined use of GRA and fsQCA to objectively identify the pathways for improving government-platform-public collaboration, providing scientific evidence for enhancing the efficiency of collaborative governance in online public opinion.