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Dergiler / Ekoist: Journal of Econometrics and Statistics / 2026 / Sayı: 42

Investıgatıng the Productıve Capacıty Performance of E7 Countrıes Using the WENSLO-ARTASI Model

Yusuf Kahreman(Sivas Cumhuriyet University, Finance and Banking Department)0000-0001-5968-5081
Sayfa
154–174
Yayın Tarihi
2025-06-25
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Furthermore, when the years are analysed separately, it is observed that the productive capacity index ranking published by UNCTAD aligns with the productive capacity index ranking.

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Amaç & Hipotez:The E7 countries, which are among the major economies in the world with high growth rates, are expected to have a significant share of the world’s total GDP shortly.
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Temel Bulgular & Veri:This study intends to analyse the productive capacity performance assessment of the E7 countries from 2003 to 2022 using a WENSLO-ARTASI integrated model.
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Sonuç & Katkı:Furthermore, when the years are analysed separately, it is observed that the productive capacity index ranking published by UNCTAD aligns with the productive capacity index ranking.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2003 Verileri
⚙️Değişkenler:productive capacity · performance analysis · WENSLO

Abstract

The E7 countries, which are among the major economies in the world with high growth rates, are expected to have a significant share of the world’s total GDP shortly. This study intends to analyse the productive capacity performance assessment of the E7 countries from 2003 to 2022 using a WENSLO-ARTASI integrated model. For this purpose, all variables considered in calculating the productive capacity index published by UNCTAD (United Nations Conference on Trade and Development) are used in the study. The WENSLO (Weights by ENvelope and SLOpe) method is employed to reveal the importance levels of the evaluation criteria, while the ARTASI (Alternative Ranking Technique based on Adaptive Standardised Intervals) method is used to determine the productive capacity performance ranking of the E7 countries. Additionally, the reliability of the proposed model was tested through various sensitivity analyses. Because of the study, it was concluded that the information and communication technologies index holds the highest importance among the evaluation criteria. In the productive capacity evaluation, when the ranking results for all years are averaged, the rankings are China, Turkey, Russia, Mexico, Brazil, Indonesia, and India. Furthermore, when the years are analysed separately, it is observed that the productive capacity index ranking published by UNCTAD aligns with the productive capacity index ranking.

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Bu Makaleye Atıf Yap

Kahreman, Y. (2025). Investıgatıng the Productıve Capacıty Performance of E7 Countrıes Using the WENSLO-ARTASI Model. Ekoist: Journal of Econometrics and Statistics, Sayı: 42, 154–174. https://doi.org/10.26650/ekoist.2025.42.1614814

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Kaynakça (44)

  1. Altıntaş, F. F. (2022). G20 grubu ülkelerin üretkenlik kapasitelerinin değerlendirilmesi. 19 Mayıs Sosyal Bilimler Dergisi, 3(2), 138–152. https://doi.org/10.52835/19maysbd.1071564 google scholar
  2. Aydın, F. F. (2010). Enerji tüketimi ve ekonomik büyüme. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 35, 317–340. google scholar
  3. Balac, M. (2015). Productive capacities in developing countries: Does foreign direct investment matter? (Unpublished master’s thesis). Université d’Auvergne, Clermont-Ferrand. google scholar
  4. Bassani, A., & Scarpetta, S. (2001). Does human capital matter for growth in OECD countries? Evidence from pooled mean-group estimates. Economics Department Working Papers, 282. http://dx.doi.org/10.2139/ssrn.265364 google scholar
  5. Božanić, D., Milić, A., Tešić, D., Salabun, W., & Pamučar, D. (2021). D numbers–FUCOM–fuzzy RAFSI model for selecting the group of construction machines for enabling mobility. Facta Universitatis, Series: Mechanical Engineering, 19(3), 447–471. https://doi.org/ 10.22190/FUME210318047B google scholar
  6. Bulut, Z. A. (2004). İşletmeler açısından kapasite planlaması ve kapasite planlamasına etki eden faktörler. Mevzuat Dergisi, 80, 1–10. google scholar
  7. Cheng, J., Gil, K. Y., Shamansurova, Z., & Lanre, I. R. (2024). Financing sustainable environment in the wake of global warming for E7 economies: Heterogeneous analyses based on NARDL and quantile regression. International Journal of Sustainable Development & World Ecology, 31(3), 298–313. https://doi.org/10.1080/13504509.2023.2284906 google scholar
  8. Cornia, G. A., & Scognamillo, A. (2016). Clusters of least developed countries, their evolution between 1993 and 2013, and policies to expand their productive capacity. Department of Economic & Social Affairs, 33, 1–35. google scholar
  9. Deliktaş, E. (2001). Malthusgil yaklaşımdan modern ekonomik büyümeye. Ege Akademik Bakış, 1(1), 92–114. google scholar
  10. Demiral, M., & Demiral, Ö. (2021). Socio-economic productive capacities and energy efficiency: Global evidence by income level and resource dependence. Environmental Science and Pollution Research, 30(15), 42766-42790. https://doi.org/10.1007/s11356-021-17266-z google scholar
  11. Doğanay, M. A., & Değer, M. K. (2020). Kurumlar ve ekonomik büyüme: Panel veri analizi (2002-2018). Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 10(19), 75–99. google scholar
  12. Gnangnon, S. K. (2021). Effect of productive capacities on economic complexity: Do aid for trade flows matter? Journal of Economic Integration, 36(4), 626–688. https://doi.org/10.11130/jei.2021.36.4.626 google scholar
  13. González-Blanco, J., Vila-Alonso, M., & Guisado-González, M. (2019). Exploring the complementarity between foreign technology, embedded technology and increase of productive capacity. Technological and Economic Development of Economy, 25(1), 39–58. https://doi.org/10.3846/tede.2019.7600 google scholar
  14. Hall, E., & Jones, C. I. (1996). Why do some countries produce so much more output per worker than others? Stanford University National Bureau of Economic Research (Working Paper). https://doi.org/10.1162/003355399555954 google scholar
  15. Hayaloğlu, P. (2018). İklim değişikliğinin tarım sektörü ve ekonomik büyüme üzerindeki etkileri. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(25), 51–62. google scholar
  16. Hicks, N. (1980). Economic growth and human resources. World Bank Staff Working Paper, 408. google scholar
  17. Işık, Ö. (2022). COVID-19 salgınının katılım bankacılığı sektörünün performansına etkisinin MEREC-PSI-MAIRCA modeliyle incelenmesi. Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 10(2). https://doi.org/10.52122/nisantasisbd.1167829 google scholar
  18. Jones, C. I. (1996). Human capital, ideas, and economic growth. Department of Economics, Stanford University. google scholar
  19. Kara, K., Ergin, E. A., Yalçın, G. C., Çelik, T., Deveci, M., & Kadry, S. (2025). Sustainable brand logo selection using an AI-supported PF-WENSLO-ARLON hybrid method. Expert Systems with Applications, 260, 125382. https://doi.org/10.1016/j.eswa.2024.125382 google scholar
  20. Kara, K., Yalçın, G. C., Ergin, E. A., Simic, V., & Pamucar, D. (2024a). A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance. Socio-Economic Planning Sciences, 94, 101918. https://doi.org/10.1016/j.seps.2024.101918 google scholar
  21. Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., & Pamucar, D. (2024b). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft ComputinG, 162, 111826. https://doi.org/10.1016/j.asoc.2024. 111826 google scholar
  22. Kartal, G. (2021). Enerji güvenliği ve ekonomik büyüme ilişkisi: Türk dünyası ülke panelinden kanıtlar. Bilig, 101, 163–192. https://doi.org/ 10.12995/bilig.10107 google scholar
  23. Koç, E., Şenel, M. C., & Kaya, K. (2017). Türkiye’de ekonomik göstergeler - İmalat sanayi kapasite kullanım oranı. Mühendis ve Makina, 58(689), 1–22. google scholar
  24. Koşaroğlu, Ş. M. (2021). E7 ülkelerinin makroekonomik performanslarının ENTROPİ ve ARAS yöntemleriyle karşılaştırılması. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 68, 203–221. https://doi.org/10.51290/dpusbe.833885 google scholar
  25. Mian, A., Sufi, A., & Verner, E. (2019). How does credit supply expansion affect the real economy? The productive capacity and household demand channels. The Journal of Finance, 75(2), 949–994. https://doi.org/10.1111/jofi.12869 google scholar
  26. Middendorf, T. (2005). Human capital and economic growth in OECD countries: some new insights. Journal of Economic and Administrative Sciences, (36)4, 367-380. https://doi.org/10.1108/JEAS-07-2019-0073 google scholar
  27. Molua, E. L., Benhin, J., Kabubo-Mariara, J., Ouedraogo, M., & El-Marsafawy, S. (2010). Global climate change and vulnerability of African agriculture: Implications for resilience and sustained productive capacity. Quarterly Journal of International Agriculture, 49(3), 183–211. google scholar
  28. Olarte, S. H., Villarreal, F., & Torrent, J. (2021). Is productive capacity a key factor to reduce inequalities in South America? Development Studies Research, 8(1), 94–108. https://doi.org/10.1080/21665095.2021.1890171 google scholar
  29. Özkan, G., & Çelik, H. (2018). Bilgi iletişim teknolojileri ile ekonomik büyüme arasındaki ilişki: Türkiye için bir uygulama. Uluslararası Ticaret ve Ekonomi Araştırmaları Dergisi, 2(1), 1–15. google scholar
  30. Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., & Deveci, M. (2023). A novel WENSLO and ALWAS multicriteria methodology and its application to green growth performance evaluation. IEEE Transactions on Engineering Management, 71, 9510-9525.https://doi. org/10.1109/TEM.2023.3321697 google scholar
  31. Pamucar, D., Žižović, 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, 19(3), 361-380. https://doi.org/10.22190/FUME210214031P google scholar
  32. Pamucar, D., Simic, V., Görçün, Ö. F., & Küçükönder, H. (2024). Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals. Expert Systems with Applications, 239, 122312. https://doi.org/10.1016/j.eswa.2023.122312 google scholar
  33. Puška, A., Stević, Ž., & Pamučar, D. (2022). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi criteria analysis methods. Environment, Development and Sustainability, 24, 11195–11225. https://doi.org/10. 1007/s10668-021-01902-2 google scholar
  34. Salim, R. A. (2008). Differentials at firm level productive capacity realization in Bangladesh food manufacturing: An empirical analysis. Applied Economics, 40, 3111–3126. https://doi.org/10.1080/00036840600994088 google scholar
  35. Samadder, S., Ghosh, K., & Basu, T. (2012). Scaling analysis of prime stock exchange indices of the Emerging Seven (E7) countries. International Journal of Applied Computational Science and Mathematics, 2(1), 11–22. google scholar
  36. Therkildsen, O. (2008, March). Public sector reforms and the development of productive capacities in LDCs (Background Paper No. 1). United Nations Conference on Trade and Development (UNCTAD). google scholar
  37. Top, A. (2002). Verimlilik ve üretkenlik üzerine düşünceler. Öneri, 5(17), 31–34. https://doi.org/10.14783/maruoneri.683345 google scholar
  38. Türedi, S. (2013). Bilgi ve iletişim teknolojilerinin ekonomik büyümeye etkisi: Gelişmiş ve gelişmekte olan ülkeler için panel veri analizi. Gümüşhane Üniversitesi Sosyal Bilimler Elektronik Dergisi, 4(7). google scholar
  39. UNCTAD. (2021). UNCTAD productive capacities index: Methodological approach and results. United Nations Conference on Trade and Development, United Nations, Geneva, Switzerland. google scholar
  40. Wen, Z., Liao, H., & Zavadskas, E. K. (2020). MACONT: Mixed aggregation by comprehensive normalization technique for multi-criteria analysis. Informatica, 31(4), 857–880. https://doi.org/10.15388/20-INFOR417 google scholar
  41. Wilson, J. (2021). Inflation and productive capacity - An empirical risk reduction model. SSRN, 1(14). http://dx.doi.org/10.2139/ssrn. 3912154 google scholar
  42. Zakeri, S., Chatterjee, P., Konstantas, D., & Ecer, F. (2023). A decision analysis model for material selection using simple ranking process. google scholar
  43. Scientific Reports, 13(1), 8631. https://doi.org/10.1038/s41598-023-35405-z google scholar
  44. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD. Erişim tarihi: 28 Haziran 2024 google scholar