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dynamics (OECD, 2023; World Bank, 2022), technological adoption alone does not
guarantee sustained competitiveness.
In emerging economies such as Vietnam, export-led growth has driven integration
into global value chains but has also revealed limitations in domestic value capture
(Gereffi et al., 2005; Rodrik, 2018). As production becomes more knowledge-intensive,
upgrading requires stronger innovation capability and supportive institutional conditions
(Cohen & Levinthal, 1990; Lundvall, 1992).
Despite growing research on AI and digital transformation, limited studies integrate
AI adoption, innovation capability, export competitiveness, and institutional alignment
into a unified framework, particularly in emerging economy contexts. Existing studies
often examine these dimensions separately, without capturing their interaction in shaping
export upgrading.
This study addresses this gap by proposing a conceptual and policy-oriented
framework linking AI adoption, innovation capability, and export competitiveness in
Vietnamese export-oriented enterprises. By integrating perspectives from innovation
theory, digital transformation, and international competitiveness, the paper contributes
to both academic literature and policy discussions in emerging economies (Porter, 1990;
UNCTAD, 2021).
The remainder of the paper is organized as follows. Section 2 reviews relevant
literature. Section 3 presents the conceptual framework. Section 4 analyzes the
Vietnamese context. Section 5 discusses policy implications. Section 6 provides discussion
and implications. Section 7 concludes.
2. Literature review
2.1. Artificial intelligence and firm-level competitiveness
Artificial intelligence is widely recognized as a transformative general-purpose
technology that reshapes industries, markets, and organizational processes (Brynjolfsson
& McAfee, 2014; UNCTAD, 2021). Unlike conventional automation tools, AI systems
possess learning capabilities that enable predictive analytics, adaptive optimization, and
autonomous decision-making (Acemoglu & Restrepo, 2018). Recent studies emphasize its
role in enhancing firm productivity and innovation performance, particularly in digital
economies where data-driven decision-making improves competitive positioning (Agrawal
et al., 2022; OECD, 2023).
At the firm level, AI adoption enhances operational efficiency, reduces transaction
costs, and improves supply chain coordination. However, sustainable competitiveness
depends on differentiation, innovation, and value creation rather than cost leadership
alone (Porter, 1990). Empirical research indicates that firms integrating AI into strategic
functions outperform late adopters in innovation outcomes and market responsiveness
(Autor, 2015; OECD, 2021). Nevertheless, technological investment without
complementary organizational transformation yields limited returns, as digital
technologies must be embedded within dynamic managerial capabilities to generate long-
term advantage (Teece, 2018).
2.2. Innovation capability and dynamic capabilities
Innovation capability refers to a firm’s capacity to generate, absorb, and apply new
knowledge to enhance competitiveness (Schumpeter, 1934; Fagerberg et al., 2005). In
digital contexts, innovation increasingly relies on intangible assets, algorithms, and data
ecosystems. Dynamic capability theory highlights how firms sense opportunities, seize
them, and reconfigure resources to sustain competitiveness (Teece et al., 1997).

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