A Brief Discussion on KPI Development in Public Administration
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910742811246592 |
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| author | Fioretto, Simona Masciari, Elio Napolitano, Enea Vincenzo |
| author_facet | Fioretto, Simona Masciari, Elio Napolitano, Enea Vincenzo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09142 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Brief Discussion on KPI Development in Public Administration Fioretto, Simona Masciari, Elio Napolitano, Enea Vincenzo Machine Learning 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. |
| title | A Brief Discussion on KPI Development in Public Administration |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.09142 |