Summary of A Brief Discussion on Kpi Development in Public Administration, by Simona Fioretto et al.
A Brief Discussion on KPI Development in Public Administration
by Simona Fioretto, Elio Masciari, Enea Vincenzo Napolitano
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for constructing key performance indicators (KPIs) in Public Administration (PA) is proposed, utilizing Random Forest algorithms and variable importance analysis. The framework identifies critical variables influencing PA performance, providing valuable insights into organizational success factors. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed to address performance-critical areas. This study applies machine learning techniques to enhance PA performance, fostering a more agile and results-driven approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents an innovative way to create important metrics for evaluating how well government organizations work. The method uses special computer algorithms and analyzes which factors are most important in making these organizations successful. By combining this analysis with what experts think is important, the framework can develop useful metrics that help organizations improve. This research uses machine learning to make government services more efficient and effective. |
Keywords
» Artificial intelligence » Machine learning » Random forest
