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مومنی, امیررضا, یعقوبی, نورمحمد, روشن, سیدعلیقلی, پورعزت, علی اصغر. (1405). طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند با استفاده از رویکرد فراترکیب. , 18(1), 263-295. doi: 10.22059/jipa.2025.398124.3737
امیررضا مومنی; نورمحمد یعقوبی; سیدعلیقلی روشن; علی اصغر پورعزت. "طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند با استفاده از رویکرد فراترکیب". , 18, 1, 1405, 263-295. doi: 10.22059/jipa.2025.398124.3737
مومنی, امیررضا, یعقوبی, نورمحمد, روشن, سیدعلیقلی, پورعزت, علی اصغر. (1405). 'طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند با استفاده از رویکرد فراترکیب', , 18(1), pp. 263-295. doi: 10.22059/jipa.2025.398124.3737
مومنی, امیررضا, یعقوبی, نورمحمد, روشن, سیدعلیقلی, پورعزت, علی اصغر. طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند با استفاده از رویکرد فراترکیب. , 1405; 18(1): 263-295. doi: 10.22059/jipa.2025.398124.3737

طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند با استفاده از رویکرد فراترکیب

مدیریت دولتی
دوره 18، شماره 1، 1405، صفحه 263-295    اصل مقاله (911.65 K)
نوع مقاله: مقاله علمی پژوهشی
شناسه دیجیتال (DOI): 10.22059/jipa.2025.398124.3737
نویسندگان
امیررضا مومنی1؛ نورمحمد یعقوبی* 2؛ سیدعلیقلی روشن3؛ علی اصغر پورعزت4
1دانشجوی دکتری، گروه مدیریت دولتی، دانشکدۀ مدیریت و اقتصاد، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
2استاد، گروه مدیریت دولتی، دانشکدۀ مدیریت و اقتصاد، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
3دانشیار، گروه مدیریت دولتی، دانشکدۀ مدیریت و اقتصاد، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
4استاد، گروه خط‌‌مشی‌گذاری عمومی، دانشکده علوم اداری و سازمانی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران.
چکیده
هدف: یکی از مفاهیمی که در سال‌های اخیر در راستای تحول دولت‌ها و حرکت به‌سوی دولت هوشمند مطرح شده است، مفهوم پذیرش هوش مصنوعی در قالب حکمرانی هوشمند است. شناسایی عوامل مؤثر بر پذیرش هوش مصنوعی در حکمرانی و دولت‌های هوشمند، می‌تواند نقشه‌راهی برای طراحی و اجرای خط‌مشی‌های مؤثر باشد. یکی از اهداف حکمرانی هوشمند، ایجاد رضایت در میان همۀ ذی‌نفعان جامعه است؛ به ویژه، در مواجهه با چالش‌هایی نظیر رشد جمعیت، بحران‌های متعدد و پیچیدگی‌های مدیریتی، نقش محوری ایفا می‌کند. این پژوهش تلاش دارد تا با استفاده از رویکرد فراترکیب، چارچوبی جامع برای پذیرش هوش مصنوعی در حکمرانی هوشمند طراحی کند. این چارچوب با هدف شناسایی، تحلیل و طبقه‌بندی مؤلفه‌ها و عوامل مرتبط با پذیرش فناوری‌های نوین، به حکمرانی کمک می‌کند تا از ظرفیت‌های هوش مصنوعی در همۀ ابعاد طراحی، مدیریتی و اجرایی بهره‌مند شود. همچنین، این مطالعه، در پی آن است که از طریق مرور پژوهش‌های پیشین، راه‌حل‌هایی جامع برای بهره‌برداری بهینه از اطلاعات و داده‌ها ارائه دهد و زمینه را برای پژوهش‌های آتی فراهم کند.
روش: رویکرد پژوهش حاضر کیفی است و برای دستیابی به اهداف پژوهش از روش فراترکیب استفاده شده است. این روش، بر پایه مدل پیاز پژوهش ساندرز و همکاران شکل گرفته که یکی از روش‌های رایج و معتبر در پژوهش‌های کیفی، برای ترکیب سامان‌مند یافته‌های پیشین است. در این پژوهش، به‌منظور بررسی عوامل مرتبط با پذیرش هوش مصنوعی در حکومت، منابع علمی معتبری شامل ۱۱۲۳ مقاله و کتاب از پایگاه‌های دادۀ خارجی، بررسی شده است. منابع بر اساس معیارهای ورود و خروج مشخص و با تمرکز بر اطلاعات و داده‌های مرتبط با حکمرانی هوشمند و عوامل ضروری پذیرش فناوری انتخاب شدند. داده‌های این پژوهش از منابعی استخراج شدند که بین سال‌های ۲۰۱۵ تا ۲۰۲۵ میلادی، در معتبرترین پایگاه‌های علمی منتشر شدند. نتایج حاصل از این تحلیل سامان‌مند، به ارائۀ یک مدل جامع برای پذیرش هوش مصنوعی، بر پایۀ ترکیب یافته‌های کیفی و شناسایی مؤلفه‌های محوری منجر شد.
یافته‌ها: یافته‌ها نشان می‌دهد که چارچوب جامع و یکپارچۀ پذیرش موفق هوش مصنوعی در حکمرانی هوشمند، چهار لایۀ اصلی را دربرمی‌گیرد: ۱. لایۀ اطلاعاتی: زمینۀ فناوری؛ ۲. لایۀ نهادی: زمینۀ سازمانی؛ ۳. لایۀ ارزشی: زمینۀ محیطی؛ ۴. لایۀ کنشی: ظرفیت جذب. مهم‌ترین مؤلفه‌های شناسایی‌شده در این پژوهش عبارت‌اند از: زیرساخت‌های دیجیتال، حکمرانی و امنیت داده و شفافیت الگوریتمی در لایۀ اطلاعاتی؛ فرهنگ سازمانی نوآورانه، رهبری تحول‌گرا و آموزش و توانمندسازی کارکنان در لایۀ نهادی؛ چارچوب‌های قانونی، فشارهای رقابتی و خواسته‌های شهروندان در لایۀ ارزشی و در نهایت، قابلیت‌های پویا و سازوکارهای اشتراک دانش در لایۀ کنشی.
نتیجه‌گیری: پژوهش حاضر با هدف طراحی الگوی پذیرش هوش مصنوعی در حکمرانی هوشمند انجام شد و نتایج آن نشان می‌دهد که پذیرش موفق این فناوری، مستلزم فراهم‌سازی زیرساخت‌های دیجیتالی، تدوین خط‌مشی‌های حمایتی، توسعۀ فرهنگ دیجیتال، تقویت مهارت‌های انسانی و نظارت بر فرایندهای هوش مصنوعی است.
کلیدواژه‌ها
هوش مصنوعی؛ دولت هوشمند؛ حکمرانی‌ هوشمند؛ آینده دولت
عنوان مقاله [English]
The Design of an Artificial Intelligence Adoption Model in Smart Governance Using a Meta-Synthesis Approach
نویسندگان [English]
Amirreza Momeni1؛ Nour Mohammad Yaghoubi2؛ Seyed Aligholi Roshan3؛ Ali Asghar Pourezzat4
1PhD Candidate, Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchistan, Zahedan, Iran.
2Prof., Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchistan, Zahedan, Iran.
3Associate Prof., Department of Public Administration, Faculty of Management and Economics, University of Sistan and Baluchistan, Zahedan, Iran.
4Department of Public Policy, Faculty of Public Administration and Organization Science, College of Management, University of Tehran, Tehran, Iran.
چکیده [English]
Objective
In recent years, a key concept in the transformation toward smart governance has been the adoption of artificial intelligence (AI). Identifying the factors that influence AI acceptance in governance can provide a roadmap for designing and implementing effective policies. Smart governance seeks to foster satisfaction among all societal stakeholders and plays a central role in addressing challenges such as population growth, recurring crises, and managerial complexity. Consequently, this study seeks to develop a comprehensive framework for AI adoption in smart governance using a meta-synthesis approach. The proposed framework aims to identify, analyze, and classify the components and factors associated with the acceptance of emerging technologies, enabling governance systems to fully leverage AI capabilities across design, managerial, and operational dimensions. Furthermore, through a systematic review of prior research, this study endeavors to provide integrated solutions for the optimal utilization of data and information, paving the way for future scholarly investigations.
Methods
This study employed a qualitative approach, utilizing the meta-synthesis method to achieve its research objectives. The process was structured according to Saunders et al.’s “research onion” model, a widely recognized and reliable framework for systematically integrating qualitative findings. To examine the factors related to AI adoption in governance, a comprehensive review of 1,123 scholarly articles and books from international academic databases was conducted. Source selection was based on well-defined inclusion and exclusion criteria, with a particular focus on literature related to smart governance and the critical factors influencing technology adoption. The analyzed works were published between 2015 and 2025 in leading scientific databases. The findings from this systematic analysis were synthesized to develop a comprehensive model for AI adoption, structured through the integration of qualitative results and the identification of key components.
Results
The findings reveal that the successful adoption of artificial intelligence in smart governance requires a comprehensive framework incorporating four fundamental layers. First, the informational layer (technological context) encompasses digital infrastructure, data governance and security, and algorithmic transparency. Second, the institutional layer (organizational context) includes an innovative organizational culture, transformational leadership, and employee training and empowerment. Third, the value layer (environmental context) consists of legal and regulatory frameworks, competitive pressures, and citizens’ demands. Finally, the action layer (absorptive capacity) comprises dynamic capabilities and mechanisms for knowledge sharing. Collectively, these four layers and their associated components establish a holistic foundation through which governments can effectively integrate artificial intelligence into the design, management, and execution of smart governance.
Conclusion
This study aimed to design a model for the adoption of artificial intelligence in smart governance. The findings demonstrate that successful technological integration requires robust digital infrastructure, supportive policies, a mature digital culture, enhanced human skills, and continuous oversight of AI-driven processes. The proposed multi-layered framework offers a strategic roadmap for policymakers and administrators, emphasizing that AI adoption is not merely a technical upgrade but a systemic transformation involving technological, institutional, environmental, and capability-based dimensions. Future research should empirically validate and refine this framework across different governmental contexts.
کلیدواژه‌ها [English]
Artificial intelligence, Smart government, Smart governance, Future of government
مراجع
پورعزت، علی اصغر؛ عباسی، طیبه؛ مقصودی کناری، شهریار و نامدار جویباری، محمد مهدی (1403). بررسی نقش مؤلفه‌‌های اساسی حکمرانی هوشمند در تحقق شهر هوشمند با روش ISM (مطالعه موردی: شهر تهران). مدیریت دولتی، 16(3)، 535-561. 

روشن، سید علیقلی؛ یعقوبی، نورمحمد و مومنی، امیررضا (1400). کاربست هوش مصنوعی در بخش دولتی (مطالعه‌ای فراترکیب). فصلنامه انجمن علوم مدیریت ایران، 16(61)، 117-145.

 

References

Aboelmaged, M. & Mouakket, S. (2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing & Management, 57(4), 102234. https://doi.org/10.1016/j.ipm.2020.102234

Abou-Foul, M., Ruiz-Alba, J. L. & López-Tenorio, P. J. (2023). The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research, 157, 113609. https://doi.org/10.1016/j.jbusres.2022.113609

Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J. & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43, 100452. https://doi.org/10.1016/j.cosrev.2021.100452

Ahn, M. J. & Chen, Y. C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664. https://doi.org/10.1016/j.giq.2021.101664

Ajzen, I. (2012). The theory of planned behavior. In Handbook of Theories of Social Psychology: Volume 1 (Vol. 1, pp. 438-459). SAGE Publications Ltd, https://doi.org/10.4135/9781446249215

Alblooshi, M., Shamsuzzaman, M. and Haridy, S. (2021). The relationship between leadership styles and organisational innovation: A systematic literature review and narrative synthesis. European Journal of Innovation Management, 24(2), 338-370. https://doi.org/10.1108/EJIM-11-2019-0339

Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y. & Loutsaris, M. A. (2019, April). How machine learning is changing e-government. In Proceedings of the 12th international conference on theory and practice of electronic governance (pp. 354-363). https://doi.org/10.1145/3326365.3326412

Alshahrani, A., Dennehy, D. & Mäntymäki, M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia. Government Information Quarterly, 39(4), 101617. https://doi.org/10.1016/j.giq.2021.101617

Ballester, O. (2021, June). An artificial intelligence definition and classification framework for public sector applications. In DG. O2021: The 22nd Annual International Conference on Digital Government Research (pp. 67-75). https://doi.org/10.1145/3463677.3463709

Broomfield, H. & Reutter, L. M. (2021). Towards a data-driven public administration: An empirical analysis of nascent phase implementation. https://doi.org/10.58235/sjpa.v25i2.7117

Campion, A., Gasco-Hernandez, M., Jankin Mikhaylov, S. & Esteve, M. (2022). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462-477. https://doi.org/10.1177/0894439320979953

Casalino, N., Saso, T., Borin, B., Massella, E., Lancioni, F. (2020). Digital Competences for Civil Servants and Digital Ecosystems for More Effective Working Processes in Public Organizations. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds) Digital Business Transformation. Lecture Notes in Information Systems and Organisation, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-47355-6_21

Chatfield, A. T. & Reddick, C. G. (2018). Customer agility and responsiveness through big data analytics for public value creation: A case study of Houston 311 on-demand services. Government Information Quarterly, 35(2), 336-347. https://doi.org/10.1016/j.giq.2017.11.002

Chatterjee, S. (2020), AI strategy of India: policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), 757-775. https://doi.org/10.1108/TG-05-2019-0031

Chohan, S.R., Hu, G., Khan, A.U., Pasha, A.T. and Sheikh, M.A. (2021). Design and behavior science in government-to-citizens cognitive-communication: a study towards an inclusive framework. Transforming Government: People, Process and Policy, 15(4), 532-549. https://doi.org/10.1108/TG-05-2020-0079

Chourasia, S., Dhama, A., & Bhardwaj, G. (2024, May). AI-driven organizational culture evolution: A critical review. In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 1839-1844). IEEE. https://doi.org/10.1109/IC3SE62002.2024.10592949

Clarke, A. & Craft, J. (2017). The vestiges and vanguards of policy design in a digital context. Canadian Public Administration, 60(4), 476-497. https://doi.org/10.1111/capa.12228

Coglianese, C. & Lehr, D. (2016). Regulating by robot: Administrative decision making in the machine-learning era. Geo. LJ, 105, 1147.

Criado, J.I. and Gil-Garcia, J.R. (2019). Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. International Journal of Public Sector Management, 32(5), 438-450. https://doi.org/10.1108/IJPSM-07-2019-0178

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008

De Vries, H., Bekkers, V. & Tummers, L. (2016). Innovation in the public sector: A systematic review and future research agenda. Public administration, 94(1), 146-166. https://doi.org/10.1111/padm.12209

Dodd, C., & Cordella, A. (2019, June). It takes two to tango: Bringing together users and artificial intelligence to create public value. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 389-397).

Dulam, N., Gade, K. R. & Gosukonda, V. (2022). Data Mesh and Data Governance: Finding the Balance. Journal of AI-Assisted Scientific Discovery, 2(2), 226-250. https://scienceacadpress.com/index.php/jaasd/article/view/230

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives onemerging challenges, opportunities, and agenda for research, practice and policy.International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Erkut, B. (2020). From Digital Government to Digital Governance: Are We There Yet? Sustainability, 12(3), 860. https://doi.org/10.3390/su12030860

Felemban, H., Sohail, M. & Ruikar, K. (2024). Exploring the Readiness of Organisations to Adopt Artificial Intelligence. Buildings, 14(8), 2460. https://doi.org/10.3390/buildings14082460

Giest, S. (2017). Big data for policymaking: fad or fasttrack?. Policy Sciences, 50(3), 367-382. https://doi.org/10.1007/s11077-017-9293-1

Gill, A.S., Germann, S. Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs). AI Ethics 2, 293–301 (2022). https://doi.org/10.1007/s43681-021-00058-z

Hammad, A. & Abu-Zaid, R. (2024). Applications of AI in Decentralized Computing Systems: Harnessing Artificial Intelligence for Enhanced Scalability, Efficiency, and Autonomous Decision-Making in Distributed Architectures. Applied Research in Artificial Intelligence and Cloud Computing, 7(6), 161–187. Retrieved from https://researchberg.com/index.php/araic/article/view/214

Issa, J., Abdulrahman, L. M., Abdullah, R. M., Sami, T. M. G. & Wasfi, B. (2024). AI-powered Sustainability Management in Enterprise Systems based on Cloud and Web Technology: Integrating IoT Data for Environmental Impact Reduction. Journal of Information Technology and Informatics, 3(1), 154.

Janssen, M., Brous, P., Estevez, E., Barbosa, L. S. & Janowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government information quarterly, 37(3), 101493. https://doi.org/10.1016/j.giq.2020.101493

Jia, X., Chen, J., Mei, L. and Wu, Q. (2018). How leadership matters in organizational innovation: a perspective of openness. Management Decision, 56(1), 6-25. https://doi.org/10.1108/MD-04-2017-0415

Kankanhalli, A., Charalabidis, Y. & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309. https://doi.org/10.1016/j.giq.2019.02.003

Klyuchko, O. & Melezhyk, O. (2024). Innovative Databases in Ecomonitoring Information Systems: Images of Genetic Codes as Keys. Journal of Applied Interdisciplinary Research, (Special Issue), 35-49. https://doi.org/10.25929/re84ry96

Kulal, A., Rahiman, H. U., Suvarna, H., Abhishek, N. & Dinesh, S. (2024). Enhancing public service delivery efficiency: Exploring the impact of AI. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100329. https://doi.org/10.1016/j.joitmc.2024.100329

Kumar, P. (2024). Artificial intelligence (AI)-augmented knowledge management capability and clinical performance: implications for marketing strategies in health-care sector. Journal of Knowledge Management, 29(2), 415-441. https://doi.org/10.1108/JKM-01-2024-0111

Kuziemski, M. & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976

Li, M. & Wang, J. (2024). The impact of vertical and horizontal pressures on policy diffusion: evidence from APLS adoption in China. Journal of Chinese Governance, 1–25. https://doi.org/10.1080/23812346.2024.2426769

Lopes, K.M.G., Macadar, M.A. and Luciano, E.M. (2019). Key drivers for public value creation enhancing the adoption of electronic public services by citizens. International Journal of Public Sector Management, 32(5), 546-561. https://doi.org/10.1108/IJPSM-03-2018-0081

Madan, R. & Ashok, M. (2022). A Public Values Perspective on the Application of Artificial Intelligence in Government Practices: A Synthesis of Case Studies. In J. Saura & F. Debasa (Eds.), Handbook of Research on Artificial Intelligence in Government Practices and Processes (pp. 162-189). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-9609-8.ch010

Madan, R. & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. https://doi.org/10.1016/j.giq.2022.101774

Madan, R., & Ashok, M. (2025). Making sense of AI benefits: a mixed-method study in Canadian public administration. Information Systems Frontiers, 27(3), 889-923. https://doi.org/10.1007/s10796-024-10475-0

Mahmood, H. S., Abdulqader, D. M., Abdullah, R. M., Rasheed, H., Ismael, Z. N. R. & Sami, T. M. G. (2024). Conducting In-Depth Analysis of AI, IoT, Web Technology, Cloud Computing, and Enterprise Systems Integration for Enhancing Data Security and Governance to Promote Sustainable Business Practices. Journal of Information Technology and Informatics, 3(2).

Mahmood, S., Rehman, S. U. & Ashraf, M. U. (2023). Organizational culture, AI training, and technological tools: Influencing women's leadership success in the unique context of the UAE. Journal of Excellence in Management Sciences, 2(2), 18-28. https://journals.smarcons.com/index.php/jems/article/view/146

Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., ... & Niehaves, B. (2022). Enabling AI capabilities in government agencies: A study of determinants for European municipalities. Government Information Quarterly, 39(4), 101596. https://doi.org/10.1016/j.giq.2021.101596

Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 376(2128). http://dx.doi.org/10.1098/rsta.2017.0357

Milosevic, Z. (2021, October). Enabling scalable AI for Digital Health: interoperability, consent and ethics support. In 2021 IEEE 25th international enterprise distributed object computing workshop (EDOCW) (pp. 18-27). IEEE https://doi.org/10.1109/EDOCW52865.2021.00028.

Misra, S. K., Sharma, S. K., Gupta, S. & Das, S. (2023). A framework to overcome challenges to the adoption of artificial intelligence in Indian Government Organizations. Technological Forecasting and Social Change.vol 194. https://doi.org/10.1016/j.techfore.2023.122721

Nandan Prasad, A. (2024). Future Trends and Emerging Challenges. In: Introduction to Data Governance for Machine Learning Systems. Apress, Berkeley, CA. https://doi.org/10.1007/979-8-8688-1023-7_10

Neumann, O., Guirguis, K. & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685

Ojo, A., Mellouli, S. & Ahmadi Zeleti, F. (2019). A realist perspective on AI-era public management. In Proceedings of the 20th Annual International Conference on Digital Government Research (pp. 159-170). https://doi.org/10.1145/3325112.3325261

Pastor-Escuredo, D., Treleaven, P. & Vinuesa, R. (2022). An Ethical Framework for Artificial Intelligence and Sustainable Cities. AI, 3(4), 961-974. https://doi.org/10.3390/ai3040057

Pencheva, I., Esteve, M. & Mikhaylov, S. J. (2020). Big Data and AI – A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24-44. https://doi.org/10.1177/0952076718780537

Perera, H. M. (2024). Scaling AI Solutions for Societal Benefit: Infrastructural, Organizational, and Policy Considerations. Journal of Intelligent Connectivity and Emerging Technologies, 9(1), 40–52. Retrieved from https://questsquare.org/index.php/JOUNALICET/article/view/50

Pourezzat, A. A., Abbasi, T., Maghsoodi Kenari, S. & Namdar Joybari, M. M. (2024). Investigating the Role of the basic Components of Smart Governance in Realizing a Smart City with the ISM Method (Case Study: Tehran). Journal of Public Administration, 16(3), 535-561. doi: 10.22059/jipa.2024.376694.3505 (in Persian)

Putri, A. & Tran, M. Q. (2023). Artificial Intelligence and the Quest for Sustainable Innovation: Ethical Implications, Cultural Considerations, and Operational Excellence in the Deployment of AI across Diverse Sectors. AI, IoT and the Fourth Industrial Revolution Review, 13(10), 12–17. Retrieved from https://scicadence.com/index.php/AI-IoT-REVIEW/article/view/46

Radu, R. (2021). Steering the governance of artificial intelligence: National strategies in perspective. Policy and Society, 40(2), 178-192. https://doi.org/10.1080/14494035.2021.1929728

Rane, N. (2023). Integrating leading-edge artificial intelligence (AI), internet of things (IOT), and big data technologies for smart and sustainable architecture, engineering and construction (AEC) industry: Challenges and future directions. Engineering and Construction (AEC) Industry: Challenges and Future Directions https://dx.doi.org/10.2139/ssrn.4616049

Rezaei, M., Pironti, M. and Quaglia, R. (2024). AI in knowledge sharing, which ethical challenges are raised in decision-making processes for organisations? Management Decision, 63(10), 3369-3388. https://doi.org/10.1108/MD-10-2023-2023

Rizk, B. (2020). Effects of organisational support and innovation culture on AI adoption (Doctoral dissertation, Dublin, National College of Ireland). https://norma.ncirl.ie/id/eprint/4799

Rowshan, S. A., Yaqoubi, N. & Momeni, A. (2021). Application of artificial intelligence in the public sector (meta-combination study). Iranian journal of management sciences, 16(61), 117-145. (in Persian)

Salamkar, M.A. & Immaneni, J. (2024). Data Governance: AI applications in ensuring compliance and data quality standards. Journal of AI-Assisted Scientific Discovery, 4(1), 158-183. https://scienceacadpress.com/index.php/jaasd/article/view/224

Sandelowski, M. & Barroso, J. (2006). Handbook for synthesizing qualitative research. Springer publishing company

Saunders, M., Lewis, P. & Thornhill, A. (2007). Research methods for business students. Fifth edition. Harlow: Pearson Education.

Schaefer, C., Lemmer, K., Samy Kret, K., Ylinen, M., Mikalef, P. & Niehaves, B. (2021). Truth or dare?–how can we influence the adoption of artificial intelligence in municipalities? https://doi.org/10.24251/HICSS.2021.286

Schedler, K., Guenduez, A. A. & Frischknecht, R. (2019). How smart can government be? Exploring barriers to the adoption of smart government. Information Polity, 24(1), 3-20. https://doi.org/10.3233/IP-180095

Sharma, S. (2025). From Data to Decisions: Cloud, IoT, and AI Integration. In Integration of Cloud Computing and IoT (pp. 461-479). Chapman and Hall/CRC.

Siaw, C.A. & Ali, W. (2024). Substitution and complementarity between human and artificial intelligence: a dynamic capabilities view. Journal of Managerial Psychology, 40(5), 539- 554. https://doi.org/10.1108/JMP-06-2024-0398

Stenberg, L. & Nilsson, S. (2020). Factors influencing readiness of adopting AI: A qualitative study of how the TOE framework applies to AI adoption in governmental authorities. KTH, Skolan för industriell teknik och management (ITM).

Sun, T. Q. & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368-383. https://doi.org/10.1016/j.giq.2018.09.008

Tabim, V. M., Ayala, N. F., & Frank, A. G. (2024). Implementing vertical integration in the industry 4.0 journey: which factors influence the process of information systems adoption?  Information Systems Frontiers, 26(5), 1615-1632. https://doi.org/10.1007/s10796-021-10220-x

Tallarico, S., Pellegrini, L., Lazzarotti, V. and Lazzini, S. (2024). Boosting firms’ absorptive capacity: the digital technologies edge. European Journal of Innovation Management, 28(6), 2558-2580. https://doi.org/10.1108/EJIM-09-2023-0741

Tornatzky, L. & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

Tursunbayeva, A. & Gal, H. C. B. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders. Business Horizons, 67(4), 357- 368. https://doi.org/10.1016/j.bushor.2024.04.006

van Noordt, C. & Misuraca, G. (2020, September). Evaluating the impact of artificial intelligence technologies in public services: towards an assessment framework. In Proceedings of the 13th international conference on theory and practice of electronic governance (pp. 8-16). https://doi.org/10.1145/3428502.3428504

Wirtz, B. W. & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076-1100. https://doi.org/10.1080/14719037.2018.1549268

Wirtz, B. W., Weyerer, J. C. & Geyer, C. (2018). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 1–20. doi:10.1080/01900692.2018.1498

Wirtz, B. W., Weyerer, J. C. & Geyer, C. (2018). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103

Young, M. M., Bullock, J. B. & Lecy, J. D. (2019). Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance, 2(4), 301-313. https://doi.org/10.1093/ppmgov/gvz014

Zhang, L., Shao, Z., Chen, B. & Benitez, J. (2024). Unraveling Generative AI Adoption in Enterprise Digital Platforms: The Moderating Role of Internal and External Environments. IEEE Transactions on Engineering Management. https://doi.org/110.1109/TEM.2024.3513773

Zuiderwijk, A., Chen, Y. C. & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government information quarterly, 38(3), 101577. https://doi.org/10.1016/j.giq.2021.101577

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