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Küresel Akademik Performans Sıralamalarına Doğru: Farklı Veri Tabanlarındaki Bilimsel Göstergelere Dayalı Dinamik ve Entegre Bir Karar Destek Sistemi

Yıl 2025, Cilt: 10 Sayı: 26, 154 - 174, 28.02.2025

Mahmut Baydaş

https://doi.org/10.25204/iktisad.1582267

Öz

Bu çalışma, ihmal edilen bir konu olan küresel ölçekte bireysel akademik performansa ulaşma yolunda çok kriterli karar verme (ÇKKV) metodolojilerine dayalı bir karar destek sistemi önermeyi amaçlamaktadır. Daha önceki klasik uygulama ve geçmiş çalışmaların aksine bu çalışmada farklı veri tabanlarından (Scopus, Web of Science, InCites, Google Akademik) alınan farklı bilim göstergeleri (atıf sayıları, makale sayısı ve alan bazlı etki) birleştirilmiş ve her bir kritere nesnel ağırlıklar atanmıştır. Entropi yöntemiyle ağırlıklandırılan bu göstergeler, CRADIS ve diğer alternatif yöntemlerle analiz edilmiştir. Analiz sonuçları, Q1 makale sayısı ve alan bazlı etki puanlarının yüksek öneme sahip olduğunu, buna karşın Google Akademik atıflarının daha düşük ağırlık taşıdığını göstermiştir. Akademik camiada oldukça etki bırakan Leiden manifestosunun çok göstergeli ve alan bazlılığın dikkate alınması önerisine uygun olarak bu çalışmada önerilen sistem, bireysel araştırmacı performansının dinamik (güncellenebilir) ve kapsamlı bir şekilde değerlendirilmesine de olanak tanımaktadır. Literatür veya uygulamalardaki tek yönlü ve kısıtlı performans ölçümlerle kıyaslandığında bu çalışma ciddi bir boşluğu doldurmaktadır. Dahası bu sistem taraflara isabetli ve güncellenebilir stratejik karar vermeye yardımcı olacaktır.

Anahtar Kelimeler

Çok Kriterli Karar Verme Bireysel ve küresel akademik performans cradis yöntemi entropi yöntemi

Kaynakça

  • AD Scientific Index. (2024, September 15). World Scientist and University Rankings, September 2024. https://www.adscientificindex.com/
  • Al-Hagree, S., Mohsen, A. A., Abdulrazzak, F. H., Al-Sanabani, M., Alalayah, K. M., Al-Gaphari, G., ... and Gawbah, H. (2023, 10-11 October). Universities the best performers: a cluster analysis of yemeni universities rankings. In 2023 3rd International Conference on Emerging Smart Technologies and Applications (p. 1-9). Taiz, Yemen. https://doi.org/10.1109/eSmarTA59349.2023.10293385
  • Ardil, C. (2021). Scholar index for research performance evaluation using multiple criteria decision making analysis. International Journal of Educational and Pedagogical Sciences, 13(2), 93-104.
  • Baydaş, M., Elma, O. E., and Stević, Ž. (2024). Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return. Financial Innovation, 10(1), 1-35. https://doi.org/10.1186/s40854-023-00526-x
  • Baydaş, M., Eren, T., Stević, Ž., Starčević, V., and Parlakkaya, R. (2023). Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics. PeerJ Computer Science, 9,1-24. https://doi.org/10.7717/peerj-cs.1350
  • Birkle, C., Pendlebury, D. A., Schnell, J., and Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363-376. https://doi.org/10.1162/qss_a_00018
  • Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2(2022), 1-7. https://doi.org/10.1016/j.dajour.2021.100021
  • Clarivate analytics (Incites). (2024, September 15). Incites Analysis Person, September 2024. https://incites.clarivate.com/#/analysis/0/person
  • Clarivate™. (2024, September 15). Clarivate Highly Cited Researchers, September 2024. https://clarivate.com/highly-cited-researchers/
  • Clarivate™. (2024, September 15). InCites Help Center, September 2024. https://tinyurl.com/yn34arxx Docampo, D., and Cram, L. (2019). Highly cited researchers: a moving target. Scientometrics, 118(3), 1011-1025. https://doi.org/10.1007/s11192-018-2993-2
  • Google Academic. (2024, September 15). Google Academic, September 2024. https://scholar.google.com/
  • Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., and Rafols, I. (2015). Bibliometrics: the Leiden Manifesto for research metrics. Nature, 520(7548), 429-431. https://www.nature.com/articles/520429a.pdf
  • Hyland, K. (2023). Academic publishing and the attention economy. Journal of English for Academic Purposes, 64(2023), 1-10. https://doi.org/10.1016/j.jeap.2023.101253
  • Ioannidis, J. P. A. (2024). August 2024 data-update for "Updated science-wide author databases of standardized citation indicators", Elsevier Data Repository, V7. https://doi.org/10.17632/btchxktzyw.7
  • Ioannidis, J. P. A., Boyack, K. W., and Baas, J. (2020). Updated science-wide author databases of standardized citation indicators. PLOS Biology, 18(10), 1-3. https://doi.org/10.1371/journal.pbio.3000918
  • Li, X., Wang, K., Liu, L., Xin, J., Yang, H., and Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia engineering, 26(2011), 2085-2091. https://doi.org/10.1016/j.proeng.2011.11.2410
  • Li, Z., Liechty, M., Xu, J., and Lev, B. (2014). A fuzzy multi-criteria group decision making method for individual research output evaluation with maximum consensus. Knowledge-Based Systems, 56, 253-263. https://doi.org/10.1016/j.knosys.2013.11.018
  • Maral, M. (2024). Research performance of higher education in OECD countries: A hybrid multi-criteria decision-making approach. SAGE Open, 14(2), 1-20. https://doi.org/10.1177/2158244024125775
  • Markusova, V.A., Libkind, A.N., Zolotova, A.V., and Kotelnikova, N.A. (2023). Priority areas of scientific cooperation between scientists of Russia, Iran, India, and Turkey: bibliometric analysis according to the InCites database (2011–2021). Automatic Documentation and Mathematical Linguistics, 57(5), 274-283. https://doi.org/10.3103/S0005105523050047
  • Olcay, G. A., and Bulu, M. (2017). Is measuring the knowledge creation of universities possible?: A review of university rankings. Technological Forecasting and Social Change, 123, 153-160. https://doi.org/10.1016/j.techfore.2016.03.029
  • Parker, C. (2000). Performance measurement, Work Study, 49(2), 63-66. https://doi.org/10.1108/00438020010311197
  • Potter, R. W., Kovač, M., and Adams, J. (2024). Tracking changes in CNCI: the complementarity of standard, collaboration and fractional CNCI in understanding and evaluating research performance. Scientometrics, 129, 1-14. https://doi.org/10.1007/s11192-024-05028-w
  • Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 1-59. https://doi.org/10.3390/publications9010012
  • Puška A, Stević Ž., and Pamučar D. (2022). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 24(9), 11195-11225. https://doi.org/10.1007/s10668-021-01902-2
  • Puška A., and Stojanović, I. 2022. Fuzzy multi-criteria analyses on green supplier selection in an agrifood company. Journal of Intelligent Management Decision 1(1),2–16. https://doi.org/10.56578/jimd010102
  • Scopus (Elsevier). (2024, September 5). Scopus Search, September 2024. https://www.scopus.com/search/form.uri#basic
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  • Soh, K. (2017). The seven deadly sins of world university ranking: A summary from several papers. Journal of Higher Education Policy and Management, 39(1), 104-115. http://dx.doi.org/10.1080/1360080X.2016.1254431
  • Stewart, I. (2012). In pursuit of the unknown: 17 equations that changed the world. https://doi.org/10.1007/s00283-012-9303-2
  • Szluka, P., Csajbók, E., and Győrffy, B. (2023). Relationship between bibliometric indicators and university ranking positions. Scientific Reports, 13(1), 14193. https://doi.org/10.1038/s41598-023-35306-1
  • Tuan, N., Hue, T., Lien, L., Thao, T., Quyet, N., Van, L., and Anh, L. (2020). A new integrated MCDM approach for lecturers’ research productivity evaluation. Decision Science Letters, 9(3), 355-364. https://doi.org/10.5267/j.dsl.2020.5.001
  • URAP (2023, October 28). 2023-2024 URAP Türkiye sıralaması basın açıklaması, October 2023. https://tinyurl.com/2ujta97z
  • Wang, Z., and Rangaiah, G.P. (2017). Application and analysis of methods for selecting an optimal solution from the Pareto-Optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research, 56(2), 560-574. https://doi.org/10.1021/acs.iecr.6b03453
  • Wang, Z., Parhi, S. S., Rangaiah, G. P., and Jana, A. K. (2020). Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in chemical engineering applications. Industrial & Engineering Chemistry Research, 59(33), 14850-14867. https://doi.org/10.1021/acs.iecr.0c00969
  • Wu, J., Sun, J., Liang, L., and Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165. https://doi.org/10.1016/j.eswa.2010.10.046
  • YÖK Akademik. (2024, September 23). Yüksek Öğretim Akademik Arama, September, 2024. https://akademik.yok.gov.tr/AkademikArama/

Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases

Yıl 2025, Cilt: 10 Sayı: 26, 154 - 174, 28.02.2025

Mahmut Baydaş

https://doi.org/10.25204/iktisad.1582267

Öz

This study aims to propose a decision support system based on multi-criteria decision-making (MCDM) methodologies in order to reach individual and global-scale academic performance, which is a neglected subject. Unlike previous classical applications and past studies, in this study, different science indicators (citation counts, article counts, and field-based impact) taken from different databases (Scopus, Web of Science, InCites, Google Scholar) were combined, and objective weights were assigned to each criterion. These indicators, weighted with the entropy method, were analyzed with CRADIS and other alternative methods. The analysis results showed that the Q1 article count and field-based impact scores were of high importance, whereas Google Scholar citations had lower weight. In accordance with the recommendation of the Leiden manifesto, which had a great impact on the academic community, to take into account multi-indicator and being field-based, the system proposed in this study also allows for the dynamic (updatable) and comprehensive evaluation of individual researcher performance. Compared to one-sided and limited performance measurements in literature or applications, this study fills a serious gap. Moreover, this system will help the parties to make accurate and updatable strategic decisions.

Anahtar Kelimeler

CRADIS Method Individual and global academic performance multi-criteria decision making entropy method

Kaynakça

  • AD Scientific Index. (2024, September 15). World Scientist and University Rankings, September 2024. https://www.adscientificindex.com/
  • Al-Hagree, S., Mohsen, A. A., Abdulrazzak, F. H., Al-Sanabani, M., Alalayah, K. M., Al-Gaphari, G., ... and Gawbah, H. (2023, 10-11 October). Universities the best performers: a cluster analysis of yemeni universities rankings. In 2023 3rd International Conference on Emerging Smart Technologies and Applications (p. 1-9). Taiz, Yemen. https://doi.org/10.1109/eSmarTA59349.2023.10293385
  • Ardil, C. (2021). Scholar index for research performance evaluation using multiple criteria decision making analysis. International Journal of Educational and Pedagogical Sciences, 13(2), 93-104.
  • Baydaş, M., Elma, O. E., and Stević, Ž. (2024). Proposal of an innovative MCDA evaluation methodology: knowledge discovery through rank reversal, standard deviation, and relationship with stock return. Financial Innovation, 10(1), 1-35. https://doi.org/10.1186/s40854-023-00526-x
  • Baydaş, M., Eren, T., Stević, Ž., Starčević, V., and Parlakkaya, R. (2023). Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics. PeerJ Computer Science, 9,1-24. https://doi.org/10.7717/peerj-cs.1350
  • Birkle, C., Pendlebury, D. A., Schnell, J., and Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363-376. https://doi.org/10.1162/qss_a_00018
  • Chakraborty, S. (2022). TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2(2022), 1-7. https://doi.org/10.1016/j.dajour.2021.100021
  • Clarivate analytics (Incites). (2024, September 15). Incites Analysis Person, September 2024. https://incites.clarivate.com/#/analysis/0/person
  • Clarivate™. (2024, September 15). Clarivate Highly Cited Researchers, September 2024. https://clarivate.com/highly-cited-researchers/
  • Clarivate™. (2024, September 15). InCites Help Center, September 2024. https://tinyurl.com/yn34arxx Docampo, D., and Cram, L. (2019). Highly cited researchers: a moving target. Scientometrics, 118(3), 1011-1025. https://doi.org/10.1007/s11192-018-2993-2
  • Google Academic. (2024, September 15). Google Academic, September 2024. https://scholar.google.com/
  • Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., and Rafols, I. (2015). Bibliometrics: the Leiden Manifesto for research metrics. Nature, 520(7548), 429-431. https://www.nature.com/articles/520429a.pdf
  • Hyland, K. (2023). Academic publishing and the attention economy. Journal of English for Academic Purposes, 64(2023), 1-10. https://doi.org/10.1016/j.jeap.2023.101253
  • Ioannidis, J. P. A. (2024). August 2024 data-update for "Updated science-wide author databases of standardized citation indicators", Elsevier Data Repository, V7. https://doi.org/10.17632/btchxktzyw.7
  • Ioannidis, J. P. A., Boyack, K. W., and Baas, J. (2020). Updated science-wide author databases of standardized citation indicators. PLOS Biology, 18(10), 1-3. https://doi.org/10.1371/journal.pbio.3000918
  • Li, X., Wang, K., Liu, L., Xin, J., Yang, H., and Gao, C. (2011). Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia engineering, 26(2011), 2085-2091. https://doi.org/10.1016/j.proeng.2011.11.2410
  • Li, Z., Liechty, M., Xu, J., and Lev, B. (2014). A fuzzy multi-criteria group decision making method for individual research output evaluation with maximum consensus. Knowledge-Based Systems, 56, 253-263. https://doi.org/10.1016/j.knosys.2013.11.018
  • Maral, M. (2024). Research performance of higher education in OECD countries: A hybrid multi-criteria decision-making approach. SAGE Open, 14(2), 1-20. https://doi.org/10.1177/2158244024125775
  • Markusova, V.A., Libkind, A.N., Zolotova, A.V., and Kotelnikova, N.A. (2023). Priority areas of scientific cooperation between scientists of Russia, Iran, India, and Turkey: bibliometric analysis according to the InCites database (2011–2021). Automatic Documentation and Mathematical Linguistics, 57(5), 274-283. https://doi.org/10.3103/S0005105523050047
  • Olcay, G. A., and Bulu, M. (2017). Is measuring the knowledge creation of universities possible?: A review of university rankings. Technological Forecasting and Social Change, 123, 153-160. https://doi.org/10.1016/j.techfore.2016.03.029
  • Parker, C. (2000). Performance measurement, Work Study, 49(2), 63-66. https://doi.org/10.1108/00438020010311197
  • Potter, R. W., Kovač, M., and Adams, J. (2024). Tracking changes in CNCI: the complementarity of standard, collaboration and fractional CNCI in understanding and evaluating research performance. Scientometrics, 129, 1-14. https://doi.org/10.1007/s11192-024-05028-w
  • Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 1-59. https://doi.org/10.3390/publications9010012
  • Puška A, Stević Ž., and Pamučar D. (2022). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 24(9), 11195-11225. https://doi.org/10.1007/s10668-021-01902-2
  • Puška A., and Stojanović, I. 2022. Fuzzy multi-criteria analyses on green supplier selection in an agrifood company. Journal of Intelligent Management Decision 1(1),2–16. https://doi.org/10.56578/jimd010102
  • Scopus (Elsevier). (2024, September 5). Scopus Search, September 2024. https://www.scopus.com/search/form.uri#basic
  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  • Soh, K. (2017). The seven deadly sins of world university ranking: A summary from several papers. Journal of Higher Education Policy and Management, 39(1), 104-115. http://dx.doi.org/10.1080/1360080X.2016.1254431
  • Stewart, I. (2012). In pursuit of the unknown: 17 equations that changed the world. https://doi.org/10.1007/s00283-012-9303-2
  • Szluka, P., Csajbók, E., and Győrffy, B. (2023). Relationship between bibliometric indicators and university ranking positions. Scientific Reports, 13(1), 14193. https://doi.org/10.1038/s41598-023-35306-1
  • Tuan, N., Hue, T., Lien, L., Thao, T., Quyet, N., Van, L., and Anh, L. (2020). A new integrated MCDM approach for lecturers’ research productivity evaluation. Decision Science Letters, 9(3), 355-364. https://doi.org/10.5267/j.dsl.2020.5.001
  • URAP (2023, October 28). 2023-2024 URAP Türkiye sıralaması basın açıklaması, October 2023. https://tinyurl.com/2ujta97z
  • Wang, Z., and Rangaiah, G.P. (2017). Application and analysis of methods for selecting an optimal solution from the Pareto-Optimal front obtained by multiobjective optimization. Industrial & Engineering Chemistry Research, 56(2), 560-574. https://doi.org/10.1021/acs.iecr.6b03453
  • Wang, Z., Parhi, S. S., Rangaiah, G. P., and Jana, A. K. (2020). Analysis of weighting and selection methods for pareto-optimal solutions of multiobjective optimization in chemical engineering applications. Industrial & Engineering Chemistry Research, 59(33), 14850-14867. https://doi.org/10.1021/acs.iecr.0c00969
  • Wu, J., Sun, J., Liang, L., and Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165. https://doi.org/10.1016/j.eswa.2010.10.046
  • YÖK Akademik. (2024, September 23). Yüksek Öğretim Akademik Arama, September, 2024. https://akademik.yok.gov.tr/AkademikArama/
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yöneylem
Bölüm Araştırma Makaleleri
Yazarlar

Mahmut Baydaş 0000-0001-6195-667X

Erken Görünüm Tarihi 25 Şubat 2025
Yayımlanma Tarihi 28 Şubat 2025
Gönderilme Tarihi 9 Kasım 2024
Kabul Tarihi 27 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 10 Sayı: 26

Kaynak Göster

APA Baydaş, M. (2025). Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, 10(26), 154-174. https://doi.org/10.25204/iktisad.1582267
AMA Baydaş M. Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases. İKTİSAD. Şubat 2025;10(26):154-174. doi:10.25204/iktisad.1582267
Chicago Baydaş, Mahmut. “Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi 10, sy. 26 (Şubat 2025): 154-74. https://doi.org/10.25204/iktisad.1582267.
EndNote Baydaş M (01 Şubat 2025) Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases. İktisadi İdari ve Siyasal Araştırmalar Dergisi 10 26 154–174.
IEEE M. Baydaş, “Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases”, İKTİSAD, c. 10, sy. 26, ss. 154–174, 2025, doi: 10.25204/iktisad.1582267.
ISNAD Baydaş, Mahmut. “Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases”. İktisadi İdari ve Siyasal Araştırmalar Dergisi 10/26 (Şubat 2025), 154-174. https://doi.org/10.25204/iktisad.1582267.
JAMA Baydaş M. Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases. İKTİSAD. 2025;10:154–174.
MLA Baydaş, Mahmut. “Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, c. 10, sy. 26, 2025, ss. 154-7, doi:10.25204/iktisad.1582267.
Vancouver Baydaş M. Towards Global Academic Performance Rankings: A Dynamic and Integrated Decision Support System Based on Scientometric Indicators in Different Databases. İKTİSAD. 2025;10(26):154-7.
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