Please use this identifier to cite or link to this item: https://ruomoplus.lib.uom.gr/handle/8000/1916
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dc.contributor.authorLibório, Matheus Pereirael
dc.contributor.authorKaragiannis, Roxaniel
dc.contributor.authorDiniz, Alexandre Magno Alvezel
dc.contributor.authorEkel, Petr Iakovlevitchel
dc.contributor.authorVieira, Douglas Alexandre Gomesel
dc.contributor.authorRibeiro, Laura Cozziel
dc.date.accessioned2024-12-20T07:50:25Z-
dc.date.available2024-12-20T07:50:25Z-
dc.date.issued2024-02-06-
dc.identifier.urihttps://ruomoplus.lib.uom.gr/handle/8000/1916-
dc.description.abstractThis research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.el
dc.language.isoenel
dc.publisherMDPIel
dc.relation.ispartofEntropy (Basel, Switzerland)el
dc.rightsCC0 1.0 Παγκόσμια*
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Διεθνές*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFRASCATI__Social sciences__Economics and Businessel
dc.subject.othercomposite indicatorsel
dc.subject.othercost of doing businessel
dc.subject.otherdiscriminating powerel
dc.subject.otherhybrid weighting schemeel
dc.subject.otherinformation entropyel
dc.titleThe Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicatorsel
dc.typejournal articleel
dc.identifier.doi10.3390/e26020143-
dc.identifier.scopus2-s2.0-85185942066-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85185942066-
dc.relation.issn1099-4300el
dc.description.volume26el
dc.description.issue2el
dc.description.startpage143el
dc.contributor.departmentPontificia Universidade Catolica de Minas Geraisel
dc.contributor.departmentDepartment of Business Administrationel
dc.contributor.departmentPontificia Universidade Catolica de Minas Geraisel
dc.contributor.departmentPontificia Universidade Catolica de Minas Geraisel
dc.contributor.departmentFederal Center of Technological Education of Minas Geraisel
dc.contributor.departmentPontificia Universidade Catolica de Minas Geraisel
dc.relation.eissn1099-4300el
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypejournal article-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.journal.journalissn1099-4300-
crisitem.journal.journaleissn1099-4300-
crisitem.author.deptUniversity of Macedonia-
crisitem.author.departmentDepartment of Business Administration-
crisitem.author.orcid0000-0002-5753-2433-
crisitem.author.facultySchool of Business Administration-
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