Advancing Country-Level Research Benchmarking: A Bibliometric Multistage Principal Component Analysis-Based Composite Index Approach

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Abstract

Research performance evaluation and ranking are crucial, as policymakers, funding institutions, administrators and researchers rely on these evaluations for resource allocation, strategic decision-making and policy formulation in research and innovation. Bibliometric data have been thoroughly used in measuring research performance and quality at the institutional level using publication and citation-related metrics. This study introduces a novel, multistage PCA-based composite index that integrates five aspects of bibliometric data, namely publication, authorship, collaboration, sponsorship and impact or citation. These aspects represent input, process, output and outcome elements of a system. This data-driven approach provides a comprehensive benchmarking tool to rank countries’ research performance. As a use case, the composite index is used to compare the performance of blockchain research between countries based on a wide selection of bibliometric data prepared to accommodate the demographic variation among countries. The ranks obtained from the composite index perform better than the h-index in differentiating lower-ranked countries by incorporating collaboration, authorship and sponsorship parameters alongside publication and citation metrics, contributing to the index’s robustness. The implications of this study suggest that research performance evaluations should consider a wider set of factors to better reflect the complexities of national research ecosystems. Future policy decisions could be enhanced by adopting such comprehensive benchmarking tools, encouraging strategic decisions such as resource allocation, talent development and collaboration initiatives.

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Kurniawan, R. D., Riza, H., Wardhani, S. S. W., Ba’Abdullah, F., & Kusumaningrum, D. (2025). Advancing Country-Level Research Benchmarking: A Bibliometric Multistage Principal Component Analysis-Based Composite Index Approach. Journal of Scientometric Research, 14(1), 16–31. https://doi.org/10.5530/jscires.20251297

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