Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method

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dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorRazavian, Behnam
dc.contributor.authorHamed, S.Masoud
dc.contributor.authorFayyaz, Maryam
dc.contributor.authorGhasemi, Peiman
dc.contributor.authorÖzkul, Seçkin
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2025-05-12T13:56:11Z
dc.date.available2025-05-12T13:56:11Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractThe optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375. © 2024 The Authors
dc.identifier.citationRazavian, B., Fayyaz, M., Ghasemi, P., Ozkul, S., & Tirkolaee, E. B. (2024). Addressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method. Journal of Innovation & Knowledge, 9(4), 100593.
dc.identifier.doi10.1016/j.jik.2024.100593
dc.identifier.issn2530-7614
dc.identifier.issue4
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.jik.2024.100593
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7255
dc.identifier.volume9
dc.identifier.wosWOS:001342456100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorTirkolaee, Erfan Babaee
dc.institutionauthoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofJournal of Innovation and Knowledge
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBig data
dc.subjectData-sharing Challenges
dc.subjectGrey Best-Worst Method
dc.subjectSustainable Smart Cities
dc.subjectUrban Development
dc.subjectZero-sum Grey Game
dc.titleAddressing barriers to big data implementation in sustainable smart cities: Improved zero-sum grey game and grey best-worst method
dc.typeArticle

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