構繪研究影響力科學的不同世代：對衡量指標、框架與預測方法的範域文獻回顧

Mapping the Generations of Research Impact Science: A Scoping Review of Metrics, Frameworks, and Predictive Approaches

本文對研究影響力評估框架進行系統性範域文獻回顧，採Arksey與O’Malley（2005）提出、Levac等人（2010）改良、並對應PRISMA-ScR準則的方法，查找Scopus、Web of Science、PubMed、IEEE Xplore、Google Scholar以及灰色文獻後，篩選出有關評估框架、標準和方法之139篇研究論文。研究影響力科學的發展從引文測量轉變至複雜的資料驅動，分為四個世代：第一代書目計量著重於出版生產力；第二代多維框架納入社會、經濟和政策指標；第三代資料驅動整合機器學習和預測分析；第四代另類計量掌握即時數位參與。主要挑戰包括標準化受限、地區性偏誤，以及新興技術運用不足，故本研究指出需建立預測性多維框架和標準化分類，提升影響力評估的擴展性和前瞻性，有助精進評估方法，以面對社會挑戰並指引策略研究之經費補助。

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 underexploitation 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.