Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry

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dc.contributor.authorKara, Karahan
dc.contributor.authorAcar, Avni Zafer
dc.contributor.authorPolat, Mustafa
dc.contributor.authorOnden, Ismail
dc.contributor.authorYalcin, Galip Cihan
dc.date.accessioned2024-07-18T20:42:36Z
dc.date.available2024-07-18T20:42:36Z
dc.date.issued2024
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThe green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research.en_US
dc.identifier.doi10.1016/j.eswa.2024.123668
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85187789529en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2024.123668
dc.identifier.urihttps://hdl.handle.net/11411/7358
dc.identifier.volume249en_US
dc.identifier.wosWOS:001210540800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGreen Supplier Selectionen_US
dc.subjectGreen Performanceen_US
dc.subjectSustainable Sourcingen_US
dc.subjectMultiple Regression Analysisen_US
dc.subjectDempster-Shafer Theoryen_US
dc.subjectEvidential F-Mcdmen_US
dc.subjectChain Management-Practicesen_US
dc.subjectProduct Innovationen_US
dc.subjectOrganizational Cultureen_US
dc.subjectFinancial Performanceen_US
dc.subjectCompetitive Advantageen_US
dc.subjectFirm Performanceen_US
dc.subjectDecisionen_US
dc.subjectTopsisen_US
dc.subjectDeterminantsen_US
dc.subjectModelen_US
dc.titleDeveloping a hybrid methodology for green-based supplier selection: Application in the automotive industryen_US
dc.typeArticleen_US

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