The rise of artificial intelligence is fundamentally transforming the competitive landscape across various sectors, offering visionary enterprises new pathways to innovation development and to get a competitive edge. AI leverages data, analysis, and observations to perform tasks without hard coding, and benefits from self-learning and continuous improvement. We use Systems Thinking to frame how managers may adopt and integrate AI in business activities. We also investigate the motivations driving entrepreneurs to adopt AI solutions, and how they may impact on sustainable business model innovation, by administering a questionnaire to a sample of innovative Italian SMEs to get a comprehensive overview of the dynamics influencing AI adoption in business. This study sheds light on the intricate relationship between technology, sustainability, and corporate innovation. It offers both valuable insights for future research and for strategic managerial decisions on AI integration. Furthermore, it helps the development of innovative, sustainable business models in the evolving landscape of the Great Reset.

This study presents a systematic review of new product development (NPD) in the textile and fashion industry, conducted through bibliometric techniques and thematic synthesis under the PRISMA 2020 protocol. By combining performance analysis, science mapping, and qualitative coding, the research aims to clarify how the literature has addressed sustainability in relation to new product design and innovation, with specific attention to the evolution of research streams, the barriers and enablers of circular business models, and the role of consumer engagement and digital technologies in fostering trust, transparency, and process innovation. The findings reveal three major trajectories: (i) growing attention to consumer engagement and communication strategies to enhance awareness and trust; (ii) increasing diffusion of circular business models, still challenged by internal resistance and systemic constraints; and (iii) the expanding use of digital technologies, such as blockchain and artificial intelligence, to enable traceability, reuse, and innovation in production and distribution. Overall, the study provides a comprehensive framework that integrates communication, circular economy, and digitalization, offering both theoretical insights and practical implications for aligning sustainability objectives with product innovation strategies.

The study examines whether circular economy (CE) disclosures lead to tangible environmental improvements reflected in ESG performance or primarily serve reputational objectives linked to greenwashing. It investigates how disclosures on energy efficiency, resource reduction, water efficiency, and emission reduction influence ESG performance and whether external audits enhance disclosure credibility. Using a quantitative design, the analysis covers 1247 European firms (2015–2024) with data from Thomson Reuters, sustainability reports, and Orbis. Multiple regression models assess the relationship between CE disclosures and ESG performance, considering the moderating role of external audits. CE disclosures are positively associated with ESG performance, with energy efficiency and resource reduction showing the strongest effects. Water and emission disclosures also yield positive impacts, though the former is weakened when paired with external audits. Overall, external audits are positively related to ESG performance and strengthen the effect of energy and emission disclosures but may reduce the effectiveness of water‐related disclosures, highlighting the need for context‐specific assurance strategies. This study contributes to the debate on greenwashing by showing that external verification can strengthen the effectiveness of CE disclosures in improving ESG performance. It underscores the importance of transparent and verifiable reporting for corporate governance, investment decisions, and policy frameworks promoting genuine environmental progress.

The objective of this paper is to examine the existing academic literature on the intersection between the circular economy (CE) and artificial intelligence (AI), with the dual aim of identifying the main themes shaping the current scholarly debate and highlighting emerging topics that may guide future research directions. To this end, a comprehensive bibliometric literature review was conducted using the Web of Science database as the primary source of academic publications. The study employs advanced analytical techniques, including co-word analysis, co-occurrence network analysis, co-citation analysis, and thematic mapping, to systematically explore the intellectual and conceptual structure of this research field. These methods allow for the identification of dominant themes, key research clusters, and knowledge gaps that define the evolving relationship between AI and CE. The findings offer a data-driven overview of how AI technologies are being conceptualized as enablers of circular economy principles and practices. This study contributes to the literature by providing a structured understanding of current research trends and by outlining future directions for scholars, policymakers, and practitioners committed to advancing sustainable, AI-enabled circular economy strategies.