Browsing by Author "Turan, Hakan"

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Citation - WoS: 3
Citation - Scopus: 3
Analyzing Critical Success Factors of Sustainable Knowledge Management: an Interval-Valued Neutrosophic Approach
(Mdpi, 2024) Turan, Hakan; Bulak, Muhammet Enis; Buyukselcuk, Elif Caloglu
Knowledge management (KM) is a structured approach that includes the organized procedures of generating, capturing, arranging, storing, retrieving, distributing, and harnessing an organization's knowledge resources to attain its goals and improve its effectiveness. Encountering uncertainty and managing imprecise information are fundamental aspects of KM that cannot be avoided. In this context, sustainable KM aims to solve these issues and address prioritizing the long-term sustainability and efficiency of knowledge-related processes within an organization. The aim of this study is to structure a sustainable KM concept for organizations and identify the most common critical success factors (CSFs) with a novel analytical approach. In this context, the Interval-Valued Neutrosophic methodology, which is one of the multi-criteria decision methods (MCDMs), was adopted to evaluate and weight the determined CSFs. Four main headings-KM, environmental, economical, and social criteria-are evaluated along with their subfactors. Our findings show that KM is found to be the most important, and environmental factors followed KM. When the results are examined in terms of subfactors, cleaner production is found to be the most significant, with a global weight value of 11.13.
Evaluation of Barriers to the Integration of Renewable Energy Technologies into Industries in Türkiye
(MDPI, 2026) Caloglu Buyukselcuk, Elif; Turan, Hakan
The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with T & uuml;rkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems.
Evaluation of Barriers Toward Data-Driven Supply Chain Sustainability Via Single-Valued Pythagorean Piprecia
(Amer Inst Mathematical Sciences-AIMS, 2026) Turan, Hakan; Buyukselcuk, Elif Caloglu
Sustainable supply chain management (SSCM) is a holistic approach that encompasses economic, social, and environmental dimensions, enabling firms to enhance their long-term competitiveness by meeting legal requirements and strengthening brand equity. The effective implementation of this approach necessitates a strong emphasis on data-driven decision-making. Accordingly, we aimed to identify the key barriers hindering the implementation of data-driven sustainable supply chain practices and to explore potential strategies to overcome these challenges. In the initial phase of the study, a comprehensive literature review was conducted to identify the major barriers to implementing data-driven sustainable supply chains. Subsequently, the relative importance of these barriers was assessed with input from top and mid-level managers working in manufacturing sector enterprises. The identified barriers were then prioritized using the Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) method based on Pythagorean fuzzy numbers. Finally, solution proposals were developed to address the most critical barriers. The study revealed that organizational barriers constitute the most prominent category, representing 29.86% of the total identified obstacles. Closely following are technical barriers, which account for 26.41% and reflect the difficulties associated with implementing and integrating digital technologies. Internal and external environmental barriers are similarly substantial, comprising 25.87% of the total. In comparison, economic barriers make up the smallest share, with a relative weight of 17.86%. The number of researchers analyzing the importance weights of barriers in the context of SSCM 4.0 remains limited. The utilization of a more contemporary and robust method compared to previously applied techniques for determining these weights enhances the originality of this study.
Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
(MDPI, 2025) Turan, Hakan; Buyukselcuk, Elif Caloglu
Maintenance 4.0 studies have become a focus for managers and employees when developing effective and efficient maintenance policies. In this study, the barriers to Maintenance 4.0 applications in the textile industry are investigated, and these barriers are weighted using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on Pythagorean fuzzy numbers. Solutions to address these barriers are presented. As a result of this study, Organizational and Managerial emerged as the most important main criterion. Operational was identified as the second most significant main criterion, followed by Technical Competence. Data-Related and Cybersecurity ranked fourth in terms of importance. On the other hand, Human Resources and Training and Financial were found to be the least important main criteria. These two criteria received lower importance scores compared to the others, with Financial being the criterion with the lowest overall significance. Sensitivity analyses were performed for six different scenarios by changing the importance weights of the decision-makers. The ranking of the criteria only slightly changed with the weights; this means that the results obtained in Case 1 are robust and reliable. Even in Case 6, where the expert weight ratios were completely reversed, the results did not change significantly. This highlights an important point regarding the reliability of the assessment.