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In recent years, researchers and practitioners have paid a great deal of attention to the circular economy (CE) due to its potential social and environmental benefits. However, limited attention has been devoted in the literature to studying the barriers to CE implementation in the apparel retail industry in emerging and developing nations besides China. Consequently, the purpose of this paper is to analyse the barriers to implementing CE in the Indian garment retail market.

This exploratory study uses a comprehensive literature analysis to identify the micro-level impediments to CE adoption in India's garment retail industry. The study aims to identify these barriers using a Delphi process, consisting of three stages. The first stage involves literature reviews and expert opinions, while the second and third stages involve survey methods with 14 industry professionals and academics. The use of the two primary data sources allows for triangulation of the data, which improves the validity of the findings and enables broader conclusions to be drawn from the results.

This study indicates that the top three challenges to implementing CE principles in the Indian apparel retail industry are “standards and regulation barriers” (84%), “strategic barriers” (82%) and “supply chain management and technology barriers” (79%). Strategies for overcoming these obstacles include gaining top management support, coordinating supply chain components, training and employee motivation.

This study considers only Indian apparel retail industry, and the practical implications could potentially limit the study to emerging Asian economies.

To the best of the authors’ knowledge, this is the first research of its type to explore CE obstacles at the organizational level in the Indian garment retail business. Thus, it contributes to a greater understanding of the topic and enables practitioners to develop effective policies and business strategies for CE and sustainability.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.