Public-sector Digital Transformation in a New Governance Context: a Conditional Maturity Framework
DOI:
https://doi.org/10.59394/JSM.156Keywords:
Public-sector digital transformation, conditional maturity framework, platform architecture, data governance and artificial intelligence, digital universityAbstract
This article examines public-sector digital transformation at the organizational level through the lens of capability dependencies. Drawing on an integrative conceptual synthesis of three streams of literature digital transformation and maturity models; platform architecture and interoperability; and data governance and artificial intelligence - the study proposes a seven-stage framework linked by four types of relationships: hard prerequisites, soft dependencies, parallel capabilities, and feedback loops. Six propositions specify the mechanisms and boundary conditions governing transitions between stages. The analytical framework does not assume a single linear temporal sequence; instead, it treats artificial intelligence as a conditional advanced capability and public value as the ultimate outcome criterion. The article discusses implications for government agencies and higher education institutions in Viet Nam and proposes directions for future empirical validation.
References
Cestari, J. M. A. P., Loures, E. de F. R., Santos, E. A. P., & Panetto, H. (2020). A capability model for public administration interoperability. Enterprise Information Systems, 14(8), 1071-1101. https://doi.org/10.1080/17517575.2018.1564154
Government of Viet Nam. (2026a). Resolution No. 66.22/2026/NQ-CP dated July 9, 2026 on digital citizen development, effective August 15, 2026.
Government of Viet Nam. (2026b). Decree No. 142/2026/ND-CP dated April 30, 2026 detailing certain articles of, and measures for implementing, the Artificial Intelligence Law.
GovStack Global Initiative. (2024). Architecture specification: Public administration ecosystem reference architecture. https://specs.govstack.global/architecture
Hohpe, G., & Woolf, B. (2003). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley.
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1-2), 18-26. https://doi.org/10.1007/s13162-020-00161-0
Janowski, T. (2015). Digital government evolution: From transformation to contextualization. Government Information Quarterly, 32(3), 221-236. https://doi.org/10.1016/j.giq.2015.07.001
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
Mendoza Dionicio, M. A., Cano Lengua, M. Á., & Rodríguez, C. (2026). Digital maturity assessment models in public administration: A systematic review. Bulletin of Electrical Engineering and Informatics, 15(2), 1283-1300.
Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), 101385.
National Assembly. (2024). Data Law No. 60/2024/QH15 dated November 30, 2024, effective July 1, 2025.
National Assembly. (2025a). Artificial Intelligence Law No. 134/2025/QH15, effective March 1, 2026.
National Assembly. (2025b). Personal Data Protection Law No. 91/2025/QH15, effective January 1, 2026.
OECD. (2019). The path to becoming a data-driven public sector. OECD Publishing. https://doi.org/10.1787/059814a7-en
OECD. (2020). The OECD digital government policy framework: Six dimensions of a digital government. OECD Publishing. https://doi.org/10.1787/f64fed2a-en
Office of the Party Central Committee. (2026). Guidance No. 11-HD/VPTW dated June 24, 2026, implementing Regulation No. 08-QC/TW dated November 5, 2025, of the Secretariat on monitoring, follow-up, and inspection task implementation through the real-time, data-based monitoring and evaluation system.
Politburo. (2024). Resolution No. 57-NQ/TW dated December 22, 2024 on breakthroughs in the development of science, technology, innovation, and national digital transformation.
Prime Minister. (2020). Decision No. 749/QD-TTg dated June 3, 2020, approving the National Digital Transformation Program to 2025, with orientations to 2030.
Prime Minister. (2026a). Decision No. 940/QD-TTg dated May 26, 2026, approving the Project on Developing the National Digital Identity Application VNeID for 2026 - 2030, with a vision to 2045.
Prime Minister. (2026b). Decision No. 33/2026/QD-TTg dated June 30, 2026, promulgating the List of High-Risk Artificial Intelligence Systems, effective August 15, 2026.
Sprengel, A., & Ulrich, P. (2025). Differentiated or deficient? Perspective on multidimensional maturity models: Evidence from a structured literature review. Procedia Computer Science, 270, 164-173.
Standing Agency of the Central Steering Committee for the Development of Science, Technology, Innovation and Digital Transformation. (2026). Notice No. 24-TB/CQTTBCD dated July 7, 2026, on the conclusions of General Secretary and State President To Lam at the national conference reviewing one year and six months of implementation of Resolution No. 57-NQ/TW.
Teichert, R. (2019). Digital transformation maturity: A systematic review of literature. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(6), 1673-1687. https://doi.org/10.11118/actaun201967061673
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003
Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490-495.
World Bank. (2025). GovTech maturity index 2025: Tracking public sector digital transformation worldwide. World Bank.
