A Brief Discussion on KPI Development in Public Administration
This addresses the need for more effective performance evaluation in public administration, though it appears incremental by applying existing machine learning techniques to this domain.
The paper tackled the problem of developing key performance indicators (KPIs) for public administration by proposing a framework that uses Random Forest algorithms and variable importance analysis to identify critical factors influencing performance, resulting in a systematic method for creating and refining KPIs to enhance service delivery.
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.