A Human-Machine Collaborative Model for Improving Administrative Efficiency in Wenzhou Universities in China

Abstract

As digital transformation continues to advance within local universities, the enhancement of administrative efficiency has emerged as a critical concern for institutional development. Human-machine collaboration (HMC) has been identified as a promising approach to addressing this challenge in the governance of higher education. It is against this backdrop that the present study examines the relationship between HMC and administrative efficiency, and proposes a localized governance model for universities in Wenzhou, China. A quantitative cross-sectional research design was employed, utilizing survey data collected from 280 administrative staff across 11 universities in Wenzhou. The study applied descriptive statistics, Pearson correlation, regression analysis, and the Priority Needs Index Modified (PNI Modified) to identify key areas for improvement and construct a hierarchical model of efficiency enhancement. The findings indicate that HMC is significantly associated with administrative efficiency (r = 0.680, p < 0.001), and regression analysis further confirms a strong predictive relationship (β = 0.680, p < 0.001). Priority analysis identifies three critical areas requiring improvement: smart task recognition, automated workload distribution, and real-time system adjustment. These priorities reflect a progressive mechanism linking process optimization to performance outcomes. Based on these findings, the study proposes the "AEGIS–Wenzhou Model," which offers a structured and context-sensitive framework for improving administrative efficiency. The study contributes to socio-technical systems theory by elucidating the role of adaptive integration in HMC, and offers practical implications for advancing digital governance in local higher education institutions.