Asian MSME research now emphasizes digital transformation and ecosystem integration, and the authors propose 'Institutional Void Bridging' to explain how firms pair in‑house digital capabilities with platform and fintech services to overcome weak institutions; the framework is conceptual and awaits empirical validation.

Main Finding

The literature on Asian MSMEs (2014–2024) has shifted from a narrow focus on firm-level resources toward digital transformation, organizational resilience, and ecosystem integration. The study synthesizes 972 Scopus publications using a hybrid bibliometric–systematic review and proposes the Asian MSME Strategic Pathway Framework, introducing the concept of Institutional Void Bridging: MSMEs strategically combine internal digital capabilities with external digital ecosystem resources (platforms, fintech, data services) to mitigate institutional constraints. The framework is conceptual (not yet empirically validated).

Key Points

• Scope: 972 Scopus-indexed publications covering 2014–2024; review follows PRISMA 2020.
• Theoretical lenses: Resource‑Based View (RBV), Institutional Theory, Dynamic Capabilities Theory.
• Evolution: Research emphasis moved from internal capabilities to:
  • digital capability and transformation,
  • organizational resilience (crisis response, continuity),
  • ecosystem/platform integration (e-commerce, marketplaces, fintech).
• Geography and impact:
  • Indonesia and India: largest publication output.
  • China: highest citation impact per publication (greater scientific influence vs. raw productivity).
• Thematic clusters (seven interconnected areas identified, examples):
• Digital capability and digital transformation
• Financial technology and access to finance
• Sustainability and green practices
• Institutional support and policy/regulation
• Organizational resilience and crisis management
• Platform/ecosystem integration and networks
• Firm-level capabilities and dynamic adaptation
• Institutional Void Bridging: a conceptual mechanism describing how MSMEs leverage internal digital assets plus external ecosystem resources to overcome gaps in formal institutions, markets, and infrastructure.
• Contribution: integrates fragmented Asian MSME research into a coherent conceptual foundation to guide future empirical work and policy.

Data & Methods

• Data source: Scopus-indexed literature (2014–2024), N = 972 publications.
• Review approach: Hybrid bibliometric–systematic literature review (B‑SLR) following PRISMA 2020 guidelines.
• Quantitative mapping: VOSviewer for bibliometric analysis (co‑authorship, co‑citation, keyword co‑occurrence, thematic clustering).
• Qualitative synthesis: Thematic coding and integration using RBV, Institutional Theory, and Dynamic Capabilities lenses to interpret clusters and derive the Strategic Pathway Framework.
• Limitation noted by authors: framework is conceptual synthesis based on reviewed literature and requires empirical validation across diverse Asian contexts.

Implications for AI Economics

• Adoption dynamics:
  • Digital transformation cluster indicates rising relevance of AI/ML adoption among MSMEs (for automation, personalization, demand forecasting). Dynamic capabilities matter for timely AI uptake.
  • Institutional Void Bridging suggests MSMEs may use AI-enabled platforms and third‑party AI services to substitute for weak local institutions (e.g., credit scoring via alternative data, AI logistics optimization).
• Productivity and inequality:
  • AI and platform-enabled services can boost MSME productivity but may widen gaps if access to digital infrastructure, data, and skills is uneven across countries/regions.
  • China’s higher citation impact highlights how research ecosystems and institutional contexts shape both AI diffusion and knowledge production.
• Finance and fintech:
  • Fintech cluster points to AI-driven credit scoring, payment, and lending platforms as key enablers of MSME finance—changing credit allocation, risk models, and market structure.
  • Economic models should account for new information frictions removed by AI (alternative data, automated underwriting) and new frictions introduced (algorithmic bias, platform fees).
• Policy and regulation:
  • Policy should target digital infrastructure, AI skills training, data governance, and regulatory sandboxes to help MSMEs safely adopt AI and digital services.
  • Support for ecosystems (platform neutrality, competition policy, interoperable data standards) can enhance Institutional Void Bridging benefits while limiting market concentration risks.
• Research agenda for AI economics:
  • Empirically validate the Strategic Pathway Framework with causal studies linking AI adoption to MSME performance under differing institutional conditions.
  • Measure heterogeneity: cross-country comparisons of AI adoption, productivity gains, access to AI-enabled fintech, and distributional effects across firm size/sectors.
  • Microdata needs: firm-level longitudinal data on AI investment, digital capabilities, platform participation, financing, and performance to estimate dynamic treatment effects.
  • Evaluate externalities: platform market power, data externalities, and systemic risks (e.g., algorithmic instability in supply chains).
• Practical considerations:
  • Interventions should combine demand-side (skills, subsidies for AI tools) and supply-side (digital infrastructure, open data) policies.
  • Encourage modular, low-cost AI tools and APIs that MSMEs can integrate without large in‑house capabilities, reducing barriers highlighted by RBV and Dynamic Capabilities theory.

If you want, I can (a) extract the bibliometric maps/key authors and journals from the study, (b) draft testable hypotheses to empirically validate the Institutional Void Bridging concept, or (c) outline microdata variables and an empirical strategy to measure AI adoption effects on MSME performance.