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E-Government 3.0: An AI Model to Use for Enhanced Local Democracies

Author

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  • Catalin Vrabie

    (Faculty of Public Administration, National University of Political Studies and Public Administration, 012244 Bucharest, Romania)

Abstract

While e-government (referring here to the first generation of e-government) was just the simple manner of delivering public services via electronic means, e-gov 2.0 refers to the use of social media and Web 2.0 technologies in government operations and public service delivery. However, the use of the term ‘e-government 2.0’ is becoming less common as the focus shifts towards broader digital transformation initiatives that may include AI technologies, among others, such as blockchain, virtual reality, and augmented reality. In this study, we present the relatively new concept of e-government 3.0, which is built upon the principles of e-government 2.0 but refers to the use of emerging technologies (e.g., artificial intelligence) to transform the delivery of public services and improve governance. The study objective is to explore the potential of e-government 3.0 to enhance citizen participation, improve public service delivery, and increase responsiveness and compliance of administrative systems in relation to citizens by integrating emerging technologies into government operations using as a background the evolution of e-government over time. The paper analyzes the challenges faced by municipalities in responding to citizen petitions, which are a core application of local democracies. The author starts by presenting an example of an e-petition system (as in use today) and analyses anonymized data of a text corpus of petitions directed to one of the Romania municipalities. He will propose an AI model able to deal faster and more accurately with the increased number of inputs, trying to promote it to municipalities who, for some reason, are still reluctant to implement AI in their operations. The conclusions will suggest that it may be more effective to focus on improving new algorithms rather than solely on ‘old’ technologies.

Suggested Citation

  • Catalin Vrabie, 2023. "E-Government 3.0: An AI Model to Use for Enhanced Local Democracies," Sustainability, MDPI, vol. 15(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9572-:d:1171004
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    References listed on IDEAS

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    1. Vrabie, Cătălin, 2011. "Digital Governance (in Romanian Municipalities). A Longitudinal Assessment of Municipal Web Sites in Romania," Apas Papers 555, Academic Public Administration Studies Archive - APAS.
    2. Basma Hamrouni & Abdelhabib Bourouis & Ahmed Korichi & Mohsen Brahmi, 2021. "Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability," Sustainability, MDPI, vol. 13(17), pages 1-28, September.
    3. Sainan Zhao & Yichao Zhang & Huma Iftikhar & Atta Ullah & Jie Mao & Tiantian Wang, 2022. "Dynamic Influence of Digital and Technological Advancement on Sustainable Economic Growth in Belt and Road Initiative (BRI) Countries," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    4. Haibo Yu & Guojun Lu & Qianhua Cai & Yun Xue, 2022. "A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
    5. Sorrell, Steve, 2009. "Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency," Energy Policy, Elsevier, vol. 37(4), pages 1456-1469, April.
    6. Suman Kumari & Basant Agarwal & Mamta Mittal, 2021. "A Deep Neural Network Model for Cross-Domain Sentiment Analysis," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(2), pages 1-16, April.
    7. Ibrahim Abaker Targio Hashem & Raja Sher Afgun Usmani & Mubarak S. Almutairi & Ashraf Osman Ibrahim & Abubakar Zakari & Faiz Alotaibi & Saadat Mehmood Alhashmi & Haruna Chiroma, 2023. "Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges," Sustainability, MDPI, vol. 15(5), pages 1-32, February.
    8. Zihao Lu & Xiaohui Hu & Yun Xue, 2022. "Dual-Word Embedding Model Considering Syntactic Information for Cross-Domain Sentiment Classification," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    9. Vuyani Goodman LANGA & Reckson Dovhani THAKHATHI, 2022. "The role of smart cities to promote smart governance in municipalities," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 6(2), pages 9-22, April.
    10. Yu-Che Chen & Michael J. Ahn & Yi-Fan Wang, 2023. "Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    11. Louise Ellegaard Fich & Silvia Viola & Niclas Scott Bentsen, 2022. "Jevons Paradox: Sustainable Development Goals and Energy Rebound in Complex Economic Systems," Energies, MDPI, vol. 15(16), pages 1-3, August.
    12. Nikola Vangelov, 2023. "Ambient Advertising in Metaverse Smart Cities," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 7(1), pages 43-55, March.
    13. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.
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    Cited by:

    1. Thomas Cantens, 2023. "How will the State think with the assistance of ChatGPT? The case of customs as an example of generative artificial intelligence in public administrations," CERDI Working papers hal-04233370, HAL.

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