Knowledge Flows and Innovation Capacity: A Reproducible Multi-Criteria Decision Analysis of the G7 and Türkiye

The macroeconomic performance of nations provides valuable insights into the knowledge economy and the governance structures that sustain its development.This study formalizes a framework for evaluating knowledge flows and innovation capacity through multi-criteria decision analysis (MCDA) using open World Bank data.The analysis employs the Logarithmic Decomposition of Criteria Importance (LODECI) method in conjunction with the Preference Selection Index (PSI) to determine objective weights, while the Weighted Euclidean Distance-Based Approach (WEDBA) is applied to rank the G7 countries and Trkiye in 2023.Knowledge flows, as represented by exports and foreign direct investment (FDI), serve as proxies for cross-border knowledge exchange, while inflation, unemployment, and economic growth are assessed within a reproducible, policy-driven framework.The weighting procedure assigns the greatest aggregate importance to inflation and the least to unemployment.The resulting rankings place the United States first, followed by Japan in second place, Trkiye fourth, and the United Kingdom last.The analysis further highlights how factors such as price stability, external openness, and investment dynamics shape national knowledge creation, diffusion, and organizational learning processes.By focusing on the utilization of open data, explicit knowledge representation, and transparent multi-criteria methodologies, the proposed framework strengthens digital knowledge infrastructures and facilitates actionable cross-country benchmarking.The findings have important policy implications, particularly in understanding how national macroeconomic variables influence innovation capacity.The framework is designed to be extensible, allowing for future adaptation to evaluate additional indicators, such as R&D intensity, high-tech export shares, and patenting activity.Furthermore, the approach is structured to support replication across various regions and timeframes, ensuring its broad applicability and scalability.