SMEs fail to combine exports with digital upgrading not for lack of demand but because of resource and capability shortfalls; partnerships and networks, rather than standalone investment, are the most effective route to bridge that gap.

Main Finding

Resource and capability constraints are the primary barriers preventing SMEs from successfully combining internationalization and technology adoption. These constraints reduce SMEs’ performance, innovation capacity, and operational effectiveness in international markets. Conversely, alliances, networking, and collaboration act as important mitigants that can help SMEs overcome these barriers.

Key Points

• Problem scope: Literature on SME internationalization and on SME technology adoption have largely evolved separately, producing fragmented understanding at their intersection.
• Core barrier: Limited resources (finance, managerial time, digital infrastructure) and capabilities (technical skills, international market knowledge, organizational readiness) dominate as obstacles.
• Effects: These barriers negatively affect SMEs’ export performance, innovation outcomes, digital transformation progress, and ability to manage cross-border operations.
• Mitigating mechanisms: Strategic alliances, inter-firm networks, collaborations with intermediaries, and partnerships with platform providers can pool resources, transfer know-how, and reduce costs/risks of cross-border technology use.
• Contribution: The study synthesizes scattered findings across 95 FT50 journal articles to map barrier types, their effects, and promising solutions for the SME context.

Data & Methods

• Study type: Systematic literature review following PRISMA guidelines.
• Sample: 95 articles published in FT50 journals.
• Analysis approach: Inductive thematic analysis to extract, group, and synthesize barriers, effects, and solutions at the intersection of internationalization and technology adoption.
• Scope limitations: Focused on high-impact journals (FT50), which may bias toward certain regions, industries, or methodological approaches reported in those outlets.

Implications for AI Economics

• Adoption bottlenecks for AI: Resource and capability constraints identified apply directly to AI adoption—SMEs face high upfront costs, shortages of AI-skilled labor, data infrastructure gaps, and uncertainty about ROI for AI applied to international markets.
• Productivity and trade effects: Slow or uneven AI uptake among SMEs may limit aggregate gains from digital trade and manufacturing/service productivity growth in open economies. This can affect comparative advantage dynamics and international value-chain participation.
• Role of networks and partnerships: Alliances (with larger firms, platforms, research organizations) can lower entry costs into AI, enable access to shared data and compute, and speed diffusion of best practices across borders—implying network-based policies and incentives could accelerate SME AI internationalization.
• Policy design: Effective interventions may include subsidized access to AI infrastructure (cloud credits, shared labs), targeted skills training, export-support programs that bundle digitalization assistance, and incentives for data-sharing consortia that respect cross-border data governance.
• Market structure and competition: If large multinationals capture most AI capabilities, SMEs may be pushed into dependent supplier roles unless collaboration and platform access are enabled—raising concerns about market concentration and implications for dynamic competition.
• Research needs for AI economics: More empirical work linking SME-level AI adoption to export performance, industry-level trade patterns, and welfare outcomes; evaluation of policy experiments (e.g., public–private AI hubs); and cross-country comparisons of how data regulations affect SME international AI use.