A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches

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dc.authoridOZCIL, ABDULLAH/0000-0001-6304-2986
dc.authoridBaydas, Mahmut/0000-0001-6195-667X
dc.authoridGumus Ozuyar, Sevilay Ece/0000-0002-1957-3648
dc.contributor.authorBaydas, Mahmut
dc.contributor.authorYilmaz, Mustafa
dc.contributor.authorJovic, Zeljko
dc.contributor.authorStevic, Zeljko
dc.contributor.authorOzuyar, Sevilay Ece Gumus
dc.contributor.authorOzcil, Abdullah
dc.date.accessioned2024-11-07T13:34:40Z
dc.date.available2024-11-07T13:34:40Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min-max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the Maximum normalization-based fuzzy integrated CODAS procedure was proposed to decision-makers to measure the economic performance of the countries.
dc.identifier.doi10.1186/s40854-023-00588-x
dc.identifier.issn2199-4730
dc.identifier.issue1
dc.identifier.urihttps://doi.org/10.1186/s40854-023-00588-x
dc.identifier.urihttps://hdl.handle.net/11480/16106
dc.identifier.volume10
dc.identifier.wosWOS:001181375200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofFinancial Innovation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectGDP
dc.subjectMCDM
dc.subjectFuzzy CODAS
dc.subjectEconomic performance
dc.titleA comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches
dc.typeArticle

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