İşletme Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/403

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Department: search.filters.department.Çankaya UniversityPublisher: search.filters.publisher.IGI Global

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  • Book Part
    Citation - Scopus: 3
    Corporate Sustainability: the Use of Esg Scores in Finance Research
    (IGI Global, 2023) Yilmaz, I.S.; Usul, N.
    This chapter presents an overview of sustainable finance literature focusing on studies using environmental, social and governance (ESG) practices, and conducts a descriptive and comparative analysis of the Refinitiv ESG database worldwide. With increasing interest in ESG investing, it is common to integrate ESG factors into portfolio decisions. The authors first address the relevant literature to .set the background for further studies. The chapter documents an increasing trend in ESG reporting and points out differences in ESG scores in terms of development level, region, industry, and legal origin for the period 2002-2019. Theoretical models and empirical tests, which integrate ESG factors into corporate performance, can only be established by a better understanding of ESG databases. Therefore, further research in the Refinitiv ESG database, as well as other ESG databases, is significant. This chapter contributes to this stream of research by explaining the Refinitiv ESG database with descriptive and comparative analysis, paving the way for new research on the subject. © 2023, IGI Global. All rights reserved.
  • Article
    A New Clustering Method With Fuzzy Approach Based on Takagi-Sugeno Model in Queuing Systems
    (IGI Global, 2013) Zanjanbar, F.G.; Şentarli, I.
    In this paper, the authors propose a new hard clustering method to provide objective knowledge on field of fuzzy queuing system. In this method, locally linear controllers are extracted and translated into the first-order Takagi-Sugeno rule base fuzzy model. In this extraction process, the region of fuzzy subspaces of available inputs corresponding to different implications is used to obtain the clusters of outputs of the queuing system. Then, the multiple regression functions associated with these separate clusters are used to interpret the performance of queuing systems. An application of the method also is presented and the performance of the queuing system is discussed. Copyright © 2013, IGI Global.