@article{4848,
  author = {Radhakrishnan N},
  title = {Research Impact of the Top Ten Universities in the 2025 QS Asia University Rankings: A Scientometric Analysis},
  journal = {Journal of Science &Technology Metrics},
  year = {2026},
  volume = {7},
  number = {3},
  doi = {https://doi.org/10.6025/jstm/2026/7/3/115-131},
  url = {https://www.dline.info/jstm/fulltext/v7n3/jstmv7n3_1.pdf},
  abstract = {Systematic evaluation of institutional functions is essential for assessing historical performance and guiding
future development. In the contemporary higher education landscape, universities are increasingly compelled
to navigate complex national and global ranking systems characterized by rigorous indicators and stringent
weightings. Introduced in 2009, the Quacquarelli Symonds (QS) Asia University Rankings (AUR) utilizes
adapted indicators and weightings derived from the QS World University Rankings (WUR). These frameworks
enable institutions to identify strategic focus areas necessary to achieve their developmental visions. While
ranking methodologies vary, the QS framework specifically evaluates core dimensions such as research
output, teaching quality, graduate employability, and internationalization. Within the research criterion,
performance is heavily dependent on publications indexed in major academic databases. Fundamentally,
research drives the creation of new knowledge, enhancing the academic prestige and institutional growth of
both researchers and their affiliations. Accordingly, this study aims to highlight the efficacy and strategic
importance of high quality regional research in driving university success.
Scientometric methods offer a robust, structured approach to evaluating institutional research outcomes
through publication and citation metrics. To this end, this study presents a comprehensive, data-driven
analysis of the research performance of the top ten universities in the 2025 QS AUR. The analysis draws on
bibliometric data extracted from the Web of Science (WoS) database for the period 2022–2024. A consolidated
dataset was compiled from these institutional records, and Python was employed for large-scale data
cleaning, processing, and visualization, leveraging libraries such as Pandas, NumPy, and Matplotlib.
Publication productivity was evaluated through annual output and growth trajectories, while citation impact
was measured using total citations, average citations per paper, and the h-index. Furthermore, subject area
profiles were generated to map thematic contributions, and collaboration patterns were assessed by
analyzing the contributions of top authors. To establish a comparative research performance index, advanced
indicators including the Compound Annual Growth Rate (CAGR) and Z-score-based composite rankings
were computed. Visual analytics, such as trend lines and ranking plots, were utilized to provide deeper
insights into temporal dynamics and relative institutional standings. Ultimately, the findings underscore the
strategic imperative of sustained, high quality research in enhancing the global visibility and ranking
trajectories of universities, particularly within the highly competitive landscape of Asian higher education.},
}