From Advocacy to Surveillance Stakeholder Tensions in Social Media Data Governance at a Chinese University
Keywords:
social media data; university governance; stakeholder perspectives; surveillance; emotional labor; qualitative researchAbstract
Universities increasingly use social media data to identify student concerns and inform management, but the same practices can also intensify surveillance, emotional labor, and distrust. This qualitative study examined how students, teachers, and administrative staff interpret social media data governance at Yunnan University of Finance and Economics in Southwest China. Fifteen participants were purposively selected, with five from each stakeholder group. Mandarin interviews lasting 45–90 minutes were transcribed verbatim and analyzed in NVivo 15 using a six-step thematic procedure. The analysis produced 95 coded references organized into four themes, ten subthemes, and nineteen initial concepts. Four linked tensions were identified. Students used public posting as leverage when formal channels appeared ineffective, whereas administrators treated the same platforms as risk signals requiring continuous monitoring. Always-on monitoring weakened work–life boundaries and created unrecognized emotional labor for teachers and frontline staff. Unequal data literacy separated a small group who could convert criticism into evidence from others who responded through avoidance or defensive compliance. Privacy awareness encouraged students to move authentic expression into anonymous or less visible spaces, reducing the representativeness of the data available to managers. These mechanisms culminated in a contest between passive crisis control and institutionalized co-governance. Inter-coder agreement was strong (overall Cohen’s κ = .823), and code saturation was approached by the eleventh interview. The findings show that management value depends less on data volume than on the governance arrangements through which data are interpreted, contested, and acted upon. Universities should separate support-oriented analytics from disciplinary monitoring, protect staff boundaries, validate social media signals with other evidence, and establish transparent student participation procedures.