A Context-Specific Framework for Managing Digital Transformation with Artificial Intelligence in Data-Driven Decision-Making: A Delphi Gap Analysis of Executive Agencies in Bushehr Province

Abbasi, I.

doi:10.22034/tdas.2026.256208

A Context-Specific Framework for Managing Digital Transformation with Artificial Intelligence in Data-Driven Decision-Making: A Delphi Gap Analysis of Executive Agencies in Bushehr Province

Document Type : Original Article

Author

I. Abbasi

Department of Industrial Management, Meybod University, Meybod, Iran

10.22034/tdas.2026.256208

Abstract

This study develops a context-specific framework for managing digital transformation in Iran’s administrative system, with an emphasis on artificial intelligence (AI) in data-driven decision-making, and examines the gap between the perceived necessity of digital-transformation requirements and their implementation feasibility in the executive agencies of Bushehr Province. The study is applied in purpose and descriptive–exploratory in design. Twenty-eight indicators in ten dimensions, derived from the literature, were evaluated in a two-round modified Delphi survey by 25 experts in public administration, information technology and administrative transformation, each on two five-point scales: necessity and implementation feasibility. All 28 indicators were rated as necessary (mean necessity 4.00–4.72) in the first round; feasibility means ranged from 3.76 to 4.12 after the second round. Feasibility was lower than necessity for every indicator (gaps from −0.08 to −0.84). Paired Wilcoxon signed-rank tests showed that the gap was significant for 24 indicators at the 5% level and for 7 after Holm correction. The largest gaps concerned the use of intelligent tools to shorten responses to citizens (−0.84), the share of strategic decisions supported by AI (−0.76), and decision-support systems, integrated data infrastructure, AI maturity and cost reduction (−0.68 each), followed by managers’ commitment, employees’ data literacy and an evidence-based decision culture (−0.64). Based on these findings, a four-layer conceptual–operational framework (infrastructure, processes, human resources and governance) is proposed, together with practical recommendations for the province. Because the evidence comes from a small expert panel, the framework is a well-founded proposal to be tested empirically rather than a validated causal model.

Keywords

Digital transformation management

Artificial intelligence

Data-driven decision-making

Administrative system

Delphi method

Gap analysis

Smart governance

[1] Almusaed, A., Almsaad, A., & Yitmen, I. (2025). Smart cities that think: Cognitive infrastructures, AI governance, and sustainable urban futures. In Smart cities: Designing the future of urban living. IntechOpen. https://doi.org/10.5772/intechopen.1013038

[2] Saxena, D., Kahn, Z., Moon, E. S.-Y., Chambers, L. M., Jackson, C., Lee, M. K., Eslami, M., Guha, S., Erete, S., & Irani, L. (2025). Emerging practices in participatory AI design in public sector innovation. In Extended abstracts of the CHI conference on human factors in computing systems. ACM. https://doi.org/10.1145/3706599.3706727

[3] Singh, B., Jain, A., Wongmahesak, K., & Chandra, S. (2025). Reimagining democracy in the digital era: Transformative role of artificial intelligence in modern governance. In Democracy and democratization in the age of AI (pp. 1–18). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8749-8.ch001

[4] Engin, Z., Crowcroft, J., Hand, D., & Treleaven, P. (2025). The algorithmic state architecture (ASA): An integrated framework for AI-enabled government. arXiv. https://arxiv.org/abs/2503.08725

[5] Baydili, I. T., & Taşcı, B. (2025). Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making. Syst., 13, 583. https://doi.org/10.3390/systems13070583

[6] Pana Micu, F. (2025). Benefits and challenges of machine learning in public administration. In Proceedings of the Central and Eastern European eDem and eGov Days 2025 (pp. 94–97). https://doi.org/10.1145/3773002.3773794

[7] Chen, J., Lou, Z., Chen, G., Zhang, C., & Li, Y. (2025). Research on the application of AI-based intelligent decision support systems in e-government services. Journal of Organizational and End User Computing, 37(1), 1–25. https://doi.org/10.4018/JOEUC.392618

[8] Mehrabi Koushki, R., & Hamidi, M. R. (2025). Investigating the problem system of digital government transformation in Iran using the soft systems methodology. Government Studies, 11(42), 169–232.

[9] Danaeefard, H., Kazemi, S. H., & Karimi, M. (2024). Exploring the challenges of digital transformation in the Iranian public sector: A qualitative study. Public Organization Review, 24(3), 1077–1103. https://doi.org/10.1007/s11115-024-00776-y

[10] Morattab, Y. (2025). Supervisory report on implementing Clause J of Article 107 of the Seventh Development Program Law of the Islamic Republic of Iran: Smart government services (21035). Parliamentary Research Center of the Islamic Consultative Assembly. https://report.mrc.ir/article_11226_e4d78a6b4d93e1d79241f7b282fa3413.pdf

[11] Moradi Berelian, M., & Tangestani, M. G. (2026). Rethinking the Legitimacy of Data-Driven Governance in Public Law with a Focus on the Iranian Legal Experience. Quarterly Journal of Governance Knowledge, e243234.

[12] Akbari, I., Fard, D., Mahmoudi, & Khosravi. (2024). Identification and prioritization of barriers and challenges to data-driven governance from the perspective of employing artificial intelligence and data-based technologies in the public sector. Strategic Studies of Public Policy, 14(51), 56–81.

[13] Sahraei, F. (2025). An analysis of data quality in e-government and its impact on data governance in Iran. Government Studies, 11(41), 133–186.

[14] Tangestani, M., & Moradi Berelian, M. (2025). Legal legitimacy of algorithmic decisions in digital governments: A comparative study with a focus on the Iranian legal experience. Administrative Law, 12(44), 27–53.

[15] Karimi,M , Danaeefard,H and Kazemi,H . (2025). Exploring the Challenges of Digital Transformation in Iran's Public Sector: The Structural Interpretive Modeling Approach. Transformation Management Journal, 16, 1-31. https://doi.org/10.22067/tmj.2024.81350.1405

[16] Kameli, B. (2025). Data governance and its role in the development of artificial intelligence in Iran (20780). Parliamentary Research Center of the Islamic Consultative Assembly. https://report.mrc.ir/article_10990_8eec1a372e8a99076315db9ea4d24b2c.pdf

[17] Roshani, A., & Sheikholeslami Kandelousi, N. (2025). Prerequisites for embracing technology artificial intelligence in Iranian government agencies: A roadmap for transformative governance. SSRN. https://doi.org/10.2139/ssrn.5098035

[18] Yeganegi, K., & Afrouz, F. (2025). Legal and ethical challenges of artificial intelligence in public law: Examining the dimensions of regulation and accountability. Comparative Public Administration, 3(1).

[19] Müller, S. D., Zaggl, M., Svangaard, R., & Jakobsen, A. M. (2025). Digital transformation toward data-driven decision-making: Theorizing action strategies in response to transformation challenges. Communications of the Association for Information Systems, 56(1), 39. https://doi.org/10.17705/1CAIS.05637

[20] Haryanti, T., Rakhmawati, N. A., & Subriadi, A. P. (2024). Assessing the digital transformation landscapes of organization: The digital transformation self-assessment maturity model (DX-SAMM). Procedia Computer Science, 234, 1561–1569. https://doi.org/10.1016/j.procs.2024.03.158

[21] Schlagwein, D., Currie, W., Leimeister, J. M., & Willcocks, L. (2025). Digital futures: Definition (what), importance (why) and methods (how). Journal of Information Technology, 40, 2–8. https://doi.org/10.1177/02683962241301544

[22] Kunadt, F. (2025). Combining strategic foresight and strategic communication: An interdisciplinary framework of future-oriented communication in times of multiple future challenges. International Journal of Strategic Communication, 19(4), 513–535. https://doi.org/10.1080/1553118X.2025.2454676

[23] Ngo, Q. D., Hoang, V. H., & Tran, T. V. H. (2025). Integrating anticipatory governance and causal layered analysis: A novel theoretical framework for long-term socioeconomic development forecasting. Futures & Foresight Science, 7(1), e70001. https://doi.org/10.1002/ffo2.70001

[24] Theodorakopoulos, L., Theodoropoulou, A., & Halkiopoulos, C. (2025). Cognitive bias mitigation in executive decision-making: A data-driven approach integrating big data analytics, AI, and explainable systems. Electronics, 14(19), 3930. https://doi.org/10.3390/electronics14193930

[25] Villeneuve, A., & Bouchamma, Y. (2025). Transforming data into action: The central role of school leadership. In INTED2025 proceedings. IATED. https://doi.org/10.21125/inted.2025.0634

[26] Abarca Pizarro, S., & Palacios Marqués, D. (2025). Building data-driven organizations: A theoretical exploration of the role of leadership in big data management. https://doi.org/10.65479/joinetech.24

[27] Kobanoğlu, B. (2025). Data-driven leadership. In Beyond authority: Leadership approaches from classical to contemporary (p. 195). Özgür Publications. https://doi.org/10.58830/ozgur.pub939.c3942

[28] Kuhlmann, S., Wollmann, H., & Reiter, R. (2025). Introduction to comparative public administration: Administrative systems and reforms in Europe. Edward Elgar Publishing.

[29] Trondal, J. (2025). The multilevel administrative state and the future of public administration research. International Journal of Public Administration, 48(5–6), 321–333. https://doi.org/10.1080/01900692.2024.2412273

[30] McDonnell, E. M. (2025). Bureaucracy in action: The sociology of public administration. Annual Review of Sociology, 51. https://doi.org/10.1146/annurev-soc-091324-053657

[31] Turtz, M. (2025). The necessity of bureaucracy and the fixation of legitimacy. International Journal of Ethics and Systems. https://doi.org/10.1108/IJOES-07-2024-0189

[32] Kazemi, M., Dehghanpour Farashah, A., & Dehghanpour Farashah, A. (2025). Smart public governance scenarios based on digital transformation: Foresight of government human capital transformation. Smart Business Management Studies.

[33] Ghaeminia, F., Fili, A., Sepehr, & Fard, A. (2025). Artificial intelligence in the context of digital transformation: A pathological reflection on understanding concepts and applications. Public Administration Perspective, 16(2), 80–103.

[34] Marzdar, M. H. (2025). Artificial intelligence in Iran's public administration: Opportunities, challenges, and strategic approaches for governance innovation. International Journal of Applied Research in Management, Economics and Accounting, 2(2), 16–35.

[35] ESMAEILPOOR, M., Hoseini, S. Y., & Jafarpoor, Y. (2018). Identification of Electronic Commerce Adaption Barriers in Small and Medium Enterprises (SMEs): ACase Study of the Active Firms Based in the Industrial Town of Bushehr City. https://doi.org/10.29252/bar.10.18.39

[36] Zhu, X. (2026). Public administration with, of, and through AI: Toward a new paradigm in the era of intelligence. Journal of Chinese Governance, 11(1), 30–57. https://doi.org/10.1080/23812346.2025.2578589

[37] Calleo, Y., & Pilla, F. (2025). Real-time AI Delphi: A novel method for decision-making and foresight contexts. Futures, 103703. https://doi.org/10.1016/j.futures.2025.103703

[38] Wang, F. (2025). Collaborative foresight in the age of AI: A framework for evolving human-AI dynamics in strategic decision-making and futures research. Journal of International DBA Studies-GGU, 1(1), 1.

[39] Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2025). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Management Decision, 63(10), 3501–3532. https://doi.org/10.1108/MD-10-2023-1946

[40] Vatamanu, A. F., & Tofan, M. (2025). Integrating artificial intelligence into public administration: Challenges and vulnerabilities. Administrative Sciences, 15(4), 149. https://doi.org/10.3390/admsci15040149

[41] Sadiku-Dushi, N., Naulleau, M., Skeja, A., Zmokhol, F., & Vrtana, D. (2026). Artificial intelligence in strategic decision-making: How fairness risk, trust, and technical capabilities shape organizational performance. Strategic Change. https://doi.org/10.1002/jsc.70099

[42] Chabok, F., Dadashkarimi, Y., & Irani, F. N. H. (2025). Identifying the components of the e-government governance model with an approach to creating and developing digital businesses. Digital Transformation and Administration Innovation, 3(4), 1–11. https://doi.org/10.61838/dtai.177

[43] Nurgabdeshov, A., Shamayleh, G., & Bensouna, I. (2025). Transforming governance: An integrated framework for digital modernization in the public sector. In Proceedings of the 85th annual meeting of the Academy of Management. https://doi.org/10.5465/AMPROC.2025.22817abstract

[44] Lloret, Á., Peral, J., Ferrández, A., Auladell, M., & Muñoz, R. (2025). A data-driven framework for digital transformation in smart cities: Integrating AI, dashboards, and IoT readiness. Sensors, 25(16), 5179. https://doi.org/10.3390/s25165179

[45] Salazar Morales, D. (2025). A set-theoretical analysis of the pathway(s) to digital innovation in developing democracies. Journal of Comparative Policy Analysis: Research and Practice, 27(5–6), 671–705. https://doi.org/10.1080/13876988.2025.2566735

[46] HOSNI, F. (2025). Strategic Foresight and Digital Transformation: A Dual Approach to Innovation. Revue Internationale des Sciences de Gestion, 8(2).

[47] Cao, L. H. N., Nguyen, P. V., Nguyen, V. T. H., Tran, T. T., & Vrontis, D. (2025). Harnessing artificial intelligence in the public sector: The critical role of strategic foresight in driving performance. Business Process Management Journal, 1–23. https://doi.org/10.1108/BPMJ-08-2025-1317

[48] Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187

[49] Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Flores, C. C., Sirait, E., Luterek, M., Dreyling, R. M., & Tesarova, B. (2025). Responsible AI adoption in the public sector: A data-centric taxonomy of AI adoption challenges. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2026.289

[50] Bin Sulaiman Al-Mutlaq, N., & Malkawi, N. M. (2026). The impact of artificial intelligence on the quality of strategic decisions: The mediating role of organizational data readiness. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3666947

[51] Pareja, H. P. (2025). Artificial intelligence as a driver of digital government transformation: Applications and trends in government operations in the Philippines. Policy & Governance Review, 9(3), 302–317. https://doi.org/10.30589/pgr.v9i3.1257

[52] Busroh, F. F., Khairo, F., Niravita, A., & Nunna, B. P. (2025). Digital transformation with the impact of AI in government decision making. Journal of Law and Legal Reform, 6(4), 1839–1874. https://doi.org/10.15294/jllr.v6i4.30339

[53] Keppeler, F., Borchert, J., Pedersen, M. J., & Lehmann Nielsen, V. (2025). How ensembling AI and public managers improves decision-making. Journal of Public Administration Research and Theory, 35(3), 261–276. https://doi.org/10.1093/jopart/muaf009

[54] Cīrulis, E., & Osis, K. (2025). Public administration and strategic decision-making support based on artificial intelligence: Constraining factors. In Society. Technology. Solutions: Proceedings of the International Scientific Conference. https://doi.org/10.35363/ViA.sts.2025.144

[55] Liarte, I., Criado, J. I., & Alcaide-Muñoz, L. (2026). Determinants of public sector innovation: A comparative study of Spanish local governments. International Journal of Public Administration, 49(1), 30–45. https://doi.org/10.1080/01900692.2025.2451390

[56] Cingolani, L., & Salazar-Morales, D. (2024). The role of public workforce diversity and the administrative ecosystem in advancing digital public service innovation. Public Management Review, 26(10), 2966–2991. https://doi.org/10.1080/14719037.2024.2303609

[57] Goyal, N., & Howlett, M. (2024). Types of learning and varieties of innovation: How does policy learning enable policy innovation? Policy & Politics, 52(4), 564–585. https://doi.org/10.1332/030557321X16841388707452

[58] Demircioglu, M. A., Audretsch, D. B., & Link, A. N. (2025). Innovation in public organizations: The role of human capital. Public Management Review, 27(12), 2908–2933. https://doi.org/10.1080/14719037.2024.2381073

[59] Giest, S. (2025). The intersection of digital and social infrastructures in (a) spatial policymaking. Policy Sciences, 58(2), 369–384. https://doi.org/10.1007/s11077-025-09569-6

[60] Firouzbakht, E., & Rezaeian, A. (2023). Analyzing the barriers of digital transformation in the project-based organizations of Iran's oil and gas industries. Journal of Improvement Management, 16(4), 94-124. https://doi.org/10.22034/jmi.2023.379920.2893

[61] Ahmadi, H., Parsaei, N., Seyed Hamed, H., & Hamidreza, N. A. (2024). Providing a General Model for the Successful Implementation of Digital Transformation in Organizations. Journal of Public Administration (JPA), 16(2) 282–315. https://doi.org/10.22059/JIPA.2024.372113.3470

[62] Fan, Y. (2025). The role of artificial intelligence in the digital transformation of government: opportunities and ethical challenges. Frontiers in public health, 13, 1694996. https://doi.org/10.3389/fpubh.2025.1694996

[63] Almeida, A. S. D., Kock, N., & Zanini, M. T. (2025). Fostering public sector innovation: How data-driven organizational culture influences exploitation innovations. Revista de Administração Contemporânea, 29(4), e240179. https://doi.org/10.1590/1982-7849rac2025240179.en

[64] Elkaabi, A., El Haloui, M. B., & Elmaallam, M. (2025). Towards an improved environmental scanning model based on artificial intelligence tools. In International Conference on Digital Age & Technological Advances for Sustainable Development (pp. 260–267). https://doi.org/10.1007/978-3-032-06725-8_22

[65] Trautendorfer, J., & Eder, N. (2025). Does transparency reach citizens? National accountability mechanisms and public perceptions in Europe. Governance, 38(4), e70041. https://doi.org/10.1111/gove.70041

[66] Jonathan, G. M., Yalew, S. D., Gebremeskel, B. K., & Watat, J. K. (2025). AI adoption in the public sector: Organizational readiness and the pursuit of public value. Complex Systems Informatics and Modeling Quarterly, (45), 43-70. https://doi.org/10.7250/csimq.2025-45.03

[67] Korhonen, P., Liesiö, J., Siljamäki, A., & Wallenius, J. (2025). Supporting scenario-based decision-making with multi-objective optimization. Futures & Foresight Science, 7(2), e70012. https://doi.org/10.1002/ffo2.70012

[68] Cretu, V. (2025). Future-ready public institutions: rethinking data governance through maturity assessment. Economy and Sociology, (1), 43-59. https://doi.org/10.36004/nier.es.2025.1-04

[69] Vissing, K. N., Knoll, M. H., & Hertzum, M. (2025). Benefits slippage: The yearlong process of implementing electronic document management in a Danish municipality. Government Information Quarterly, 42(3), 102051. https://doi.org/10.1016/j.giq.2025.102051

[70] Rho, S. (2025). From digital public institution to AI public institutions: Developing and applying the AI maturity model (PAIMM) for the public sector. Public Institutions and Policy Research, 3(2), 1–41. https://doi.org/10.23412/pipr.3.2.202512.001

Transactions on Data Analysis in Social Science

Volume 8, Issue 2
Spring 2026
Pages 116-137

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A Context-Specific Framework for Managing Digital Transformation with Artificial Intelligence in Data-Driven Decision-Making: A Delphi Gap Analysis of Executive Agencies in Bushehr Province

Volume 8, Issue 2Spring 2026Pages 116-137

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Volume 8, Issue 2
Spring 2026
Pages 116-137