用随机森林分析关键变量，帮公共部门科学设定绩效指标。
A Brief Discussion on KPI Development in Public Administration
- 结合随机森林与变量重要性分析，找出影响行政绩效的关键因素。
- 通过专家咨询与动态调整机制，系统化生成可优化的绩效指标。
- 适合关注公共管理智能化、绩效评估现代化的研究者与实践者。
公共行政（PA）高效服务的实现依赖于关键绩效指标（KPI）的构建与应用。本文提出一种创新框架，利用随机森林算法与变量重要性分析，在绩效评估体系中识别显著影响行政绩效的关键变量，为组织成功的核心驱动因素提供洞察。通过将变量重要性分析与专家咨询相结合，可系统性地开发相关KPI，确保改进策略聚焦于绩效关键领域。该框架还包含持续监测机制与自适应阶段，可根据行政需求变化动态优化KPI。本研究旨在通过机器学习技术提升公共行政绩效，推动更加敏捷、结果导向的治理模式。
原文摘要 · Abstract (English)
Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.
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