Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics

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dc.contributor.authorBaydas, Mahmut
dc.contributor.authorEren, Tevfik
dc.contributor.authorStevic, Zeljko
dc.contributor.authorStarcevic, Vitomir
dc.contributor.authorParlakkaya, Raif
dc.date.accessioned2024-02-23T14:44:51Z
dc.date.available2024-02-23T14:44:51Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractWhen it comes to choosing the best option among multiple alternatives with criteria of different importance, it makes sense to use multi criteria decision making (MCDM) methods with more than 200 variations. However, because the algorithms of MCDM methods are different, they do not always produce the same best option or the same hierarchical ranking. At this point, it is important how and according to which MCDM methods will be compared, and the lack of an objective evaluation framework still continues. The mathematical robustness of the computational procedures, which are the inputs of MCDM methods, is of course important. But their output dimensions, such as their capacity to generate well-established real-life relationships and rank reversal (RR) performance, must also be taken into account. In this study, we propose for the first time two criteria that confirm each other. For this purpose, the financial performance (FP) of 140 listed manufacturing companies was calculated using nine different MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). In the next stage, the statistical relationship between the MCDM-based FP final results and the simultaneous stock returns of the same companies in the stock market was compared. Finally, for the first time, the RR performance of MCDM methods was revealed with a statistical procedure proposed in this study. According to the findings obtained entirely through data analytics, Faire Un Choix Adequat (FUCA) and (which is a fairly new method) the compromise ranking of alternatives from distance to ideal solution (CRADIS) were determined as the most appropriate methods by the joint agreement of both criteria.en_US
dc.identifier.doi10.7717/peerj-cs.1350
dc.identifier.issn2376-5992
dc.identifier.pmid37153010en_US
dc.identifier.scopus2-s2.0-85159783285en_US
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.1350
dc.identifier.urihttps://hdl.handle.net/20.500.12452/17146
dc.identifier.volume9en_US
dc.identifier.wosWOS:000996199600001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMcdm Benchmarking And Evaluation Methodologyen_US
dc.subjectData Analyticsen_US
dc.subjectRank Reversalen_US
dc.subjectFinancial Performanceen_US
dc.titleProposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analyticsen_US
dc.typeArticleen_US

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