Fioretto, Simona (2025) A Process-Aware Perspective on the Challenges and Solutions of Public Administration Digital Transformation. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | A Process-Aware Perspective on the Challenges and Solutions of Public Administration Digital Transformation |
| Autori: | Autore Email Fioretto, Simona simona.fioretto@unina.it |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 186 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 38 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano stefano.russo@unina.it |
| Tutor: | nome email Masciari, Elio [non definito] |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 186 |
| Parole chiave: | Digital transformation, public administration, business process management, process mining, predictive process monitoring. |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
| Depositato il: | 10 Dic 2025 22:48 |
| Ultima modifica: | 02 Set 2026 08:04 |
| URI: | https://www.fedoa.unina.it/id/eprint/15964 |
Abstract
The European Union (EU) is paying increasing attention to new technologies, particularly the Digital Transformation (DT) of its member states. Yet Public Administrations (PAs) struggle to meet expectations and keep pace with technological change. To track progress, the EU monitors DT through initiatives such as the State of the Digital Decade report. Despite policy efforts and investments, many PAs still face fragmented data, out-dated procedures, and weak integration between processes and Information Technology (IT) systems, resulting in inefficiencies, reduced transparency, and limited citizen-centricity in public services. This dissertation is motivated by the persistent gap between the strategic objectives of DT and the operational challenges faced by PAs. To address this, three complementary approaches are investigated. First, Business Process Management (BPM) was applied in an on-site analysis of judicial offices, which revealed fragmented data sources, the lack of a centralized Information System (IS), and missing end-to-end process visibility, showing the value of aligning process modeling with IT implementation. Second, a Systematic Literature Review (SLR) synthesized the state of the art in Predictive Process Monitoring (PPM), highlighting the challenges and opportunities of transitioning from classical to Object Centric Event Logs (OCELs). Third, experimental applications of Process Mining (PM) and PPM to the Land Matrix dataset on large-scale land acquisitions showed that clustering and advanced encoding strategies Artificial Intelligence (AI)-based can reduce process complexity and improve predictive performance, even in irregular and semi-structured domains. Together, these contributions provide a methodological framework to support the DT of PAs by combining top-down process modeling and redesign with bottom-up, data-driven insights.
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