Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2023Scopus SJRQ3Evaluation of social factors within the circular economy concept for European countries
Kaya, Sema Kayapinar; Ayçin, Ejder; Pamucar, Dragan
The circular economy (CE) is a rapidly growing theme, particularly in the European Union (EU), that encourages the responsible and circular use of resources in the field of contributing to long-term development. The environmental and economic magnitude of development are frequently debated in the subject of CE whereas, social aspects have been only rarely and sporadically combined into the CE. Therefore, this study involves a multidisciplinary holistic framework of clustering methods and Multi-Criteria Decision Making (MCDM) for evaluating the CE paradigm in relation to EU countries' social growth. In terms of the social impact of CE strategies, the k-means cluster analysis was used to group the 27 EU members with similar social impact levels. Subsequently, a novel integrated CRITIC (The criteria importance through intercriteria correlation) and MEREC (Method based on the Removal Effects of Criteria) methods were employed for determining the weights of social indicators in order to balance the two weighting methods. In order to select the best country in each cluster, a novel extension of the hybrid MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method combined with Power averaging and Heronian operator was developed. According to final solutions, Western European countries in the first cluster have the lowest unemployment and corruption rates, with the Netherlands having the best performance in this cluster. The second cluster includes countries with the lowest employment rates following university graduates between the ages of 20 and 64. Accordingly, Croatia is the best social performance in this cluster. Countries with the highest income distribution and unemployment rate are in the 3rd cluster, the best country in this group is Lithuania. Finally, the results obtained from the innovative MCDM methods have validated in order to demonstrate the proposed methodology's applicability.
AtıfScopus30WoS23OpenAlex42
İncele - MakaleAçık Erişim2021Scopus SJRQ2Towards the circular economy: Analysis of barriers to implementation of Turkey’s zero waste management using the fuzzy DEMATEL method
Ayçin, Ejder; Kayapinar Kaya, Sema
Zero waste has an important position in the circular economy model in terms of manufacturing recyclable products instead of products to be consumed quickly, reducing the amount of waste, developing and implementing recycling/reuse technologies, and thus ensuring resource efficiency. A zero-waste strategy is one of the basic steps to achieve the goal of the circular economy. The number of studies conducted on Turkey’s zero waste management strategies referring to the circular economy is quite limited. To fill this gap in the literature, this study aims to identify the potential barriers which have an important role in zero waste management practices in Turkey. Through a broad literature review and expert opinions, 12 key barriers for zero waste implementation in Turkey have been defined in view of circular economy principles. Then, fuzzy DEMATEL methodology is employed to examine which is the most influential barrier affecting zero waste management success and to identify the interdependence of these barriers. The results indicate that uncertainty of the goals and tactics relevant to the circular economy and lack of financial and economic aid are key causal barriers that affect Turkey’s zero waste management.
AtıfScopus65WoS49OpenAlex85
İncele - MakaleYalnız künye2018CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY
Dogan, Onur; Ayçin, Ejder; Bulut, Zeki Atil
Companies need to understand the customers' data better in all aspects. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers became very important for customer-company engagement. Segmenting the customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques widely have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We detected that the current customer segmentation which built by just considering customers' expense is not sufficient. Hence, models that recommended in this research are expected to provide better customer understanding, well-designed strategies, and more efficient decisions.
AtıfWoS30İncele