Algorithmic optimization of management processes in the public sector based on artificial intelligence technologies for increasing the efficiency of digital governance

Mazur, Hennadii та Medvytskyi, Oleh та Kharytonov, Оleksii та Babichev, Аnatoliy та Marukhlenko, Oksana (2026) Algorithmic optimization of management processes in the public sector based on artificial intelligence technologies for increasing the efficiency of digital governance Journal of Theoretical and Applied Information Technology, 104 (11). ISSN 1817-3195

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Анотація

The digitalization of public administration and the integration of artificial intelligence (AI) technologies necessitate algorithmic optimization of administrative processes and evidence-based assessment of their effectiveness in digital governance systems.The aim of the study was to empirically verify the model of algorithmic optimization of management processes in digital governance. The research methodology combined process mining of administrative event logs, digital twin modelling, quasi-experimental causal identification (DiD, ITS, PSM), and statistical verification of results. This analytical scheme provided an assessment of the effects of algorithmic optimization of management processes in digital governance. Analysis of ≈50–150 thousand administrative events (24–36 months; 6–9 departments) revealed process asymmetry: the upper quartile of TAT exceeded the median by 2.1–2.6 times, and 18–27% of cases generated more than 50% of delays. Quasi-experimental evaluation (≈300–500 panel observations) recorded the effect of algorithmic optimization: TAT ↓9–14%, SLA-breach ↓11–18%, rework ↓6–10%; robustness confirmed by bootstrap (B=1000–5000), FDR=0.05, Durbin–Watson≈2, VIF<5. The study was the first to integrate process mining, digital process twinning, and causal ML in a single empirical design analysing ≈50–150 thousand administrative events, providing quantitative verification of the effects of algorithmic optimization in digital governance. Further research should focus on longer panel designs (≥48–60 months) and experimental AI interventions to test the scalability of algorithmic optimization and assess its impact on the efficiency and quality of digital governance.

Тип елементу :	Стаття
Ключові слова:	Algorithmic Optimization; Digital Governance; Artificial Intelligence; Public Administration; Process Analytics; Digital Twin Of The Process; Quasi-Experimental Analysis
Типологія:	Статті у базах даних > Scopus > У виданнях Q4 Scopus
Підрозділи:	Факультет економіки та управління > Кафедра управління
Користувач, що депонує:	доцент Оксана В'ячеславівна Марухленко
Дата внесення:	17 Серп 2026 08:36
Останні зміни:	17 Серп 2026 08:36
URI:	https://elibrary.kubg.edu.ua/id/eprint/59337

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