سینا طریقی

Objective : This study examines how different research and development (R&D) funding sources—business enterprise R&D (BERD), higher education R&D (HERD), and government-financed R&D (GOVERD)—influence national innovation and competitiveness, measured by the Global Innovation Index (GII) and Global Competitiveness Index (GCI). This study addresses a critical gap by moving beyond aggregate R&D spending to examine how funding composition shapes innovation capacity.  Methodology: Ordinary least squares (OLS) regression analysis was conducted on a sample of 47 countries using data from OECD R&D Statistics, World Bank Development Indicators, and GII/GCI reports. Missing values (< 3%) were imputed using mean substitution. Diagnostic tests were applied to verify normality, minimal multicollinearity, and compliance with heteroscedasticity assumptions. Two models were subsequently estimated with GII and GCI as dependent variables. Results : For the GII model (R² = 0.601, F = 12.37, p < 0.001), overall GERD intensity was significantly positive (β = 10.54, p = 0.040), while disaggregated components (BERD, HERD, GOVERD) showed no individual significance due to multicollinearity. GDP per capita was robust (β = 8.78e-05, p = 0.019). For the GCI model (R² = 0.651, F = 15.31, p < 0.001), GERD was non-significant; GDP per capita remained the strongest predictor (β = 0.0003, p < 0.001). Regression assumptions were satisfied (Jarque-Bera p > 0.44; Durbin-Watson ≈ 2.1). Conclusion : Overall R&D intensity significantly influences innovation, but relationships with disaggregated sources are complex. The importance of GDP per capita shows that the quality of institutions and the ability to absorb new ideas are both important for turning R&D spending into innovation and competitiveness benefits. Policymakers should prioritize both R&D funding levels and the institutional environment enabling effective R&D utilization.