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    Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Acar, Avni Zafer; Polat, Mustafa; Onden, Ismail; Yalcin, Galip Cihan
    The 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.
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    Exploring the adoption of the metaverse and chat generative pre-trained transformer: A single-valued neutrosophic Dombi Bonferroni-based method for the selection of software development strategies
    (Pergamon-Elsevier Science Ltd, 2024) Onden, Abdullah; Kara, Karahan; Onden, Ismail; Yalcin, Galip Cihan; Simic, Vladimir; Pamucar, Dragan
    The contemporary era has witnessed remarkable developments that seek to transform and reshape traditional software development methodologies. Notably, artificial intelligence (AI) supported software development as well as software development in virtual reality environments have gained considerable prominence. This article introduces software development strategies to examine how software developers and companies respond to this transformation. Also, an advanced decision model is developed using the alternative ranking order method accounting for two-step normalization (AROMAN) method and further analyzed with the single-valued neutrosophic set-based AROMAN technique. The single-valued neutrosophic weighted Dombi Bonferroni operator is employed in the analysis process. This research offers two case studies investigating the preferences of developers and managers in software development strategies. The first case study examines the preferences of developers, while the second focuses on the preferences of managers. In both case studies, three fundamental software development methods are presented. These include the traditional developers approach, AI-supported developers approach, and mixed reality and AI-supported developers approach. These methods are ranked based on expert opinions concerning 10 criteria that influence the software development process. In both case studies, output quality is identified as the most influential criterion. From the perspective of software development methods, in both case studies, the mixed reality and AI-supported developers approach is identified as the most effective. Recommendations are provided for developers and managers. The findings also have significant implications for guiding developers and managers in making informed decisions and optimizing software development practices to align with the evolving AI and virtual reality landscape.