ARTIFICIAL INTELLIGENCE-BASED GOVERNMENT MANAGEMENT TRANSFORMATION IN IMPROVING THE QUALITY OF PUBLIC SERVICES IN THE ERA OF DIGITAL GOVERNANCE
DOI:
https://doi.org/10.69916/jkbti.v4i3.465Keywords:
artificial intelligence, digital governance, government management, public accountability, public serviceAbstract
This study aims to analyze the transformation of government management based on artificial intelligence in improving the quality of public services in the era of digital governance. The study focuses on the use of AI in public-service delivery, administrative decision-making, and bureaucratic efficiency, while also examining the need for accountability and public ethics in its implementation. This research uses a qualitative method with an exploratory-descriptive and conceptual model-building approach. Data were collected through secondary and documentary sources, including recent peer-reviewed journal articles, policy documents, institutional reports, and regulatory materials related to AI governance, digital government, public administration, and public-service innovation. The data were analyzed using thematic analysis to identify key patterns related to AI utilization, organizational readiness, decision-support systems, ethical risks, and accountability mechanisms. The findings show that AI can improve public services through automation, intelligent citizen interaction, complaint classification, document verification, and predictive service delivery. AI also supports bureaucratic efficiency by reducing repetitive administrative tasks, improving data-based decision-making, and strengthening service monitoring. The main contribution of this study is the formulation of an adaptive AI-based government management model consisting of five dimensions: AI-enabled service innovation, data-driven decision support, bureaucratic workflow redesign, human oversight, and ethical-accountable governance. This model emphasizes that AI transformation in government must be supported by institutional capacity, transparent procedures, human supervision, and public-value orientation.
Downloads
References
D. Ribeiro, J. Fonte, and L. Antunes, “Assessing the information security posture of online public services worldwide: Technical insights, trends and policy implications,” Government Information Quarterly, vol. 42, no. 3, Art. no. 102031, 2025, doi: https://doi.org/10.1016/j.giq.2025.102031.
A. Zuiderwijk, Y.-C. Chen, and F. Salem, “Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda,” Government Information Quarterly, vol. 38, no. 3, Art. no. 101577, 2021, doi: https://doi.org/10.1016/j.giq.2021.101577.
C. van Noordt and G. Misuraca, “Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union,” Government Information Quarterly, vol. 39, no. 3, Art. no. 101714, 2022, doi: https://doi.org/10.1016/j.giq.2022.101714.
M. J. Ahn and Y.-C. Chen, “Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government,” Government Information Quarterly, vol. 39, no. 2, Art. no. 101664, 2022, doi: https://doi.org/10.1016/j.giq.2021.101664.
L. Tangi, A. P. R. Müller, and M. Janssen, “AI-augmented government transformation: Organisational transformation and the sociotechnical implications of artificial intelligence in public administrations,” Government Information Quarterly, vol. 42, no. 3, Art. no. 102055, 2025, doi: https://doi.org/10.1016/j.giq.2025.102055.
M. Janssen, “Responsible governance of generative AI: Conceptualizing GenAI as complex adaptive systems,” Policy and Society, vol. 44, no. 1, pp. 38–51, 2025, doi: https://doi.org/10.1093/polsoc/puae040.
T. Chen, W. Guo, X. Gao, and Z. Liang, “AI-based self-service technology in public service delivery: User experience and influencing factors,” Government Information Quarterly, vol. 38, no. 4, Art. no. 101520, 2021, doi: https://doi.org/10.1016/j.giq.2020.101520.
T. S. Gesk and M. Leyer, “Artificial intelligence in public services: When and why citizens accept its usage,” Government Information Quarterly, vol. 39, no. 3, Art. no. 101704, 2022, doi: https://doi.org/10.1016/j.giq.2022.101704.
J. Ju, Q. Meng, F. Sun, L. Liu, and S. Singh, “Citizen preferences and government chatbot social characteristics: Evidence from a discrete choice experiment,” Government Information Quarterly, vol. 40, no. 3, Art. no. 101785, 2023, doi: https://doi.org/10.1016/j.giq.2022.101785.
C. Wanckel, “An ounce of prevention is worth a pound of cure—Building capacities for the use of big data algorithm systems (BDAS) in early crisis detection,” Government Information Quarterly, vol. 39, no. 4, Art. no. 101705, 2022, doi: https://doi.org/10.1016/j.giq.2022.101705.
D. Valle-Cruz, V. Fernandez-Cortez, and J. R. Gil-Garcia, “From e-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation,” Government Information Quarterly, vol. 39, no. 2, Art. no. 101644, 2022, doi: https://doi.org/10.1016/j.giq.2021.101644.
J. R. Saura, D. Ribeiro-Soriano, and D. Palacios-Marqués, “Assessing behavioral data science privacy issues in government artificial intelligence deployment,” Government Information Quarterly, vol. 39, no. 4, Art. no. 101679, 2022, doi: https://doi.org/10.1016/j.giq.2022.101679.
H. de Bruijn, M. Warnier, and M. Janssen, “The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making,” Government Information Quarterly, vol. 39, no. 2, Art. no. 101666, 2022, doi: https://doi.org/10.1016/j.giq.2021.101666.
S. Grimmelikhuijsen, “Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making,” Public Administration Review, vol. 83, no. 2, pp. 241–262, 2023, doi: https://doi.org/10.1111/puar.13483.
N. Aoki, T. Tatsumi, G. Naruse, and K. Maeda, “Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected?” Government Information Quarterly, vol. 41, no. 4, Art. no. 101965, 2024, doi: https://doi.org/10.1016/j.giq.2024.101965.
G. Wang, Y. Guo, W. Zhang, S. Xie, and Q. Chen, “What type of algorithm is perceived as fairer and more acceptable? A comparative analysis of rule-driven versus data-driven algorithmic decision-making in public affairs,” Government Information Quarterly, vol. 40, no. 2, Art. no. 101803, 2023, doi: https://doi.org/10.1016/j.giq.2023.101803.
A. Decuypere and A. Van de Vijver, “AI: Friend or foe of fairness perceptions of the tax administration? A survey experiment on citizens’ procedural fairness perceptions,” Government Information Quarterly, vol. 42, no. 1, Art. no. 102002, 2025, doi: https://doi.org/10.1016/j.giq.2024.102002.
R. Madan and M. Ashok, “AI adoption and diffusion in public administration: A systematic literature review and future research agenda,” Government Information Quarterly, vol. 40, no. 1, Art. no. 101774, 2023, doi: https://doi.org/10.1016/j.giq.2022.101774.
G. Maragno, L. Tangi, L. Gastaldi, and M. Benedetti, “Exploring the factors, affordances and constraints outlining the implementation of artificial intelligence in public sector organizations,” International Journal of Information Management, vol. 73, Art. no. 102686, 2023, doi: https://doi.org/10.1016/j.ijinfomgt.2023.102686.
P. G. R. de Almeida and C. D. dos Santos Jr., “Artificial intelligence governance: Understanding how public organizations implement it,” Government Information Quarterly, vol. 42, no. 1, Art. no. 102003, 2025, doi: https://doi.org/10.1016/j.giq.2024.102003.
C. Wilson and M. van der Velden, “Sustainable AI: An integrated model to guide public sector decision-making,” Technology in Society, vol. 68, Art. no. 101926, 2022, doi: https://doi.org/10.1016/j.techsoc.2022.101926.
T. Yigitcanlar, J. M. Corchado, R. Mehmood, R. Y. M. Li, K. Mossberger, and K. Desouza, “Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 7, no. 1, Art. no. 71, 2021, doi: https://doi.org/10.3390/joitmc7010071.
T. Yigitcanlar, D. Agdas, and K. Degirmenci, “Artificial intelligence in local governments: Perceptions of city managers on prospects, constraints and choices,” AI & Society, vol. 38, pp. 1135–1150, 2023, doi: https://doi.org/10.1007/s00146-022-01450-x.
T. Yigitcanlar, R. Y. M. Li, T. Inkinen, and A. Paz, “Public perceptions on application areas and adoption challenges of AI in urban services,” Emerging Science Journal, vol. 6, no. 6, pp. 1199–1236, 2022, doi: https://doi.org/10.28991/ESJ-2022-06-06-01.
T. Yigitcanlar, R. Y. M. Li, P. B. Beeramoole, and A. Paz, “Artificial intelligence in local government services: Public perceptions from Australia and Hong Kong,” Government Information Quarterly, vol. 40, no. 3, Art. no. 101833, 2023, doi: https://doi.org/10.1016/j.giq.2023.101833.
A. Alshahrani, A. Griva, D. Dennehy, and M. Mäntymäki, “Artificial intelligence and decision-making in government functions: Opportunities, challenges and future research,” Transforming Government: People, Process and Policy, vol. 18, no. 4, pp. 678–698, 2024, doi: https://doi.org/10.1108/TG-06-2024-0131.
R. Sandoval-Almazan, A. O. Millan-Vargas, and R. Garcia-Contreras, “Examining public managers’ competencies of artificial intelligence implementation in local government: A quantitative study,” Government Information Quarterly, vol. 41, no. 4, Art. no. 101986, 2024, doi: https://doi.org/10.1016/j.giq.2024.101986.
D. Valle-Cruz, R. García-Contreras, and J. R. Gil-Garcia, “Exploring the negative impacts of artificial intelligence in government: The dark side of intelligent algorithms and cognitive machines,” International Review of Administrative Sciences, vol. 90, no. 2, pp. 353–368, 2024, doi: https://doi.org/10.1177/00208523231187051.
S. Hong, S. H. Kim, and M. Kwon, “Determinants of digital innovation in the public sector,” Government Information Quarterly, vol. 39, no. 4, Art. no. 101723, 2022, doi: https://doi.org/10.1016/j.giq.2022.101723.
S. R. Chohan and G. Hu, “Strengthening digital inclusion through e-government: Cohesive ICT training programs to intensify digital competency,” Information Technology for Development, vol. 28, no. 1, pp. 16–38, 2022, doi: https://doi.org/10.1080/02681102.2020.1841713.
C. van Noordt and L. Tangi, “The dynamics of AI capability and its influence on public value creation of AI within public administration,” Government Information Quarterly, vol. 40, no. 4, Art. no. 101860, 2023, doi: https://doi.org/10.1016/j.giq.2023.101860.
Downloads
Published
Scite Metrics
Altmetric
How to Cite
Issue
Section
License
Copyright (c) 2025 Lalu Ahmad Murdhani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Lalu Ahmad Murdhani, BIG DATA AND ARTIFICIAL INTELLIGENCE IN LOCAL GOVERNMENT DISASTER RISK MANAGEMENT: TOWARD RESPONSIVE AND ADAPTIVE GOVERNANCE , Jurnal Kecerdasan Buatan dan Teknologi Informasi: Vol. 5 No. 1 (2026): January 2026
menuterbaru
 |
 |
 |
 |
 |
 |
 |
 |
 |
|
|
 |
 |
 |
 |
 |
 |
 |
 |
| More Indexing and Abstracting |
|
|
View My Stats |
| Overall Visitor Statistics |
 |
