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Authors: Simona Fioretto ; Elio Masciari and Enea Napolitano

Affiliation: Department of Electrical and Information Technology Engineering, University of Naples Federico II, Naples, Italy

Keyword(s): Public Administration, Key Performance Indicators, Variable Importance, Machine Learning.

Abstract: Efficient and effective service delivery to citizens in Public Administrations (PA) requires the use of key performance indicators (KPIs) for performance evaluation and measurement. This paper proposes an innovative framework for constructing KPIs in performance evaluation systems using Random Forest and variable importance analysis. Our approach aims to identify the variables that have a strong impact on the performance of PAs. This identification enables a deeper understanding of the factors that are critical for organizational performance. By analyzing the importance of variables and consulting domain experts, relevant KPIs can be developed. This ensures improvement strategies focus on critical aspects linked to performance. The framework provides a continuous monitoring flow for KPIs and a set of phases for adapting KPIs in response to changing administrative dynamics. The objective of this study is to enhance the performance of PAs by applying machine learning techniques to achi eve a more agile and results-oriented PAs. (More)

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Paper citation in several formats:
Fioretto, S.; Masciari, E. and Napolitano, E. (2024). Machine Learning for KPI Development in Public Administration. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-707-8; ISSN 2184-285X, SciTePress, pages 522-527. DOI: 10.5220/0012820300003756

@conference{data24,
author={Simona Fioretto. and Elio Masciari. and Enea Napolitano.},
title={Machine Learning for KPI Development in Public Administration},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA},
year={2024},
pages={522-527},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012820300003756},
isbn={978-989-758-707-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - DATA
TI - Machine Learning for KPI Development in Public Administration
SN - 978-989-758-707-8
IS - 2184-285X
AU - Fioretto, S.
AU - Masciari, E.
AU - Napolitano, E.
PY - 2024
SP - 522
EP - 527
DO - 10.5220/0012820300003756
PB - SciTePress

- Science and Technology Publications, Lda.
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