TÜRKİYE’DEKİ BÜYÜKŞEHİRLERİN İHRACAT PERFORMANSLARININ BÜTÜNLEŞİK ÇOK KRİTERLİ KARAR VERME YÖNTEMLERİNE GÖRE DEĞERLENDİRİLMESİ

Abstract

Gelişmekte olan ekonomilerin kalkınmasında sanayileşme ve ihracata dayalı büyüme önem arz etmektedir. Buna paralel olarak, büyükşehirler, yüksek nüfus yoğunluğu ve gelişmiş sanayileşme ile ihracat üzerinde olumlu bir etkiye sahiptir. Bu çalışmada, Türkiye'deki büyükşehirlerin 2022 yılı ihracat performansları bütünleşik Çok Kriterli Karar Verme (ÇKKV) yöntemleri kullanılarak incelenmiş ve 30 büyükşehir ihracat performanslarına göre sıralanmıştır. Öncelikle ilgili literatürden yararlanılarak ihracat performansını etkileyen beş kriter belirlenmiştir. Bu kriterler; 2022 yılında büyükşehirlerin ihracat rakamları, ihracat yapılan ülke sayısı, ihracat yapılan sektör sayısı, ihracatçı firma sayısı ve büyükşehirde depoların kapsadığı alanlar (m2) şeklindedir. Daha sonra CRITIC tabanlı MULTIMOORA-WASPAS yöntemleri kullanılarak büyükşehirlerin ihracat performanslarının değerlendirilmesi yapılmıştır. Kriter ağırlıkları CRITIC yöntemi ile hesaplanmıştır. Bunu takiben, büyükşehirlerin ihracat performansları MULTIMOORA ve WASPAS yöntemlerine göre sıralanmıştır. Bulgular, 2022 döneminde en yüksek ihracat performansının İstanbul, Kocaeli, İzmir ve Hatay'da olduğunu gösterirken, 2022 yılında en düşük ihracat performansının ise Diyarbakır, Şanlıurfa, Ordu, Van ve Erzurum'da olduğunu gösterdi.

Keywords

• İhracat
• Büyükşehirler
• CRITIC
• WASPAS
• MULTIMOORA

ANALYSIS OF METROPOLITAN CITIES EXPORT PERFORMANCE IN TURKEY BY INTEGRATED MCDM METHODS

Abstract

Industrialization and export-based growth are very important in the development of emerging economies. In parallel, metropolitan cities have a positive effect on exports with high population density and advanced industrialization. Accordingly, in this study, the export performances of metropolitan cities in Turkey in 2022 was examined using integrated Multi-Criteria Decision Making (MCDM) methods and 30 metropolitan cities were ranked based on their export performances. Initially, five criteria which was affecting export performance were determined by using the relevant literature. These criteria are as follows: the export numbers of metropolitans in 2022, the number of countries exported, the number of exported sectors, the number of exporting companies and the areas covered by warehouses in metropolitan (m2). Then, the evaluation of the export performances of metropolitans were conducted by using CRITIC-based MULTIMOORA-WASPAS methods. The criteria weights were calculated by CRITIC method. Following this, the export performance of metropolitans was ranked by MULTIMOORA and WASPAS methods. The findings indicated that İstanbul, Kocaeli, İzmir and Hatay have the highest export performance during the period of 2022. On the other hand, Diyarbakır, Şanlıurfa, Ordu, Van and Erzurum have the lowest export performance in 2022.

Keywords

• Export
• Metropolitans
• CRITIC
• WASPAS
• MULTIMOORA

References

1. Alabı, O., Madaki, M., Sanusı, S., Suleiman, U., Omole, E., Olumuyiwa, A. S., ... & Shaba, M. G. (2022). Factors influencing export performance of ginger (Zinbiger Officinale) in Nigeria. International Journal of Agriculture Environment and Food Sciences, 6(3), 370-377.
2. Alinezhad, A., & Khalili, J. (2019). New methods and applications in multiple attribute decision making (MADM) (Vol. 277). Cham: Springer. https://doi.org/10.1007/978-3-030-15009-9.
3. Arslandere, M. (2020). Export commitment, export market orientation and performance: an analysis of Turkish exporters. Third Sector Social Economic Review, 55(2), 1217-1236. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.20.05.1354.
4. Aruldoss, M., Lakshmi, T. M., & Venkatesan, V. P. (2013). A survey on multi criteria decision making methods and its applications. American Journal of Information Systems, 1(1), 31-43. https://doi.org/10.12691/ajis-1-1-5.
5. Aytaç Adalı, E., & Tuş Işık, A. (2017). The multi-objective decision making methods based on MULTIMOORA and MOORA for the laptop selection problem. Journal of Industrial Engineering International, 13, 229-237. https://doi.org/10.1007/s40092-016-0175-5.
6. Bączkiewicz, A., Wątróbski, J., Kizielewicz, B., & Sałabun, W. (2021). Towards objectification of multi-criteria assessments: a comparative study on MCDA methods. In 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 417-425). IEEE. https://doi.org/10.15439/2021F61
7. Baidya, J., Garg, H., Saha, A., Mishra, A. R., Rani, P., & Dutta, D. (2021). Selection of third-party reverses logistic providers: an approach of BCF-CRITIC-MULTIMOORA using archimedean power aggregation operators. Complex & Intelligent Systems, 7(5), 2503-2530. https://doi.org/10.1007/s40747-021-00413-x.
8. Baležentis, T., & Baležentis, A. (2014). A survey on development and applications of the multi‐criteria decision making method multimoora. Journal of multi‐criteria decision analysis, 21(3-4), 209-222. https://doi.org/10.1002/mcda.1501.

9. Bektaş, S. (2022). Türk Sigorta Sektörünün 2002-2021 Dönemi için MEREC, LOPCOW, COCOSO, EDAS ÇKKV Yöntemleri ile Performansının Değerlendrilmesi. BDDK Bankacılık ve Finansal Piyasalar Dergisi, 16(2), 247-283. https://doi.org/10.46520/bddkdergisi.1178359.
10. Brauers, W. K. M. (2012). Project management for a country with multiple objectives. Czech Economic Review, 6(01), 80-101.
11. Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by multimoora as an instrument for transition economies. Technological and economic development of economy, 16(1), 5-24 https://doi.org/10.3846/tede.2010.01.
12. Brauers, W. K. M., & Zavadskas, E. K. (2011). From a centrally planned economy to multiobjective optimization in an enlarged project management: the case of China. Economic Computation and Economic Cybernetics Studies and Research, 45(1), 167-188.
13. Brauers, W. K. M., & Zavadskas, E. K. (2012). Robustness of MULTIMOORA: a method for multi-objective optimization. Informatica, 23(1), 1-25.
14. Brauers, W. K., & Zavadskas, E. K. (2006). The moora method and its application to privatization in a transition economy. Control and cybernetics, 35(2), 445-469.
15. Brauers, W. K., & Zavadskas, E. K. (2013). Multi-objective decision making with a large number of objectives. An application for Europe 2020. International Journal of Operations Research, 10(2), 67-79.
16. Carneiro, J., Rocha, A. D., & Silva, J. F. D. (2011). Determinants of export performance: a study of large Brazilian manufacturing firms. BAR-Brazilian Administration Review, 8, 107-132. https://doi.org/10.1590/S1807-76922011000200002.
17. Çubuk, M. (2022). Türkiye’de büyükşehirlerin sağlık turizmi potansiyellerinin CRITIC ve WASPAS yöntemleri ile karşılaştırılması. Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 147-174. https://doi.org/10.33399/biibfad.1139104.
18. Dahooie, J. H., Meidute-Kavaliauskiene, I., Vanaki, A. S., Podviezko, A., & Beheshti Jazan Abadi, E. (2020). Development of a firm export performance measurement model using a hybrid multi-attribute decision-making method. Management Decision, 58(11), 2349-2385. https://doi.org/10.1108/MD-09-2019-1156.
19. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the critic method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-H.
20. Düzgün, R., & Taşçı, H. M. (2014). Türk işletmelerinin ihracat performansını belirleyen faktörler: İSO-500 üzerine bir uygulama. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 9(3), 7-24.
21. Ecer, F., Pamucar, D., Zolfani, S. H., & Eshkalag, M. K. (2019). Sustainability assessment of OPEC countries: Application of a multiple attribute decision making tool. Journal of Cleaner Production, 241, 118324.
22. Ela, M., Doğan, A., & Uçar, O. (2018). Avrupa Birliği Ülkeleri ve Türkiye’nin Makroekonomik Performanslarının TOPSIS Yöntemi ile Karşılaştırılması. Osmaniye Korkut Ata Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2(2), 129-143.
23. Erdoğan, H. H., & Kırbaç, G. (2021). Financial performance measurement of logistics companies based on ENTROPY and WASPAS methods. İşletme Araştırmaları Dergisi, 13(2), 1093-1106. https://doi.org/10.20491/isarder.2021.1186
24. Genc, E. G., & Basar, O. D. (2019). Comparison of country ratings of credit rating agencies with MOORA method. Business and Economics Research Journal, 10(2), 391-404.
25. Jayant, A., Chandan, A. K., & Singh, S. (2019). Sustainable supplier selection for battery manufacturing industry: a MOORA and WASPAS based approach. In Journal of Physics: Conference Series (Vol. 1240, No. 1, p. 012015). IOP Publishing. https://doi.org/10.1088/1742-6596/1240/1/012015
26. Karagöz, K. (2016). Determining factors of turkey's export performance: an empirical analysis. Procedia economics and finance, 38, 446-457. https://doi.org/10.1016/S2212-5671(16)30216-7
27. Karande, P., & Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37, 317-324. https://doi.org/10.1016/j.matdes.2012.01.013.
28. Karande, P., Zavadskas, E., & Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), 399-422. https://doi.org/10.5267/j.ijiec.2016.1.001
29. Kazak, H. (2023). Türkiye perakende sektörü ve sektörün önde gelen bazı firma finansal performanslarının DEMATEL ve MOORA bütünleşik yaklaşımı ile değerlendirilmesi. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 8(1), 48-74. https://doi.org/10.29106/fesa.1186716.
30. Konak, T., Elbir, G., Yılmaz, S., Karataş, B., Durman, Y., & Düzakın, H. (2018). Borsa İstanbul’da işlem gören tekstil firmalarının TOPSIS ve MOORA yöntemi ile analizi. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(1), 11-44.
31. Maruf, M. & Özdemir, K. (2021). Türkiye’deki büyükşehirlerin ihracat performanslarının CRITIC ve MAUT yöntemi ile değerlendirilmesi. Aksaray Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 5(1), 85-99. https://doi.org/10.38122/ased.927345.
32. Metin, İ., & Küçükbay, F. (2019). İhracatta finansman kaynaklarının değerlendirilmesine yönelik çok kriterli bir yaklaşım: PROMETHEE yöntemi. Journal Of Social Sciences Institute/Sosyal Bilimler Enstitüsü Dergisi, 9(18).
33. Meydan, C., Yıldırım, B. F., & Senger, Ö. (2016). BİST’te işlem gören gıda işletmelerinin finansal performanslarının gri ilişkisel analiz yöntemi kullanılarak değerlendirilmesi. Muhasebe ve Finansman Dergisi, (69), 147-171. https://doi.org/10.25095/mufad.396668
34. Miç, P., & Antmen, Z. F. (2021). A decision-making model based on TOPSIS, WASPAS, and MULTIMOORA methods for university location selection problem. SAGE Open, 11(3), 21582440211040115. https://doi.org/0.1177/21582440211040115
35. Monajemzadeh, N., Karbassi Yazdi, A., Hanne, T., Shirbabadi, S., & Khosravi, Z. (2022). Identifying and prioritizing export-related csfs of steel products using hybrid multi-criteria methods. Cogent Engineering, 9(1), 2077162. https://doi.org/10.1080/23311916.2022.2077162
36. Mpunga, H. S. (2016). Examining the factors affecting export performance for small and medium enterprises (SMEs) in tanzania. Journal of Economics and Sustainable Development, 7(6), 41-51.
37. Mukul, E., Büyüközkan, G., & Güler, M. (2019). Evaluation of digital marketing technologies with MCDM methods. In Proceedings of the 6th International Conference on New Ideas in Management Economics and Accounting, France, Paris (pp. 19-21).
38. Özekenci, E. K. (2023). AHP-TOPSIS Yöntemine Dayalı Lojistik Merkez Kuruluş Yeri Seçimi: Çukurova Bölgesi Üzerine Bir Araştırma. Tarsus Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 4(1), 70-84.
39. Safari, A., & Saleh, A. S. (2020). Key determinants of SMEs’ export performance: a resource-based view and contingency theory approach using potential mediators. Journal of Business & Industrial Marketing, 35(4), 635-654. https://doi.org/10.1108/JBIM-11-2018-0324.
40. Satıcı, S. (2021). Ülkelerin inovasyon performansının CRITIC temelli WASPAS yöntemiyle değerlendirilmesi. Girişimcilik ve Kalkınma Dergisi, 16(2), 91-104.
41. Taçoğlu, C., Ceylan, C., & Kazançoğlu, Y. (2019). Analysis of variables affecting competitiveness of SMEs in the textile industry. Journal of Business Economics and Management, 20(4), 648-673. https://doi.org/10.3846/jbem.2019.9853
42. Taherdoost, H., & Madanchian, M. (2023). Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1), 77-87. https://doi.org/10.3390/encyclopedia3010006
43. TİM (Türkiye İhracatçılar Meclisi). (2023). Export volumes in turkey. Retrieved from: https://tim.org.tr/tr/ihracat-rakamlari (17.03.2023).
44. Türkoğlu, M., & Duran, G. (2023). Çok kriterli karar verme yöntemleri ile bölgesel kapsamlı ekonomik ortaklık (RCEP) ülkelerinin lojistik performanslarının değerlendirilmesi. Ekonomi Bilimleri Dergisi, 15(1), 45-69. https://doi.org/10.55827/ebd.1247297
45. Uslu, Y. D., Yılmaz, E., & Yiğit, P. (2021). Developing qualified personnel selection strategies using MCDM approach: A university hospital practice. In Strategic Outlook in Business and Finance Innovation: Multidimensional Policies for Emerging Economies (pp. 195-205). Emerald Publishing Limited.
46. Utama, D. M., Asrofi, M. S., & Amallynda, I. (2021). Integration of AHP-MOORA algorithm in green supplier selection in the indonesian textile industry. In Journal of Physics: Conference Series (Vol. 1933, No. 1, p. 012058). IOP Publishing. https://doi.org/10.1088/1742-6596/1933/1/012058.
47. Velasquez, M. & Hester, P.T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10, 56–66.
48. Vujičić, M. D., Papić, M. Z., & Blagojević, M. D. (2017). Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection. Tehnika, 72(3), 422-429.
49. Were, M., Ndung’u, N., Geda, A., & Karingi, S. (2002). Analysis of Kenya’s export performance: an empirical evaluation. macroeconomics division. Kenya Institute for Public Policy Research and Analysis Discussion Paper (22): November.
50. Yazgan, A. E. (2022). Bütünleşik CRITIC ve EDAS Yöntemleri ile Türkiye’deki Büyükşehirlerin İhracat Performanslarının İncelenmesi. Fiscaoeconomia, 6(2), 909-929. https://doi.org/10.25295/fsecon.1094411.
51. Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013). MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions. Economic Computation and Economic Cybernetics Studies and Research, 47, 5-20.
52. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. https://doi.org/10.5755/j01.eee.122.6.1810