Dergiler / Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 11 Issue: 2

Analysis of G20 Countries in terms of Scientific Publication Performances

Sinan Dündar(Sivas Cumhuriyet University, Faculty of Engineering, Department of Industrial Engineering)0000-0001-8061-3322
Ömer Faruk Gürcan(Sivas Cumhuriyet University, Faculty of Engineering, Department of Industrial Engineering)0000-0002-1256-2751
İlker Karadağ(Sivas Cumhuriyet University, Faculty of Engineering, Department of Industrial Engineering)0000-0002-7048-8529
Sayfa
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Yayın Tarihi
2024-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The consistency of the applied method is also confirmed by two different sensitivity analyses.

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Amaç & Hipotez:The achievement of countries in generating scientific publications is also a reflection of their efforts in the scientific domain.
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Temel Bulgular & Veri:Based on this emphasis on scientific publications, this research aimed to assess the performance of nineteen G20 countries upon scientific publication data issued by The SCImago Journal & Country Rank and covering the years 1996-2022.
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Sonuç & Katkı:The consistency of the applied method is also confirmed by two different sensitivity analyses.
🔬Tür:Research Article
📍Kapsam:2022 Verileri
⚙️Değişkenler:Scientific Publication · G20 Countries · Fuzzy SWARA

Abstract

The achievement of countries in generating scientific publications is also a reflection of their efforts in the scientific domain. The quantitative volume of these publications is not a criterion alone, but the fact that they are a source of inspiration for other scientists carrying out their studies in other countries is an important indicator in terms of evaluating the quality of publications. Based on this emphasis on scientific publications, this research aimed to assess the performance of nineteen G20 countries upon scientific publication data issued by The SCImago Journal & Country Rank and covering the years 1996-2022. The evaluation criteria do not only consist of the number of scientific documents, but also number of citable documents, number of citations, number of self-citations, number of citations per document and H-index values. Fuzzy Step-wise Weight Assessment Ratio Analysis (Fuzzy SWARA) method is employed to determine the priorities of the criteria with the participation of ten researchers from different scientific disciplines. As an outcome of the application of this method, the order of importance of the criteria is determined as H-index, number of citable documents, number of citations per document, number of citations, number of documents and self-citation. The performance order of nineteen countries is performed by using the CODAS-LN method, which includes a logarithmic normalization version of the COmbinative Distance-based ASsessment (CODAS) method and is a very convenient approach in cases where the data is not normally distributed. The results revealed that the United States has a superior position in terms of scientific publication performance, while the United Kingdom, Germany, Canada and France are aligned in the top five order. The consistency of the applied method is also confirmed by two different sensitivity analyses.

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Bu Makaleye Atıf Yap

Dündar, S., Gürcan, Ö. F., Karadağ, İ. (2024). Analysis of G20 Countries in terms of Scientific Publication Performances. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 11 Issue: 2. https://doi.org/10.30798/makuiibf.1396650

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Kaynakça (44)

  1. Alinezhad, A., & Khalili, J. (2019). New methods and applications in Multiple Attribute Decision Making (MADM) (Vol. 277). Springer.
  2. Anand, A., Gupta, P., Tamura, Y., & Papic, L. (2023). Impact of code smells on software development environments: A study based on ENTROPY-CODAS method. International Journal of Quality & Reliability Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJQRM-08-2022-0254
  3. Ansari, Z. N., Kant, R., & Shankar, R. (2020). Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: A hybrid fuzzy SWARA-fuzzy COPRAS framework approach. International Journal of Sustainable Engineering, 13(6), 473–494. https://doi.org/10.1080/19397038.2020.1758973
  4. Biswas, S., & Pamucar, D. (2021). Combinative Distance based Assessment (CODAS) Framework using Logarithmic Normalization for Multi-Criteria Decision Making. Serbian Journal of Management, 16(2), 321–340. https://doi.org/10.5937/sjm16-27758
  5. Bouraima, M. B., Tengecha, N. A., Stević, Ž., Simić, V., & Qiu, Y. (2023). An integrated fuzzy MCDM model for prioritizing strategies for successful implementation and operation of the bus rapid transit system. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05183-y
  6. Dominguez, L. A. P., Borroel, E. Z., Quezada, O. E. I., Ortiz-Munoz, D., & Najera-Acosta, A. (2023). CODAS, TOPSIS, and AHP Methods Application for Machine Selection. Journal of Computational and Cognitive Engineering, 2(4), 322–330. https://doi.org/10.47852/bonviewJCCE3202428
  7. Epifanić, V., Urošević, S., Dobrosavljević, A., Kokeza, G., & Radivojevic, N. (2020). Multi-criteria ranking of organizational factors affecting the learning quality outcomes in elementary education in Serbia. Journal of Business Economics and Management, 22(1), 1–20. https://doi.org/10.3846/jbem.2020.13675
  8. Feng, C., Hongyue, W., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. (2014). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26, 105–109. https://doi.org/10.3969/j.issn.1002-0829.2014.02.009
  9. Ghasemi, P., Mehdiabadi, A., Spulbar, C., & Birau, R. (2021). Ranking of Sustainable Medical Tourism Destinations in Iran: An Integrated Approach Using Fuzzy SWARA-PROMETHEE. Sustainability, 13(2). https://doi.org/10.3390/su13020683
  10. Ghasemian Sahebi, I., Arab, A., & Toufighi, S. P. (2020). Analyzing the barriers of organizational transformation by using fuzzy SWARA. Journal of Fuzzy Extension and Applications, 1(2), 84–97. https://doi.org/10.22105/jfea.2020.249191.1010
  11. Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
  12. Huang, Y., Kuldasheva, Z., Bobojanov, S., Djalilov, B., Salahodjaev, R., & Abbas, S. (2023). Exploring the links between fossil fuel energy consumption, industrial value added, and carbon emissions in G20 countries. Environmental Science and Pollution Research, 30, 10854–10866. https://doi.org/10.1007/s11356-022-22605-9
  13. Işık, Ö., Shabir, M., & Belke, M. (2023). Is There a Causal Relationship Between Financial Performance and Premium Production? Evidence from Turkish Insurance Industry. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(2), 1388–1412. https://doi.org/10.30798/makuiibf.1220299
  14. Karabašević, D., Stanujkić, D., Urošević, S., & Maksimović, M. (2016). An approach to personnel selection based on Swara and Waspas methods. Journal of Economics, Management and Informatics, 7(1), 1–11. https://doi.org/10.5937/bizinfo1601001K
  15. Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science.
  16. Keeney, R., Raiffa, H., & Rajala, D. (1979). Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Systems, Man and Cybernetics, IEEE Transactions On, 9, 403–403. https://doi.org/10.1109/TSMC.1979.4310245
  17. Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11, 243–258. https://doi.org/10.3846/jbem.2010.12
  18. Keshavarz-Ghorabaee, M., Zavadskas, E., Turskis, Z., & Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research, 50(3), 25–44.
  19. Koca, G., Eğilmez, Ö., Demir, E., Karamaşa, Ç., & Gökcan, H. (2022). Analysis of Drivers and Challenges in Circular Economy with SWARA and BWM Methods in Clothing Sector. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 763–787. https://doi.org/10.30798/makuiibf.822067
  20. Komasi, H., Nemati, A., Zolfani, S., Kahvand, M., Antucheviciene, J., & Šaparauskas, J. (2023). Assessing the environmental competitiveness of cities based on a novel MCDM approach. Journal of Competitiveness, 15(2), 121–150. https://doi.org/10.7441/joc.2023.02.07
  21. Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E. K., & Chakraborty, S. (2022). A SWARA-CoCoSo-Based Approach for Spray Painting Robot Selection. Informatica, 33(1), 35–54. https://doi.org/10.15388/21-INFOR466
  22. Kumari, A., & Acherjee, B. (2022). Selection of non-conventional machining process using CRITIC-CODAS method. International Conference on Materials, Machines and Information Technology-2022, 56, 66–71. https://doi.org/10.1016/j.matpr.2021.12.152
  23. Lin, G., Hu, Z., & Hou, H. (2018). Research preferences of the G20 countries. Current Science, 115(8), 1477–1485. JSTOR.
  24. Mar’I, M., Seraj, M., & Tursoy, T. (2023). The Role of Fiscal Policy in G20 Countries in the Context of the Environmental Kuznets Curve Hypothesis. Energies, 16(5), 2215. https://doi.org/10.3390/en16052215
  25. Mavi, R. K., Goh, M., & Zarbakhshnia, N. (2017). Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. The International Journal of Advanced Manufacturing Technology, 91, 2401–2418. https://doi.org/10.1007/s00170-016-9880-x
  26. Pamucar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
  27. Pamucar, D., Lukovac, V., Božanić, D., & Komazec, N. (2018). Multi-criteria FUCOM-MAIRCA model for the evaluation of level crossings: Case study in the Republic of Serbia. Operational Research in Engineering Sciences: Theory and Applications, 1(1), Article 1. https://doi.org/10.31181/oresta190120101108p
  28. Pamucar, D., Yazdani, M., Obradovic, R., Kumar, A., & Torres-Jiménez, M. (2020). A novel fuzzy hybrid neutrosophic decision-making approach for the resilient supplier selection problem. International Journal of Intelligent Systems, 35(12), 1934–1986. https://doi.org/10.1002/int.22279
  29. Pamucar, D., Zizovic, M., Biswas, S., & Božanić, D. (2021). A New Logarithm Methodology of Additive Weights (LMAW) For Multi-Criteria Decision-Making: Application in Logistics. Facta Universitatis Series Mechanical Engineering, 19(3), 361–380. https://doi.org/10.22190/FUME210214031P
  30. Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022). Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms, 11(9). https://doi.org/10.3390/axioms11090427
  31. Qahtan, S., Alsattar, H. A., Zaidan, A. A., Deveci, M., Pamucar, D., Delen, D., & Pedrycz, W. (2023). Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model. Applied Soft Computing, 138, 110170. https://doi.org/10.1016/j.asoc.2023.110170
  32. Rao, R. V., & Singh, D. (2011). Evaluating flexible manufacturing systems using Euclidean distance‐based integrated approach. International Journal of Decision Sciences, Risk and Management, 3(1/2), 32–53. https://doi.org/10.1504/IJDSRM.2011.040746
  33. Şaylan, O., Esmer, Y., & Şentürk, P. (2023). KOBİ`lerde Stratejik Pazarlama Yönetimi: Lüleburgaz Örneği. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 639–657. https://doi.org/10.30798/makuiibf.1219980
  34. Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the Selection of a packaging design based on the SWARA method. The Engineering Economics, 26(2), 181–187. https://doi.org/10.13140/RG.2.1.1350.9603
  35. Wankhede, S., Pesode, P., Gaikwad, S., Pawar, S., & Chipade, A. (2023). Implementing Combinative Distance Base Assessment (CODAS) for Selection of Natural Fibre for Long Lasting Composites. Materials Science Forum, 1081, 41–48. https://doi.org/10.4028/p-4pd120
  36. Wei, C., Wu, J., Guo, Y., & Wei, G. (2021). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078
  37. World Bank. (2023). Gross Domestic Product 2022. https://databankfiles.worldbank.org/public/ddpext_download/GDP.pdf
  38. Xiang, Z., Naseem, M. H., & Yang, J. (2022). Selection of Coal Transportation Company Based on Fuzzy SWARA-COPRAS Approach. Logistics, 6(1), 1–15. https://doi.org/10.3390/logistics6010007
  39. Yazdani, M., Chatterjee, P., Pamucar, D., & Doval, M. (2019). A risk-based integrated decision-making model for green supplier selection: A case study of a construction company in Spain. Kybernetes, 49(4), 1229–1252. https://doi.org/10.1108/K-09-2018-0509
  40. Zavadskas, E. K., & Turskis, Z. (2010). A New Additive Ratio Assessment (ARAS) Method in Multicriteria Decision‐Making. Technological and Economic Development of Economy, 16(2), Article 2. https://doi.org/10.3846/tede.2010.10
  41. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevičius, A. (2012). Optimization of Weighted Aggregated Sum Product Assessment. Electronics and Electrical Engineering, 122. https://doi.org/10.5755/j01.eee.122.6.1810
  42. Zavadskas, E., & Turskis, Z. (2008). A New Logarithmic Normalization Method in Games Theory. Informatica, Lith. Acad. Sci., 19(2), 303–314. https://doi.org/10.15388/Informatica.2008.215
  43. Zolfani, S., & Šaparauskas, J. (2013). New Application of SWARA Method in Prioritizing Sustainability Assessment Indicators of Energy System. Engineering Economics, 24, 408–414. https://doi.org/10.5755/j01.ee.24.5.4526
  44. Zolfani, S. H., Zavadskas, E. K., & Turskis, Z. (2013). Design of Products with Both International and Local Perspectives based on Yin-Yang Balance Theory and Swara Method. Economic Research-Ekonomska Istraživanja, 26(2), 153–166. https://doi.org/10.1080/1331677X.2013.1151761