Rodyti trumpą aprašą

dc.contributor.authorAmiri, Maghsoud
dc.contributor.authorHashemi-Tabatabaei, Mohammad
dc.contributor.authorKeshavarz-Ghorabaee, Mehdi
dc.contributor.authorAntuchevičienė, Jurgita
dc.contributor.authorŠaparauskas, Jonas
dc.contributor.authorKeramatpanah, Mohsen
dc.date.accessioned2023-09-18T16:41:00Z
dc.date.available2023-09-18T16:41:00Z
dc.date.issued2023
dc.identifier.other(crossref_id)148117563
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/115950
dc.description.abstractModern technologies have changed human life and created a generation of customers who have different needs compared to the past. Considering Industry 4.0 and its drivers, the implementation of digital banking (DB) has faced various challenges that are caused by emerging trends. Both Industry 4.0 and DB are contemporary concepts, and decision-makers are often faced with uncertainties in their decisions regarding the implementation of DB and its indicators. For this purpose, a novel multi-criteria group decision-making approach has been developed utilizing the best–worst method (BWM) and α-cut analysis as well as trapezoidal fuzzy numbers (TFNs). By reviewing the literature and using experts’ opinions, the DB implementation criteria are determined, and considering an uncertain environment, the criteria are prioritized using the proposed method. Then, the available DB models and alternatives are examined based on the decision criteria and the importance of each criterion. This research contributes to the existing literature by identifying and prioritizing the criteria necessary for the successful implementation of DB, taking into account emerging trends and technological advances driven by Industry 4.0. Subsequently, the study prioritizes the prevalent models of DB based on these criteria. This study proposes a decision-support framework for dealing with ambiguity, lack of information, insufficient knowledge, and uncertainty in decision-making. The framework uses TFNs to account for imprecision and doubt in decision-makers’ preferences. Additionally, the study presents a fuzzy multi-criteria group decision-making approach that enables a group of experts to arrive at more reliable results. The proposed approach can help improve the quality of decision-making in complex and uncertain situations. The results of this research show that human resources, rules and regulations, and customer satisfaction are the most important criteria for implementing DB. In addition, the open, blockchain, and social banking models are the crucial models that significantly cover the implementation criteria for DB.eng
dc.formatPDF
dc.format.extentp. 1-43
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://www.mdpi.com/2075-1680/12/6/516
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:166974466/datastreams/MAIN/content
dc.titleEvaluation of digital banking implementation indicators and models in the context of industry 4.0: A fuzzy group MCDM approach
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references188
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionAllameh Tabataba’i University
dc.contributor.institutionGonbad Kavous University
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyStatybos fakultetas / Faculty of Civil Engineering
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldS 003 - Vadyba / Management
dc.subject.vgtuprioritizedfieldsFM0101 - Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enmulti-criteria decision-making (MCDM)
dc.subject.enbest–worst method (BWM)
dc.subject.entrapezoidal fuzzy numbers (TFNs)
dc.subject.enIndustry 4.0
dc.subject.endigital banking
dc.subject.enBanking 4.0
dc.subject.enservice quality (SERVQUAL)
dcterms.sourcetitleAxioms: Special issue: Multiple-criteria decision-making and computational intelligence: recent applications II
dc.description.issueiss. 6
dc.description.volumevol. 12
dc.publisher.nameMDPI
dc.publisher.cityBasel
dc.identifier.doi148117563
dc.identifier.doi001014033900001
dc.identifier.doi10.3390/axioms12060516
dc.identifier.elaba166974466


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