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This framework highlights that export competitiveness in emerging economies
depends not only on technological adoption but also on the interaction between firm-
level capabilities and institutional conditions.
4. AI adoption and innovation in Vietnamese export enterprises
Vietnam has achieved significant export expansion and integration into global value
chains; however, domestic value capture remains limited, with a substantial share of
exports generated by foreign-invested enterprises (Gereffi et al., 2005; Rodrik, 2018).
The Vietnamese government has recognized the strategic importance of digital
transformation and artificial intelligence. Resolution No. 52-NQ/TW emphasizes proactive
participation in the Fourth Industrial Revolution, while the National Strategy on Research,
Development and Application of Artificial Intelligence to 2030 outlines objectives for
technological capacity building (Vietnam Government, 2019; Vietnam Prime Minister,
2021).
Despite these policy initiatives, implementation challenges persist. Many export-
oriented enterprises, particularly small and medium-sized firms, face constraints related
to digital skills, financial resources, and infrastructure access (World Bank, 2022). Without
coordinated ecosystem support, AI adoption risks becoming concentrated among large
enterprises, thereby widening structural disparities.
Global supply chain reconfiguration and sustainability requirements further
intensify competitive pressures. International buyers increasingly demand traceability,
quality assurance, and compliance with environmental standards, areas where AI-enabled
monitoring systems can provide strategic advantages (UNCTAD, 2023; OECD, 2023).
Effective alignment between enterprise-level innovation and national policy
frameworks remains essential (Mazzucato, 2013).
5. Policy implication for enhancing export competitiveness
Based on the proposed framework, a multi-pillar policy approach is required to
strengthen AI-enabled export competitiveness.
First, enterprise-level transformation should focus on the strategic integration of AI
into core business functions. Firms need to invest in data infrastructure, develop digital
capabilities, and build hybrid talent combining technical and managerial expertise (OECD,
2021). Effective AI governance is also necessary to ensure cybersecurity and sustainable
implementation.
Second, ecosystem development is essential to support technology diffusion and
reduce coordination failures. Public–private partnerships can facilitate research
collaboration and knowledge transfer, while targeted financial support enables small and
medium-sized enterprises to access AI technologies (Mazzucato, 2013; World Bank, 2022).
Third, institutional alignment must ensure policy coherence and regulatory clarity.
Data governance frameworks, digital trade facilitation, and alignment with international
standards play a critical role in improving market access and investor confidence (OECD,
2023).
Overall, AI-driven export competitiveness should be approached as a systemic
transformation requiring coordinated efforts among enterprises, policymakers, and
innovation institutions.
6. Discussion
The findings of this study contribute to both theoretical and policy debates. From a
theoretical perspective, the framework extends dynamic capability theory into the
domain of digital trade and export upgrading (Teece, 2018). It emphasizes that

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