Mapping Tools, Techniques, and Applications for Research Excellence: A Bibliometric Study

Sushil Kumar Sahoo; Szabolcs  Fischer; Supriya  Sahu; Bibhuti Bhusan  Choudhury; Prasant Ranjan Dhal; Ipsita Dhar

doi:10.31181/sems41202672s

Authors

Sushil Kumar Sahoo Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India Author https://orcid.org/0000-0002-8551-7353
Szabolcs Fischer Department of Transport Infrastructure and Water Resources Engineering, Széchenyi István University, Győr, Hungary Author https://orcid.org/0000-0001-7298-9960
Supriya Sahu Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India Author https://orcid.org/0000-0002-1823-9568
Bibhuti Bhusan Choudhury Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India Author https://orcid.org/0000-0002-2990-3911
Prasant Ranjan Dhal Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India Author https://orcid.org/0000-0002-7402-2187
Ipsita Dhar Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang 759146, Odisha, India Author https://orcid.org/0009-0007-4086-8137

DOI:

https://doi.org/10.31181/sems41202672s

Keywords:

Research Excellence, Bibliometric Analysis, Research Evaluation Framework, Interdisciplinary Research, Artificial Intelligence, Quantitative Method, Qualitative Method

Abstract

The quality of research has become a crucial measure of quality, impactful and innovative scientific results in the contemporary knowledge-based age. This paper gives an in-depth examination of research tools, techniques, applications, and solutions that help in the realization of research excellence. The systematic literature review and bibliometric methodology were used, however, with the help of the Dimensions.ai database, where 705 relevant articles were identified after inclusion and exclusion criteria were followed. The research analyzes quantitative, qualitative, mixed-method, and bibliometric methods, as well as sophisticated digital applications and AI-related methodologies. The results show that the volume of research and citations has increased especially since 2020, which is evidence of the growing importance of data analytics, cross-disciplinary teamwork, and open science practices. Moreover, the paper determines the major challenges, such as the dependency on the metric, resource constraints, and complexity of the methods. This paper suggests a merged system of tools, methodologies and ethical practices that can improve the quality of research, its visibility and impact on society and provides good knowledge to researchers, institutions, and policymakers.

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References

Riad, A., Alkasaby, M., Changchroenkul, N., & Koščík, M. (2026). National-, institutional-, and individual-level determinants of psychiatric research excellence: Analysis of Stanford-Elsevier lists of the top 2% scholars worldwide (2017-2023). Asian Journal of Psychiatry, 104826. https://doi.org/10.1016/j.ajp.2025.104826

Haupt, F., Senge, J. F., von Tengg-Kobligk, H., & Bosbach, W. A. (2026). Enabling transparent research evaluation: A method for historical RCR retrieval using public NIH metadata. PloS one, 21(1), e0340697. https://doi.org/10.1371/journal.pone.0340697

Pinar, M., & Horne, T. J. (2022). Assessing research excellence: evaluating the research excellence framework. Research Evaluation, 31(2), 173-187. https://doi.org/10.1093/reseval/rvab042

Thelwall, M., Kousha, K., Wilson, P., Makita, M., Abdoli, M., Stuart, E., & Cancellieri, M. (2023). Predicting article quality scores with machine learning: The UK Research Excellence Framework. Quantitative Science Studies, 4(2), 547-573. https://doi.org/10.1162/qss_a_00258

Grzeszczyk, T. A. (2026). Support Vector Machines for Evaluating Research Excellence in Higher Education. In The Future of Work: How Technology is Transforming Jobs and Skills (pp. 847-856). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-87372-0_73

Sørensen, M. P., Bloch, C., & Young, M. (2016). Excellence in the knowledge-based economy: from scientific to research excellence. European Journal of Higher Education, 6(3), 217-236. https://doi.org/10.1080/21568235.2015.1015106

Wigren-Kristoferson, C., Gabrielsson, J., & Kitagawa, F. (2011). Mind the gap and bridge the gap: Research excellence and diffusion of academic knowledge in Sweden. Science and Public Policy, 38(6), 481-492. https://doi.org/10.3152/030234211X12960315267859

Bonaccorsi, A., Cicero, T., Haddawy, P., & Hassan, S. U. (2017). Explaining the transatlantic gap in research excellence. Scientometrics, 110(1), 217-241. https://doi.org/10.1007/s11192-016-2180-2

Pinar, M., & Unlu, E. (2020). Evaluating the potential effect of the increased importance of the impact component in the Research Excellence Framework of the UK. British Educational Research Journal, 46(1), 140-160. https://doi.org/10.1002/berj.3572

Torrance, H. (2020). The research excellence framework in the United Kingdom: Processes, consequences, and incentives to engage. Qualitative Inquiry, 26(7), 771-779. https://doi.org/10.1177/10778004198787

Antes, A. L., Kuykendall, A., & DuBois, J. M. (2019). Leading for research excellence and integrity: A qualitative investigation of the relationship-building practices of exemplary principal investigators. Accountability in research, 26(3), 198-226. https://doi.org/10.1080/08989621.2019.1611429

Lee, D. J., Stvilia, B., Hegde, N., & Malneedi, S. N. R. (2025). Exploring doctoral student researchers' use and adoption of research information management systems. Library & Information Science Research, 47(3), 101369. https://doi.org/10.1016/j.lisr.2025.101369

García-Villar, C. (2021). A critical review on altmetrics: can we measure the social impact factor?. Insights into Imaging, 12(1), 92. https://doi.org/10.1186/s13244-021-01033-2

Sahoo, S. K., Choudhury, B. B., & Dhal, P. R. (2024). A bibliometric analysis of material selection using MCDM methods: trends and insights. Spectrum of mechanical engineering and operational research, 1(1), 189-205. https://doi.org/10.31181/smeor11202417

Bahari, A. (2025). Balancing syntactic complexity and clarity: the role of AI in enhancing academic writing proficiency. Saudi Journal of Language Studies, 5(4), 271-290. https://doi.org/10.1108/SJLS-10-2024-0062

Dilber, C., & Yoşumaz, İ. (2026). The Impact of Language Translation on Plagiarism Rates: Evidence from Turnitin, iThenticate, and Grammarly. Journal of Academic Ethics, 24(1), 4. https://doi.org/10.1007/s10805-025-09681-5

Patterson, B. (2025). Can AI help with that? The limitations of AI tools for information discovery, search and reviews. Journal of Electronic Resources in Medical Libraries, 22(1-2), 56-59. https://doi.org/10.1080/15424065.2025.2496622

Lim, W. M. (2025). What is quantitative research? An overview and guidelines. Australasian Marketing Journal, 33(3), 325-348. https://doi.org/10.1177/14413582241264622

Slater, P., & Hasson, F. (2025). Quantitative research designs, hierarchy of evidence and validity. Journal of Psychiatric and Mental Health Nursing, 32(3), 656-660. https://doi.org/10.1111/jpm.13135

Li, H., Wang, Y., Luo, S., & Huang, C. (2025). The influence of GenAI on the effectiveness of argumentative writing in higher education: Evidence from a quasi-experimental study in China. Journal of Asian Public Policy, 18(2), 405-430. https://doi.org/10.1080/17516234.2024.2363128

Kumar, R. (2025). Bibliometric analysis: comprehensive insights into tools, techniques, applications, and solutions for research excellence. Spectrum of Engineering and Management Sciences, 3(1), 45-62. https://doi.org/10.31181/sems31202535k

Steinkamp, M. W., & Maehr, M. L. (1983). Affect, ability, and science achievement: A quantitative synthesis of correlational research. Review of Educational Research, 53(3), 369-396. https://doi.org/10.3102/00346543053003369

Njeri Mugwe, J., & Runo, S. (2026). Nonexperimental Designs 1: Surveys. In Research Methodology in Agricultural Sciences (pp. 293-312). Springer Nature Singapore. https://doi.org/10.1007/978-981-95-1892-0_12

Rees, C. E., & Ottrey, E. (2026). “Lives and times”: the case for qualitative longitudinal research in anatomical sciences education. Anatomical Sciences Education, 19(2), 218-230. https://doi.org/10.1002/ase.2514

Martínez-Sánchez, I., Romero-García, M., Alonso-Fernández, S., Martínez-Momblan, M. A., Lleberia, J., & Puig-Llobet, M. (2026). Nurse Educators’ Self-Reported Level of Teaching Competence and Its Correlation with Sociodemographic, Professional, Training and Research Variables: A Cross-Sectional Multicentre Study. Nursing Reports, 16(2), 41. https://doi.org/10.3390/nursrep16020041

Mutanana, N., & Shoko, C. T. (2026). Addressing the Efficacy of Quality in Qualitative Research: A Review of the Current Discourse. International Journal of Qualitative Methods, 25, 16094069261432368. https://doi.org/10.1177/1609406926143236

Dougherty, D. (2017). Grounded theory research methods. The Blackwell companion to organizations, 849-866. https://doi.org/10.1002/9781405164061.ch37

Almusaed, A., Almssad, A., & Yitmen, I. (2025). Ethnography and Case Study Methods. In Practice of Research Methodology in Civil Engineering and Architecture: A Comprehensive Guide (pp. 317-353). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97393-2_10

Downey, G., Dalidowicz, M., & Mason, P. H. (2015). Apprenticeship as method: embodied learning in ethnographic practice. Qualitative Research, 15(2), 183-200. https://doi.org/10.1177/1468794114543400

Mahadevan, J., & Moore, F. (2023). A framework for a more reflexive engagement with ethnography in International Business Studies. Journal of World Business, 58(4), 101424. https://doi.org/10.1016/j.jwb.2022.101424

Pino Gavidia, L. A., & Adu, J. (2022). Critical narrative inquiry: An examination of a methodological approach. International Journal of Qualitative Methods, 21, 16094069221081594. https://doi.org/10.1177/160940692210815

Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International journal of qualitative methods, 22, 16094069231205789. https://doi.org/10.1177/16094069231205789

Best, S., & Williams, S. J. (2025). Improving the way we do action research in quality improvement. Production Planning & Control, 36(2), 165-176. https://doi.org/10.1080/09537287.2024.2315154

Christodoulou, M. (2025). Grounded theory as a framework for explanatory sequential mixed-method design, an example from educational research. Quality & Quantity, 1-17. https://doi.org/10.1007/s11135-025-02214-7

Chiarini, A., & Kumar, M. (2022). What is Quality 4.0? An exploratory sequential mixed methods study of Italian manufacturing companies. International Journal of Production Research, 60(16), 4890-4910. https://doi.org/10.1080/00207543.2021.1942285

Bhana, A. (2024). Unlocking the power of convergent parallel designs and triangulation for enhanced management and leadership research: A comprehensive theoretical exploration. Asian Journal of Management, Entrepreneurship and Social Science, 4(04), 1770-1793. https://doi.org/10.63922/ajmesc.v4i04.864

Johnson, R. E., Grove, A. L., & Clarke, A. (2019). Pillar integration process: A joint display technique to integrate data in mixed methods research. Journal of Mixed Methods Research, 13(3), 301-320. https://doi.org/10.1177/15586898177431

Sim, C. L., Wong, T. A., Sin, K. Y., & Sivakumaran, V. M. (2024). Pragmatism as a paradigm for quality management research in bridging academic-practitioner gaps. International Journal of Quality and Service Sciences, 16(2), 330-342. https://doi.org/10.1108/IJQSS-07-2024-192

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and policy in mental health and mental health services research, 42(5), 533-544. https://doi.org/10.1007/s10488-013-0528-y

Johnson, R. E., Grove, A. L., & Clarke, A. (2019). Pillar integration process: A joint display technique to integrate data in mixed methods research. Journal of Mixed Methods Research, 13(3), 301-320. https://doi.org/10.1177/1558689817743108

Tang, G. (2025). Using mixed methods research to study research integrity: Current status, issues, and guidelines. Accountability in Research, 32(5), 807–828. https://doi.org/10.1080/08989621.2024.2449041

Coker, D. C., Bobek, V., Kaaki, W., & Dawkins, E. (2025). Quantity and Quality in Social Research (Mixed Methods Research): A Framework for Integration. In A. Akande (Ed.), Modern Cross-Cultural Management. Diversity and Inclusion Research. Springer, Cham. https://doi.org/10.1007/978-3-031-82900-0_9

Sahoo, S. K., Sahu, S., Choudhury, B. B., Dhal, P. R., & Dhar, I. (2026). Transforming Decision-Making in Generation Z Education: A Systematic and Bibliometric Review of Artificial Intelligence. Journal of Contemporary Decision Science, 2(1), 114-130.

Sahoo, S. K., Goswami, S. S., Božanić, D., & Mitra, S. (2025). Evaluating Green Economy Strategies Through Multi-Criteria Decision Analysis: A Systematic Review. International Journal of Economic Sciences, 14(1), 385-407. https://doi.org/10.31181/ijes1412025245

Özekenci, E. K., Onat, K. T., & Özekenci, S. Y. (2027). Emerging Trends and Research Insights in Fuzzy Multi-Criteria Decision-Making Applications for Logistics Location Selection: A Comprehensive Bibliometric Analysis (1982-2025). Spectrum of Operational Research, 1-20. https://doi.org/10.31181/sor202772

Acuña-Muñoz, M. J., Carvajal-Trujillo, E., Orts-Cardador, J. J., & Liébana-Cabanillas, F. (2026). A five-decade review of academic research on healthcare supply chain: a bibliometric approach using co-word analysis and bibliographic coupling. International Journal of Health Care Quality Assurance, 39(1), 1-21. https://doi.org/10.1108/IJHCQA-01-2024-0003

Huang, Y., Yang, F., Lan, M., Reniers, G., & Ni, X. (2026). Visualized analysis on thermal runaway related research trends for lithium-ion batteries: A bibliometric study. Journal of Energy Storage, 142, 119646. https://doi.org/10.1016/j.est.2025.119646

Gao, D., Cai, J., & Wu, K. (2025). The smart green tide: A bibliometric analysis of AI and renewable energy transition. Energy Reports, 13, 5290-5304. https://doi.org/10.1016/j.egyr.2025.04.052

Blackburn, R., Dibb, S., & Tonks, I. (2025). The Effect of Research Evaluations on Inclusion: A Focus on University Submissions to the Research Excellence Framework. Available at SSRN 5125250. http://dx.doi.org/10.2139/ssrn.5125250

Loo, B. P. (2024). Research excellence in a world of change. Transactions of the Institute of British Geographers, e12729. https://doi.org/10.1111/tran.12729

Qiu, L., Zhou, E., Yu, T., & Smyth, N. (2019). Data analytics and research evaluation. Library Hi Tech News, 36(4), 1-6. https://doi.org/10.1108/LHTN-11-2018-0073

Widimsky, P., Stetkarova, I., & Tousek, P. (2023). The importance of interdisciplinary research. European Heart Journal Supplements, 25(Supplement_E), E1-E2. https://doi.org/10.1093/eurheartjsupp/suad108

Conroy, J. C., & Smith, R. (2017). The ethics of research excellence. Journal of Philosophy of Education, 51(4), 693-708. https://doi.org/10.1111/1467-9752.12249

Khosravi, A., & Chavan, M. (2012). A comprehensive view on research excellence. Total quality management & business excellence, 23(5-6), 507-521. https://doi.org/10.1080/14783363.2012.669537