EKONOMİK ÖZGÜRLÜK ENDEKSİNİN ÇOK KRİTERLİ KARAR VERME YÖNTEMLERİYLE ANALİZİ / Analysis Of The Economic Freedom Index With Multi-Criteria Decision-Making Methods

Abstract

Bu çalışmanın iki önemli amacı vardır. Birincisi, Ekonomik Özgürlük İndeksinin çok kriterli karar verme yöntemiyle analizdir. Bunun için IEF sıralama sonuçları hesaplanmasında TOPSIS ve MAUT yöntemleri kullanılmıştır. İkincisi, Ekonomik Özgürlük Endeksi sıralama sonuçları ile CETOPSIS ve CEMAUT yöntemlerinden elde edilen sıralama sonuçlarının karşılaştırılmasıdır. Bunun için Spearman Korelasyon yaklaşımı kullanılmıştır. Çalışmanın uygulama bölümünden dört önemli sonuç elde edilmiştir. Birincisi, CRITIC ve ENTROPY yöntemleriyle yapılan TOPSIS sıralaması arasında tam korelasyon ilişkisi vardır. İkincisi, CRITIC ve ENTROPY yöntemleriyle yapılan MAUT sıralaması arasında tam korelasyon ilişkisi vardır. Üçüncüsü, CETOPSIS ve CEMAUT yöntemlerinden elde edilen performans sıralama sonuçları arasında pozitif yönlü kuvvetli bir ilişki vardır. Dördüncüsü, CETOPSIS ve CEMAUT performans sıralama sonuçları ile Ekonomik Özgürlük Endeks performans sıralama sonuçları arasında pozitif yönlü kuvvetli bir ilişki vardır. Bulunan sonuçlar istatistiksel olarak anlamlıdır. Karar süreci dinamik bir özellik gösterir.

Keywords

• CRITIC
• ENTTOPY
• TOPSIS
• MAUT
• Ekonomik Özgürlük Endeksi

ANALYSIS OF THE ECONOMIC FREEDOM INDEX WITH MULTI-CRITERIA DECISION-MAKING METHODS / Ekonomik Özgürlük Endeksinin Çok Kriterli Karar Verme Yöntemleriyle Analizi

Abstract

This study has two important aims. The first is the analysis with the multi-criteria decision-making method of the Economic Freedom Index. For this, TOPSIS and MAUT methods were used in the calculation of IEF ranking results. The second is to compare the ranking results of the Economic Freedom Index with the ranking results obtained from CETOPSIS and CEMAUT methods. Spearman Correlation approach was used for this. Four important results were obtained from the application part of the study. First, there is a full correlation relationship between TOPSIS ranking made by CRITIC and ENTROPY methods. Second, there is a full correlation between the MAUT rankings made with the CRITIC and ENTROPY methods. Third, there is a strong mutually positive correlation between the performance ranking results obtained from CETOPSIS and CEMAUT methods. Fourth, there is a mutually strong positive correlation between CETOPSIS and CEMAUT performance ranking results and Economic Freedom Index performance ranking results. The results found are statistically significant. The decision process shows a dynamic feature.

Keywords

• CRITIC
• ENTROPY
• TOPSIS
• MAUT
• Economic Freedom Index

References

1. Akyene, T. (2012). Cell Phone Evaluation base on Entropy and TOPSIS. Interdisciplinary Journal of Research in Business, 1(12), 9-15.
2. Altun Turker, Y., Baynal, K., ve Turker, T. (2019). The Evaluation of Learning Management Systems by Using Fuzzy AHP, Fuzzy TOPSIS and an Integrated Method: A Case Study. Turkish Online Journal of Distance Education, 20(2), 195-218.
3. Ameri, A. A., Pourghasemi, H. R., ve Cerda, A. (2018). Erodibility Prioritization of Sub-Watersheds Using Morphometric Parameters Analysis and Its Mapping: A Comparison among TOPSIS, VIKOR, SAW, and CF Multi-Criteria Decision Making Models. Science of the Total Environment, 613, 1385-1400.
4. Bukhsh, Z. A., Stipanovic, I., Hartmaan, A., ve Klanker, G. (2018). Evaluation and Application of AHP, MAUT and ELECTRE for Infrastructure management. Conference Paper, 1-9.
5. Çalık, A., Çizmecioğlu, S., ve Akpınar, A. (2019). An Integrated AHP‐TOPSIS Framework for Foreign Direct Investment in Turkey. Journal of Multi‐Criteria Decision Analysis, 26(5-6), 296-307.
6. De Faria, D. A., Frazão, M. L. D. S., Vieira, J. G., da Silva, J. E., ve Lemos, P. H. (2019). A Combination Of Discrete Event Simulation And Multi-Crıieria Analysis To Configure Sugarcane Drop And Hook Delivery Systems. Engenharia Agrícola, 39(2), 248-256.
7. Emovon, I., Norman, R. A., ve Murphy, A. J. (2016). Metodology of Using an Integrated Averaging Technique and MAUT METHOD For Failure Mode and Effects Analysis. Journal of Engineering ve Technology (JET), 7(1), 140-155.
8. Garg, H. (2018). Multi-Criteria Decision-Making Method Based on Prioritized Muirhead Mean Aggregation Operator under Neutrosophic Set Environment. Symmetry, 10(7), 280.

9. Guo, S., ve Zhao, H. (2017). Fuzzy Best-Worst Multi-Criteria Decision-Making Method and Its Applications. Knowledge-Based Systems, 121, 23-31.
10. Hafezalkotob, A., Hafezalkotob, A., Liao, H., ve Herrera, F. (2019). An Overview of MULTIMOORA for Multi-Criteria Decision-Making: Theory, Developments, Applications, and Challenges. Information Fusion, 51, 145-177.
11. Ishizaka, A., ve Siraj, S. (2018). Are Multi-Criteria Decision-Making Tools Useful? An Experimental Comparative Study of Three Methods. European Journal of Operational Research, 264(2), 462-471.
12. Jahanshahloo, G. R., Lotfi, F. H., ve Izadikhah, M. (2006). Extension of the TOPSIS METHOD for Decision-Making Problems with Fuzzy Data. Applied Mathematics and Computation, 181(2), 1544-1551.
13. Kaplanoğlu, E. (2019). Entropi Tabanlı Maut Yöntemiyle Performans Ölçümü: MKEK Fabrikalarının Sıralanması. İşletme Ekonomi ve Yönetim Araştırmaları Dergisi, 2(1), 7-18.
14. Khoshnava, S. M., Rostami, R., Valipour, A., Ismail, M., ve Rahmat, A. R. (2018). Rank of green Building Material Criteria Based on the Three Pillars of Sustainability Using the Hybrid Multi Criteria Decision Making Method. Journal of Cleaner Production, 173, 82-99.
15. Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., ... ve Hong, H. (2019). A Comparative Assessment of Flood Susceptibility Modeling Using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology, 573, 311-323.
16. Kiracı, K., ve Asker, V. (2019). Hava Aracı Leasing Şirketlerinin Performans Analizi: Entropi Temelli Topsis Uygulaması. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (24), 17-28.
17. Liao, H., ve Wu, X. (2020). DNMA: A Double Normalization-Based Multiple Aggregation Method for Multi-Expert Multi-Criteria Decision Making. Omega, 94, 102058.
18. Madic, M., ve Radovanović, M. (2015). Ranking of Some Most Commonly Used Nontraditional Machining Processes Using ROV and CRITIC METHODs. UPB Sci. Bull., Series D, 77(2), 193-204.
19. Marković, V., Stajić, L., Stević, Ž., Mitrović, G., Novarlić, B., ve Radojičić, Z. (2020). A Novel Integrated Subjective-Objective MCDM Model for Alternative Ranking in Order to Achieve Business Excellence and Sustainability. Symmetry, 12(1), 164.
20. Nabeeh, N. A., Abdel-Basset, M., El-Ghareeb, H. A., ve Aboelfetouh, A. (2019). Neutrosophic Multi-Criteria Decision Making Approach for Iot-Based Enterprises. IEEE Access, 7, 59559-59574.
21. Noureddine, M., ve Ristic, M. (2019). Route Planning for Hazardous Materials Transportation: Multicriteria Decision Making Approach. Decision Making: Applications in Management and Engineering, 2(1), 66-85.
22. Opricovic, S., ve Tzeng, G. H. (2004). Compromise Solution by MCDM METHODs: A Comparative Analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.
23. Parihar, N. S., ve Bhargava, P. (2019). Multi Attribute Utility Theory (MAUT) Driven New Product Development Using Multi Criteria Decision Making (MCDM). International Journal of Engineering Trends and Technology, 67(5), 27 - 30.
24. Peng, J. J., Wang, J. Q., ve Yang, W. E. (2017). A Multi-Valued Neutrosophic Qualitative Flexible Approach Based on Likelihood for Multi-Criteria Decision-Making Problems. International Journal of Systems Science, 48(2), 425-435.
25. Phochanikorn, P., ve Tan, C. (2019). An Integrated Multi-Criteria Decision-Making Model Based on Prospect Theory for Green Supplier Selection under Uncertain Environment: A Case Study of the Thailand Palm Oil Products Industry. Sustainability, 11(7), 1872.
26. Rezaei, J., Hemmes, A., ve Tavasszy, L. (2017). Multi-Criteria Decision-Making for Complex Bundling Configurations in Surface Transportation of Air Freight. Journal of Air Transport Management, 61, 95-105.
27. Tian, Z. P., Wang, J., Wang, J. Q., ve Zhang, H. Y. (2017). An Improved MULTIMOORA Approach For Multi-Criteria Decision-Making Based On Interdependent Inputs Of Simplified Neutrosophic Linguistic Information. Neural Computing and Applications, 28(1), 585-597.
28. Villacreses, G., Gaona, G., Martínez-Gómez, J., ve Jijón, D. J. (2017). Wind Farms Suitability Location Using Geographical Information System (GIS), Based on Multi-Criteria Decision Making (MCDM) Methods: The Case of Continental Ecuador. Renewable Energy, 109, 275-286.
29. Vujičić, M. D., Papić, M. Z., ve 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.
30. Wang, C. N., Yang, C. Y., ve Cheng, H. C. (2019). Fuzzy Multi-Criteria Decision-Making Model for Supplier Evaluation and Selection in a Wind Power Plant Project. Mathematics, 7(5), 417.
31. Wu, K. J., Tseng, M. L., Chiu, A. S., ve Lim, M. K. (2017). Achieving Competitive Advantage Through Supply Chain Agility Under Uncertainty: A Novel Multi-Criteria Decision-Making Structure. International Journal of Production Economics, 190, 96-107.
32. Wu, X., ve Liao, H. (2018). An Approach to Quality Function Deployment Based on Probabilistic Linguistic Term Sets and ORESTE Method for Multi-Expert Multi-Criteria Decision Making. Information Fusion, 43, 13-26.
33. Wu, X., Liao, H., Xu, Z., Hafezalkotob, A., ve Herrera, F. (2018). Probabilistic Linguistic MULTIMOORA: A Multicriteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule. IEEE Transactions on Fuzzy Systems, 26(6), 3688-3702.
34. Yazdani, M., Zarate, P., Zavadskas, E. K., ve Turskis, Z. (2019). A Combined Compromise Solution (CoCoSo) Method for Multi-Criteria Decision-Making Problems. Management Decision.
35. Yu, S. M., Wang, J., Wang, J. Q., ve Li, L. (2018). A Multi-Criteria Decision-Making Model for Hotel Selection with Linguistic Distribution Assessments. Applied Soft Computing, 67, 741-755.
36. Zietsman, J., Rilett, L. R., ve Kim, S. J. (2006). Transportation Corridor Decision-Making with Multi-Attribute Utility Theory. International Journal of Management and Decision Making, 7(2-3), 254-266.