Browsing by Author "Kazancoglu, Y"

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    A conceptual framework for barriers of circular supply chains for sustainability in the textile industry
    Kazancoglu, I; Kazancoglu, Y; Yarimoglu, E; Kahraman, A
    Circular economy is a contemporary concept including usage of renewable materials and technologies. The transition to the circular economy creates value through closed-loop systems, reverse logistics, eco-design, product life cycle management, and clean production. The aim of the study was to propose a holistic conceptual framework for barriers of circular supply chain for sustainability in the textile industry. Within this aim, an in-depth literature review on barriers was conducted by covering all supply chain stages and circular initiatives in textile industry. Then, a focus group study was implemented. In the focus group study, barriers related to supply chains that prevent companies to implement the circular economy were discussed and validated. As a result, a total of 25 barriers were classified under nine main categories such as (a) management and decision-making, (b) labour, (c) design challenges, (d) materials, (e) rules and regulations, (f) lack of knowledge and awareness, (g) lack of integration and collaboration, (h) cost, and (i) technical infrastructure.
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    Investigating barriers to circular supply chain in the textile industry from Stakeholders' perspective
    Kazancoglu, I; Kazancoglu, Y; Kahraman, A; Yarimoglu, E; Soni, G
    The objectives of this study are to understand the circular supply chain barriers for textile companies to implement the circular economy. Main contributions of the study were to propose a specific framework that reveals circular supply chain barriers in transition to circular economy with holistic view by encompassing all stakeholders, to reveal causal relationships among the circular supply chain barriers within textile industry. Causal relationships between the proposed circular supply chain barriers were identified by Fuzzy-Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The barriers are classified under cause and effect groups and related implications are proposed. The findings of this study are lack of collecting, sorting and recycling, reluctance for acceptance of CE model, and problems related to uniformity and standardisation are revealed as the most important barriers, respectively. Moreover, lack of technical knowledge is the most influencing factor, whereas, challenges in product design is the most influenced factor.
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    Detecting fake reviews through topic modelling
    Birim, SÖ; Kazancoglu, I; Mangla, SK; Kahraman, A; Kumar, S; Kazancoglu, Y
    Against the uncertainty caused by the information overload in the online world, consumers can benefit greatly by reading online product reviews before making their online purchases. However, some of the reviews are written deceptively to manipulate purchasing decisions. The purpose of present study is to determine which feature combination is most effective in fake review detection among the features of sentiment scores, topic distributions, cluster distributions and bag of words. In this study, additional feature combinations to a sentiment analysis are searched to examine the critical problem of fake reviews made to influence the decision-making process using review from amazon.com dataset. Results of the study points that behavior-related features play an important role in fake review classifications when jointly used with text-related features. Verified purchase is the only behavior related feature used comparatively with other text-related features.
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    The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
    Birim, S; Kazancoglu, I; Mangla, SK; Kahraman, A; Kazancoglu, Y
    In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.