Analysis of Research Performance and Research Workforce through Microsoft Power BI Dashboard to Support Strategic Policy Decision-Making
Main Article Content
Abstract
The quantity and quality of academic publications serve as crucial benchmarks for a university's research competitiveness. Data-driven strategic research management is indispensable for developing policies and personnel plans, vital for both retaining young researchers and managing personnel nearing retirement to ensure continuous and sustainable research productivity. This study outlines the design and development of a Microsoft Power BI-based research performance dashboard. The tool combines and displays national and international publication data alongside the age profile of the research workforce. Statistics demonstrate that the dashboard offers executives transparent insights into trends, strengths, and opportunities, thus facilitating evidence-based policy formation and strategic planning. Furthermore, it enables personnel to monitor their own performance, thereby improving research capabilities and fostering internal collaboration. The dashboard serves as an effective and sustainable tool for supporting research policies across various research organizations.
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References
Banerjee, S., Fullerton, C. E., Gaharwar, S. S., & Jaselskis, E. J. (2025). Strategic web-based data dashboards as monitoring tools for promoting organizational innovation. Buildings, 15(13), Article 2204 https://doi.org/10.3390/buildings15132204
Dia, N. J., Sieras, J. C., Khalid, S. A., Macatotong, A. H. T., Mondejar, J. M., Genotiva, E. R., & Delena, R. D. (2025). EduGuard RetainX: An advanced analytical dashboard for predicting and improving student retention in tertiary education. SoftwareX, 29. https://doi.org/10.1016/j.softx.2025.102057
Elrayah, M., & Semlali, Y. (2023). Sustainable total reward strategies for talented employees’ sustainable performance, satisfaction, and motivation: Evidence from the educational sector. Sustainability, 15(2), 1605. https://doi.org/10.3390/su15021605
Gan, Q. (2023). Study on the relationship between research incentive mechanisms and research outcome commercialization in private higher education institutions. Frontiers in Business, Economics and Management, 12(1), 73-76. https://doi.org/10.54097/fbem.v12i1.13760
Gonçalves, C. T., Angélico Gonçalves, M. J., & Campante, M. I. (2023). Developing integrated performance dashboards visualisations using Power BI as a platform. Information, 14(11), 614. https://doi.org/10.3390/info14110614
Győrffy, B., Csuka, G., Herman, P., & Török, Á. (2020). Is there a golden age in publication activity?—An analysis of age-related scholarly performance across all scientific disciplines. Scientometrics, 124(2), 1081–1097. https://doi.org/10.1007/s11192-020-03501-w
Komljenovic, J., Sellar, S. & Birch, K. (2025). Turning universities into data-driven organisations: Seven challenges and strategies. Higher Education, 89, 1369–1386. https://doi.org/10.1007/s10734-024-01277-z
Kridelbaugh, D. (2021). Succession planning: Best practices for knowledge transfer. Lab Manager. https://www.labmanager.com/succession-planning-best-practices-for-knowledge-transfer-27460
Kwiek, M., & Roszka, W. (2024). The young and the old, the fast and the slow: A large-scale study of productivity classes and rank advancement. Studies in Higher Education, 49(11), 2036–2051. https://doi.org/10.1080/03075079.2023.2288172
Lv, A. (2024). Incentive mechanisms, work engagement, and productivity of higher education teachers in China: Basis for faculty development plan. International Journal of Research Studies in Management, 12(4), 187-199. https://doi.org/10.5861/ijrsm.2024.1044
Li, X., Dong, Y., & Ai, Z. (2025). Path to intelligent evaluation: Utilizing power BI for enhanced performance insights. Computers and Education Open, 9(100271). https://doi.org/10.1016/j.caeo.2025.100271
Mahmud, D., & Ikbal, M. Z. (2024). Power BI and data analytics in financial reporting: A review of real-time dashboarding and predictive business intelligence tools. International Journal of Scientific Interdisciplinary Research, 5(2), 125–157. https://doi.org/10.63125/yg9zxt61
Martinez-Gil, J. (2023). Framework to automatically determine the quality of open data catalogs. arXiv. https://doi.org/10.48550/arXiv.2307.15464
Microsoft. (2025). Import a Power BI visual from AppSource into your workspace. Microsoft Learn. https://learn.microsoft.com/en-us/power-bi/developer/visuals/import-visual
QS Quacquarelli Symonds. (2024). QS World University Rankings 2025. QS Top Universities. https://www.topuniversities.com/world-university-rankings/2025
Sijbrandij, J. J., Hoekstra, T., Almansa, J., Peeters, M., Bültmann, U., & Reijneveld, S. A. (2020). Variance constraints strongly influenced model performance in growth mixture modeling: A simulation and empirical study. BMC Medical Research Methodology, 20(1), 1–15. https://doi.org/10.1186/s12874-020-01154-0
Times Higher Education. (2024). World University Rankings 2025. THE World University Rankings. https://www.timeshighereducation.com/world-university-rankings/2025/world-ranking
Tirupati, K. K., Joshi, A., Singh, S. P., Chhapola, A., Jain, S., & Gupta, A. (2023). Leveraging Power BI for enhanced data visualization and business intelligence. Universal Research Reports, 10(2), 676–711. https://doi.org/10.36676/urr.v10.i2.1375
White, M. (2022). Sample size in quantitative instrument validation studies: A systematic review of articles published in Scopus, 2021. Heliyon, 8, Article e12223. https://doi.org/10.1016/j.heliyon.2022.e12223
Wiltshire, D., & Alvanides, S. (2022). Ensuring the ethical use of big data: Lessons from secure data access. Heliyon, 8(2). https://doi.org/10.1016/j.heliyon.2022.e08981
Zimmer, F., Henninger, M., & Debelak, R. (2024). Sample size planning for complex study designs: A tutorial for the mlpwr package. Behavior Research Methods, 56, 4217. https://doi.org/10.3758/s13428-023-02269-0
