Improving Czech Digital Government Based on Quantified Maturity Model of Enterprise Architecture

0574406 - ÚTIA 2024 RIV PT eng C - Konferenční příspěvek (zahraniční konf.)
Rod, M. - Vomlel, Jiří
Improving Czech Digital Government Based on Quantified Maturity Model of Enterprise Architecture.
Proceedings of the 25th International Conference on Enterprise Information Systems. Setúbal: SCITEPRESS, 2023 - (Filipe, J.; Śmiałek, M.; Brodsky, A.; Hammoudi, S.), s. 600-607. ISBN 978-989-758-648-4. ISSN 2184-4992.
[International Conference on Enterprise Information Systems 2023 /25./. Praha (CZ), 24.04.2023-26.04.2023]
Grant ostatní: Internal Grant Agency of the Prague University of Economics and Business(CZ) IGS F6/61/2020
Institucionální podpora: RVO:67985556
Klíčová slova: Enterprise architecture * Bayesian networks * Maturity model * E-Government * Digital Government
Obor OECD: Automation and control systems
http://library.utia.cas.cz/separaty/2023/MTR/vomlel-0574406.pdf

One of the current drivers for transitioning from the traditional E-Government to the digital government is the ability to create and share new services in the governmental ICT landscape. The government must effectively communicate and offer its services to itself (G2G) and outside, be it an end-consumer or business (G2C, G2B). Since the government is internally divided, there is a need to measure its parts' performance for effective management. However, conventional maturity models cannot address and explain the cause of the differences, and thus typically respond to symptoms and show just winners and losers of the given benchmark. From this position, a study and a deeper analysis of the maturity model used in the public administration of Czechia are provided. Further analysis was undertaken via Bayesian networks to answer the question: How do project management and prioritization affect service level management? Or how the enterprise architecture as a method is linked to the overall organization's performance? Significant relationships were identified, and the use of the Bayesian network as a prediction model was proposed. Further evaluation steps and research opportunities were discussed.
Trvalý link: https://hdl.handle.net/11104/0344806