The Commonplace
Home Papers Evidence Explore Trends Syntheses Digests References Docs 🎲 Workforce Futures
← Papers
Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

Traditional competitiveness theory is showing its age: Porter’s diamond accounts for only around 55–60% of growth variation as AI, platforms and value‑chain fragmentation reshape economies, prompting a shift to a 'Sustainable Competitive Advantages' model that pairs strategic protectionism with industrial cooperation.

Transformation of the national competitiveness paradigm: from M. Porter’s model to sustainable competitive advantages
V. L. Abramov, A. B. Otarbayeva, Z. A. Khitakhunov · December 14, 2025 · Bulletin of Turan University
openalex theoretical low evidence 7/10 relevance Full text usable extracted full text DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation:

  1. V. L. Abramov provider ID
  2. A. B. Otarbayeva provider ID
  3. Z. A. Khitakhunov provider ID

Semantic Scholar

Latest observation:

  1. V. Abramov provider ID
  2. A. Otarbayeva provider ID
  3. Z. A. Khitakhunov provider ID
Porter’s diamond explains only about 55–60% of growth variation and fails to capture structural shifts from digitalization, AI, platforms, and fragmented value chains, so the paper proposes a Sustainable Competitive Advantages (SCA) framework that integrates industrial policy, macroeconomic regulation, and regional cooperation.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

The article examines the methodological limitations of traditional competitiveness theories amid the transition to a polycentric global economy. Using a four-dimensional analytical framework and systemic approach, the study demonstrates that M. Porter’s paradigm, while valuable at micro level, proves insufficient for explaining contemporary structural transformations marked by digitalization, economic sovereignty, and value chain fragmentation. Empirical analysis reveals that Porter’s “diamond” determinants explain only 55–60% of economic growth variations, indicating theoretical gaps. The research highlights digital transformation as a key challenge, fundamentally reconfiguring all components of traditional models through AI, platforms, and new business models. As a theoretical alternative, the article proposes the concept of Sustainable Competitive Advantages (SCA), integrating macroeconomic regulation, industrial policy, and regional cooperation. Using the EAEU and comparative cases (China, Singapore), the study demonstrates the effectiveness of strategic protectionism and industrial cooperation. The findings have practical significance for developing new competitiveness indicators and policy tools adapted to polycentric realities.

Summary

Main Finding

Porter’s “diamond” model remains informative at the micro/industry level but is insufficient to explain national competitiveness in the contemporary, digitally driven, polycentric global economy. The paper argues for a new paradigm of Sustainable Competitive Advantages (SCA) that integrates macroeconomic regulation, industrial policy, technological sovereignty, and regional value‑chain design. Empirically, the authors find that Porter’s determinants explain only ~55–60% of cross‑country variation in economic growth, indicating substantive theoretical gaps that digitalization and AI exacerbate.

Key Points

  • Conceptual shift: competitiveness must be reconceptualized from static/productivity‑centred measures to emergent, system‑level SCA built via purposeful policy, ecosystems, and technological sovereignty.
  • Polycentricity: the rise of multiple centres of economic power, regional blocs, and strategic autonomy reduces the explanatory power of liberal, unipolar models.
  • Porter limitations: underestimates the active role of the state, macroeconomic/institutional context, and the strategic design of industrial structure in a fragmented global value‑chain environment.
  • Digitalization & AI reconfigure all classical determinants:
    • Factor conditions → digital assets (data, AI, cloud, platforms), digital infrastructure (5G, data centres).
    • Demand conditions → personalized, service‑centric, AI‑driven demand.
    • Related/supporting industries → global digital ecosystems, platform/API integration.
    • Firm strategy/rivalry → data‑driven strategies, hybrid AI+human models, networked collaboration.
  • Labor and skills: authors cite estimates that AI/digitalization can automate ~60–70% of tasks (especially high‑skill), making large‑scale reskilling imperative.
  • Policy alternatives: strategic industrial policy, technological sovereignty, regional integration (e.g., EAEU cases) and strategic protectionism can be effective tools for securing SCA; comparative examples include China and Singapore.
  • Measurement and methodological gap: productivity indicators alone are inadequate — need new indicators capturing digital assets, platform ecosystems, value‑capture, and resilience to shocks.

Data & Methods

  • Theoretical/methodological approach: systemic, multi‑level framework treating competitiveness as an emergent property from nonlinear macro–meso–micro interactions.
  • Analytic tools: comparative‑historical analysis; critical theoretical and methodological deconstruction of Porter; institutional analysis; structural‑functional analysis.
  • Four‑dimensional analytical framework developed: factor endogeneity, institutional embeddedness, technological dynamics, and level of economic analysis.
  • Empirical basis: literature corpus (classical and contemporary), statistics from multilateral institutions (World Bank, IMF, WEF), and strategic policy documents of national governments and integration associations. Comparative cases include China, Singapore, and the EAEU (used to illustrate industrial cooperation and strategic protectionism).
  • Quantitative finding referenced: Porter’s diamond determinants explain roughly 55–60% of observed variation in economic growth (authors’ empirical summary; details of estimation not included in excerpt).

Implications for AI Economics

  • Measurement and indicators:
    • Develop national‑level metrics beyond TFP and productivity: digital asset stocks (data, models), AI adoption intensity, platform concentration, data governance index, digital infrastructure resilience, and metrics of technological sovereignty.
    • Measure value capture in GVCs: share of intangibles/value added retained domestically (software, AI services, IP).
  • Modeling:
    • Move to endogenous‑technology, multi‑level models linking firm behaviour, platform/network effects, industry structure, and macro policy. Consider agent‑based or network models to capture polycentric interactions and non‑linearities.
    • Incorporate trade frictions, data‑localization policies, and strategic protectionism as endogenous policy choices affecting technology diffusion and comparative advantage.
  • Empirical research priorities:
    • Firm‑ and sector‑level microdata on AI adoption, complementarities (skills, organizational change), and productivity effects to validate macro claims.
    • Causal assessment of industrial policy and protectionist measures on AI ecosystem development and long‑run competitiveness.
    • Evaluate reskilling and labor market transitions from AI automation across skill strata and regions.
  • Policy design and evaluation:
    • Recognize state roles: build digital ecosystems (infrastructure, skills, regulation), support strategic industries, and design regional cooperation to secure resilient value chains.
    • Design AI policy instruments that target both capability building (R&D, data access, standards) and distributional outcomes (reskilling, social protection).
    • Balance openness and technological sovereignty: carefully calibrated data‑governance and cross‑border AI cooperation to avoid self‑harmful fragmentation while protecting strategic capabilities.
  • Distributional and geopolitical considerations:
    • AI economics must account for how platformization and data concentration reshape rents and international bargaining power; modeling should include inequality and geopolitical spillovers.
  • Limits & research cautions:
    • The paper is largely conceptual/systemic with macro‑level evidence and illustrative cases; more granular causal evidence is needed about how specific AI investments and policies map into national SCA.
    • Replication of the cited 55–60% explanatory figure should be sought with transparent econometric specifications and alternative datasets.

Reference (from the article header) - Abramov V.L., Otarbayeva A.B., Khitakhunov Z.A., “Transformation of the national competitiveness paradigm: from M. Porter’s model to sustainable competitive advantages”, Vestnik universiteta “Turan”, 2025, No. 4(108). DOI: https://doi.org/10.46914/1562-2959-2025-1-4-234-246

If you want, I can (a) extract testable hypotheses from the paper for econometric work, (b) propose specific indicators to operationalize SCA for cross‑country analysis, or (c) draft an empirical design to validate the 55–60% claim. Which would be most useful?

Assessment

Paper Typetheoretical Evidence Strengthlow — Findings rest on cross-sectional/comparative macro-level analysis and descriptive empirical correlations (e.g., Porter’s diamond explaining ~55–60% of growth variation) without a clear causal identification strategy, counterfactuals, or robustness checks; potential confounding, measurement choices, and selection of cases limit causal inference. Methods Rigormedium — The paper advances a coherent four-dimensional analytical framework and applies comparative empirical illustrations (EAEU, China, Singapore), but it provides limited detail on empirical methods, sampling, model specification, control variables, or sensitivity analyses; rigor is adequate for theory-building but insufficient for strong empirical claims. SampleComparative, macro-level case analysis focused on the Eurasian Economic Union (EAEU) with comparative references to China and Singapore; uses national/regional competitiveness indicators, growth outcome measures, and qualitative evidence on digitalization, value-chain fragmentation, and industrial policy (time period and sample size not clearly specified). Themesgovernance innovation adoption GeneralizabilityCase-based sample (EAEU, China, Singapore) may not generalize to other regions or economies, Macro-level indicators obscure firm- and sector-level heterogeneity in AI adoption and impacts, Rapid pace of digital/AI change may make findings time-sensitive, Comparative/descriptive design susceptible to omitted variable bias and context-specific policy effects

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Empirical analysis reveals that Porter’s “diamond” determinants explain only 55–60% of economic growth variations, indicating theoretical gaps. Fiscal And Macroeconomic negative economic growth variations
Reading fidelity high
Study strength medium
55–60%
0.12
M. Porter’s paradigm, while valuable at the micro level, proves insufficient for explaining contemporary structural transformations marked by digitalization, economic sovereignty, and value chain fragmentation. Market Structure negative adequacy of Porter’s paradigm to explain structural economic transformations
Reading fidelity high
Study strength medium
not reported
0.12
Porter’s paradigm is valuable at the micro level. Firm Productivity positive utility of Porter’s paradigm at micro (firm/industry) level
Reading fidelity high
Study strength medium
not reported
0.12
Digital transformation is a key challenge that fundamentally reconfigures all components of traditional competitiveness models through AI, platforms, and new business models. Innovation Output negative degree of reconfiguration of traditional competitiveness model components
Reading fidelity high
Study strength medium
not reported
0.12
As a theoretical alternative, the article proposes the concept of Sustainable Competitive Advantages (SCA), integrating macroeconomic regulation, industrial policy, and regional cooperation. Governance And Regulation positive proposed policy/theoretical framework (SCA) integrating macro regulation, industrial policy, regional cooperation
Reading fidelity high
Study strength speculative
not reported
0.02
Using the EAEU and comparative cases (China, Singapore), the study demonstrates the effectiveness of strategic protectionism and industrial cooperation. Firm Productivity positive effectiveness of strategic protectionism and industrial cooperation in enhancing competitiveness
Reading fidelity medium
Study strength low
not reported
0.04
The findings have practical significance for developing new competitiveness indicators and policy tools adapted to polycentric realities. Governance And Regulation positive development of new competitiveness indicators and policy tools
Reading fidelity high
Study strength speculative
not reported
0.02
The paper employs a four-dimensional analytical framework and a systemic approach to analyze competitiveness in a polycentric global economy. Other null_result research methodology used
Reading fidelity high
Study strength speculative
not reported
0.02

Notes