Against the backdrop of digital transformation, this study aims to examine how governments process massive social information to sustain governance resilience in noncompetitive electoral systems. Focusing on China’s unique sheqing minyi xinxi (SMX) system, it addresses the theoretical gap in understanding the operational logic of institutionalized feedback channels where direct voting signals are absent.
A mixed methods design is adopted. First, a scientometric analysis via CiteSpace of 232 relevant documents maps the macro-evolution of state absorption priorities. Then, qualitative analysis is conducted on 83 selected core documents to unpack the micro-political logic underlying these patterns.
The SMX system functions as a sophisticated dual-filtering system: a technical filter leverages digital technologies to reduce information noise, while a political filter strategically modifies sensitive signals based on a blame-avoidance logic.
By integrating information processing theory and administrative absorption theory, this study proposes the dual-filter model to elucidate the operational logic of institutionalized absorption. Theoretically, it improves existing information filtration models by replacing cognitive limits with bureaucratic blame-avoidance logic in noncompetitive regimes. Practically, it offers an actionable five-in-one optimization framework for public administrators to design agile, digitally empowered feedback channels. Socially, the findings underscore the critical need to mitigate datafication bias to accurately capture marginalized citizen voices, thereby fostering state-society trust and resilient governance.
