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    A fuzzy Kano model proposal for sustainable product design: Mobile application feature analysis
    (Elsevier Ltd, 2025) Karakurt, Necip Fazıl; Cebi, Selcuk
    Companies aim to maximize profits by effectively designing mobile applications to promote their services in a competitive market. However, identifying the design features that significantly impact mobile applications is challenging due to their subjective nature. Traditional Kano approaches face limitations, such as information loss caused by considering only the most frequent values. To address these limitations, this study proposes a novel fuzzy Kano approach to better manage the subjectivity in human judgments and the uncertainty in user preferences. This approach uncovers hidden preference levels, accounts for uncertainties, resolves dual classification issues, compares membership degrees, and emphasizes subtle details that may otherwise be overlooked. The fuzzy Kano approach was applied to survey data from 100 participants, covering 33 mobile application features. By classifying these features, the fuzzy Kano model examined their influence on user satisfaction and quality perception. The results demonstrated the feasibility and effectiveness of the proposed method, identifying key features—such as Product Details, Order Management and Returns, and Product Opinions and Reviews—that, if absent, could lead to customer dissatisfaction. Additionally, the findings revealed significant differences between the fuzzy and traditional Kano models and highlighted variations in mobile application characteristics across different demographic groups, providing valuable insights for mobile application design. © 2025 Elsevier B.V.
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    Development of a decision support system for client acceptance in independent audit process
    (Elsevier, 2024) Cebi, Selcuk; Karakurt, Necip Fazil; Kurtulus, Erkan; Tokgoz, Bunyamin
    Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.
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    Optimizing Wind Energy Technology Selection Under Uncertain and Incomplete Information Using Fuzzy Best Worst Method and Fuzzy Information Axiom
    (Old City Publishing Inc, 2024) Cebi, Selcuk; Cem, Ecem; Unal, Gorkem; Karakurt, Necip Fazil
    Technology has a pivotal role in wind energy production, encompassing turbine design, control systems, and grid integration solutions. However, selecting the optimal technology investment presents a multifaceted challenge due to rapid industry evolution. Site-specific considerations, economic viability, reliability, durability, and integration with existing infrastructure all weigh heavily in the decision-making process. Environmental and societal impacts are rigorously supposed to be assessed for responsible energy production. A comprehensive approach, including a thorough evaluation of vendor and supplier capabilities, is deemed indispensable. To address these complexities, this study introduces an innovative approach to optimize wind turbine selection within established locations. The proposed methodology integrates the Fuzzy Best Worst Method (FBWM) and the Information Axiom, chosen for their adaptability in handling subjective expert responses. This combination aligns seamlessly with the nuanced nature of wind turbine technology assessment. The study offers a comprehensive review of relevant multi-criteria decision-making techniques, elaborates on the FBWM and Fuzzy Infor mation Axiom (FIA) approach, and presents apractical application. In the study, operational cost, maintenance cost, and power curve emerge as pivotal criteria. Ultimately, the study provides a robust framework for making informed and impactful technology investments in wind energy production.
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    Risk Analysis in the Food Cold Chain Using Decomposed Fuzzy Set-Based FMEA Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arslan, Özlem; Karakurt, Necip; Cem, Ecem; Cebi, Selcuk
    Risk analysis is employed across various domains, including the increasingly vital food supply chain, particularly highlighted by the COVID-19 pandemic. This study focuses on applying decomposed fuzzy sets (DFS), a novel extension of intuitionistic fuzzy sets, within the context of the food cold chain. The objective is to develop “Decomposed Fuzzy Set-Based FMEA (DF FMEA)” by extending the well-known failure mode and effect analysis (FMEA) method to DFS, to assess risks in the food cold chain. The functional and dysfunctional questions related to the severity, occurrence, and detectability of the identified risks; they were addressed to three experts working on the food cold chain. The purpose is to prevent an inconsistent assignment considering the uncertainty and indecision of decision makers. Due to the implementation of the DF FMEA, the identified risks were prioritized as follows: “Financial Risks” held the highest priority, followed by “Delivery Risks”, “Technological Ability Risks”, “Environmental Risks”, “Quality Risks”, and “Social Risks” with the lowest priority. The study’s practical impact lies in the innovative risk assessment method. By considering decision makers’ preferences and uncertainties, the DF FMEA approach enhances informed decision making. This contributes to a robust framework for addressing risks in the food cold chain, aiding practitioners in more effective risk management. © 2023 by the authors.