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.

In recent years there is a trend of consuming natural products for a sustainable and healthier life. Therefore, firms began aligning their strategy with sustainability by communication strategies that they produce natural products, which are better for health as well as the environmental sustainability. However, sometimes these claims may be deceptive. The purpose of this paper is to understand the consumers' purchasing intentions toward products claiming naturalness in their advertising and packaging strategies. This research also examined greenwashing perceptions and their potential roles in purchasing intentions. In‐depth face‐to‐face interviews carried out with 20 Turkish women regarding personal care products (local brand and international brand). The findings of the interviews revealed eight themes (perceived greenwashing, perceived green image, price perception, environmental concern, green trust, skepticism, perceived risk, and purchase intention). This study contributes to predict a framework from consumer viewpoint for identifying the themes related to greenwashing.

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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 B Yigit Kazancoglu