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Union member states is strongly associated with human capital development, innovation
capacity, and economic conditions.
Recent research has further developed the concept of institutional readiness for AI
adoption. Anomah (2025) argues that the capacity of public institutions to implement AI
depends on the interaction between digital infrastructure, organizational capability, and
institutional environments that support innovation. In many developing countries,
limitations in data infrastructure, digital skills, and regulatory frameworks remain
significant barriers to AI implementation in the public sector.
Evidence from Vietnam also reflects these challenges. Studies indicate that AI
applications are gradually emerging in areas such as data management, digital public
services, and decision support in local governance (Nguyen et al., 2026). International
reports further suggest that Vietnam has begun to strengthen its national AI strategy and
innovation ecosystem in recent years (UNESCO, 2025). Nevertheless, the adoption of AI in
the Vietnamese public sector remains constrained by limitations in data infrastructure,
analytical capacity, and institutional coordination.
Overall, the literature indicates that the implementation of AI in public governance
is shaped not only by technological development but also by organizational capability and
institutional environments. Consequently, assessing a country’s readiness for AI adoption
requires an integrated perspective that considers the interaction between digital
infrastructure, data governance capacity, and broader institutional conditions.
2.3. Analytical framework: linking e-government foundations, GovTech
integration, and AI readiness
Building on the literature discussed above, this study conceptualizes digital
governance development as a multi-layer process in which different technological and
institutional capabilities evolve over time. Previous research suggests that the
implementation of AI in public governance cannot be separated from the digital
infrastructure and data governance capacities established during earlier stages of digital
government development (Zuiderwijk et al., 2021; OECD, 2025).
To analyze this relationship, the study adopts an analytical framework that
combines the Technology–Organization–Environment (TOE) model with Institutional
Theory. The TOE framework suggests that technology adoption is shaped by three key
dimensions: technological factors, organizational characteristics, and environmental
conditions. Institutional Theory complements this perspective by emphasizing the role of
regulatory frameworks, governance norms, and institutional environments in shaping
organizational behavior and technological change.
Integrating these two perspectives allows for a more comprehensive understanding
of the conditions that influence AI adoption in the public sector. Based on this theoretical
foundation, the study proposes a three-layer analytical framework that reflects the
development trajectory of digital governance.
The first layer represents the technological foundation, which includes digital
infrastructure, online public services, and digital capabilities among citizens. These
elements form the basis of e-government development and are commonly captured
through indicators such as the United Nations E-Government Development Index (United
Nations, 2024).
The second layer reflects the organizational dimension of digital governance,
particularly the integration of GovTech systems and data governance within public
administration. At this stage, governments develop interoperable information systems,

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