Mapping the Generations of Research Impact Science: A Scoping Review of Metrics, Frameworks, and Predictive Approaches
This scoping review systematically examines the evolution of research impact assessment frameworks, categorising their development into 4 generations. The study follows Arksey and O’Malley’s (2005) scoping review methodology, enhanced by Levac et al. (2010) and aligned with PRISMA-ScR guidelines. A...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
National Taiwan University
2025-06-01
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| Series: | Journal of Library and Information Studies |
| Subjects: | |
| Online Access: | https://jlis.lis.ntu.edu.tw/files/journal/j60-1.pdf |
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| Summary: | This scoping review systematically examines the evolution of research impact assessment frameworks, categorising their development into 4 generations. The study follows Arksey and O’Malley’s (2005) scoping review methodology, enhanced by Levac et al. (2010) and aligned with PRISMA-ScR guidelines. A comprehensive search was conducted across Scopus, Web of Science, PubMed, IEEE Xplore, and Google Scholar, as well as grey literature, to identify relevant studies on research impact frameworks, metrics, and methodologies. A total of 139 studies were selected based on predefined inclusion criteria, encompassing bibliometric indices, multidimensional frameworks, predictive analytics, and alternative metrics.
The findings highlight the transition from traditional citation-based measures to sophisticated, data-driven methodologies. The first generation (bibliometric indices) included 26 key metrics focusing on publication productivity. The second generation introduced 41 multidimensional frameworks incorporating societal, economic, and policy indicators. The third-generation integrated machine learning and predictive analytics to assess impact across 17 aspects and 9 data-driven factors. The fourth generation utilised 16 alternative metrics, including Altmetric Attention Scores and PlumX Metrics, to capture real-time digital engagement.
Key challenges identified include limited standardisation, regional biases, and the under-exploitation of emerging technologies, such as large language models. The study underscores the need for predictive multidimensional frameworks and standardised taxonomies to enhance scalability and foresight in impact assessment. By structuring the evolution of research impact science, this review provides actionable insights to refine assessment methodologies, ensuring their relevance for addressing societal challenges and guiding strategic research investments. |
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| ISSN: | 1606-7509 |