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Abstract
This study aims to assess how Indonesia's Government Office of Kediri District responds to integrating Artificial Intelligence (AI) in improving service frequency and public satisfaction. An explanatory quantitative approach was adopted to investigate the causal relationship between AI adoption and the two dependent variables—service frequency and public satisfaction—through multiple linear regression analysis, utilizing data from a sample of 15 districts between 2018 and 2023. The study reveals that AI adoption significantly enhances public satisfaction and service frequency. Implementing AI, facilitated through the Digital Service Living Lab (DSLL) platform, increases efficiency, responsiveness, and service quality. The study underscores that AI integration can boost public service effectiveness and efficiency, encouraging local governments to broaden AI usage and invest in staff training and capacity development. This research offers strategic recommendations for governments to expand AI adoption further, strengthen inter-district collaboration, and optimize the use of Electronic Government (E-Government) initiatives to support the goals of smart city and provincial development.
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References
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- https://doi.org/10.1145/3543434.3543450
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References
Almazan, R., Faura, J., & Vargas, A. (2023). Defining A User Profile for E-Government Services: The Diffusion of Innovation Perspective.Journal Information Development,2(2),26-69. https://doi.org/10.1177/02666669231218213
Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2018). Transforming the Communication Between Citizens and Government Through AI-guided chatbots. Government Information Quarterly, 36(2), 358–367. https://doi.org/10.1016/j.giq.2018.10.001
Apoorva, I., Siri, V., & Ramesh. (2019). Automated Criminal Identification By Face Recognition Using Open Computer Vision Classifiers.Journal of Computer Science, 1(1), 775–778. https://doi.org/10.1109/ICCMC.2019.8819850
Chen, T., Hernandez, M., & Marc, E. (2023). The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from US State Governments. Journal American Review of Public Administration, 54(3), 255–270, https://doi.org/10.1177/02750740231200522.
Cruz, D. (2019). Public Value of E-Government Services Through Emerging Technologies. International Journal of Public Sector Management, 32(2),10–20. https://doi.org/10.1108/IJPSM-03-2018-0072
Davis, F. (1989). Perceived Usefulness, Ease of Use, and User Acceptance of Information Technology. MIS Quarterly,13(3),319–340.
Goralski, M., & Tan, T. (2020). Artificial Intelligence and Sustainable Development. The International Journal of Management Education, 18(1),20–30. https://doi.org/10.1016/j.ijme.2019.100330.
Hu, Y. C. (2021). Forecasting the Demand for Tourism Using Combinations of Forecasts by Neural Network-Based Interval Grey Prediction Models. Asia Pacific of Tourism Research, 26(12),1350–1363, https://doi.org/ 10.1080/10941665.2021.1983623.
Humans. (2023, July 12). Humas Jabar. Retrieved from Penerapan Platform Digital Service: https://jabarprov.go.id/berita/delapan-kabupaten-kota-di-jawa-barat-sepakat-terapkan-platform-digital-service-living-lab-9649
Iftikhar, P., Kujipers, M., Khayyat, A., Iftikhar, A., & Sa, M. (2020). Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice. Journal of National Health, 12(2).30-40. https://doi.org/10.7759/cureus.7124
Ku, C., Iriberri, A., & Leroy, G. (2008). Natural Language Processing and E-Government: Crime Information Extraction from Heterogeneous Data Sources. International Conference on Digital Government Research, 1(1),162-170. https://doi.org/10.5555/1367832.1367862
Kuberkar, S., & Singhai, T. (2020). Factors Influencing Adoption Intention of Al Powered Chatbot for Public Transport Services Within a Smart City. International Journal on Emerging Technologies,11(3), 948-958.
Mushayt, O. (2019). Automating E-Government Services With Artificial Intelligence. Journal of Government, 7(1),1468211–146829. https://doi.org/10.1109/ACCESS.2019.2946204
Oliver, R. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions.Journal of Marketing Research, 17(4),460–469. https://doi.org/10.2307/3150499
Parasuraman, P., Zeithaml, V., & Berry, L. (1985). A Conceptual Model of Service Quality and Its Implication for Future Research. Journal of Marketing, 49(4),41–50. https://doi.org/10.2307/1251430
Reyes, L., & Harrison, T. (2022). A Systems View of Enterprise Data Governance for Artificial Intelligence Applications in Government. Proceedings of the 23rd Annual International Conference on Digital Government Research, 2(2),206–213.
https://doi.org/10.1145/3543434.3543450
Savaget, P., Chiarini, T., & Evans, S. (2018). Empowering Political Participation Through Artificial Intelligence. Journal Science Public of Policy, 46(3), 369-380. https://doi.org/10.1093/scipol/scy064
Seliger, G., Carpinetti, L., Gerolamo, M., & Balerezo, T. (2008). Promoting Innovative Clusters and Cooperation Networks: The European Commission Observatories of SMEs and The Context of Berlin-Brandenburg. International Journal of Networking and Virtual Organisations, 5(2),204-223. https://doi.org/10.1504/IJNVO.2008.017011.
Sha, K., Taeihagh, A., & Jong, M. (2023). Governing Disruptive Technologies for Inclusive Development in Cities: A Systematic Literature Review. Technological Forecasting and Social Change, 203(3),1-15. https://doi.org/10.1016/j.techfore.2024.123382
Uzun, M. (2020). Artificial Intelligence and State Economic Security. Eurasian Economic Perspective, 15(1), 40-45. https://doi.org/10.1016/j.techfore.2024.123382.
Zhang, W., Zuo, N., He, W., Li, S., & Yu, L. (2021). Factors Influencing the Use of Artificial Intelligence in Government: Evidence From China. Journal of Technology in Society,66(2) 1-16, https://doi.org/10.1016/j.techsoc.2021.101675