In recent years, interest in economic, environmental and social sustainability has increased significantly. Companies are gradually adopting behaviors aimed at achieving the Sustainable Development Goals, which represent a crucial aspect of the 2030 Agenda. In practice, they are currently incorporating organizational strategies that jointly consider environmental, social and corporate governance (ESG), with the aim of generating value for all stakeholders. This paper aims to review, through a recognized seven-step procedure, the current literature on the impact that ESG practices have in industry, with a focus on the reduction of carbon emissions. The results are extremely useful for both researchers and entrepreneurs. The bibliometric analysis shows that interest in the ESG paradigm has grown considerably in the last three years. Furthermore, through the analysis of 13 key documents, it emerges that (i) the European community is pushing significantly towards the adoption of ESG practices through new regulations, (ii) the link between industrial operations and carbon emissions can no longer be neglected within the factory of the future, and (iii) significant efforts are still needed to standardize, in terms of variables and KPIs, the adoption of ESG-centric strategies.

The adoption of Artificial Intelligence is transforming enterprises worldwide, influencing various aspects of business operations and affecting all dimensions of the triple bottom line. Companies ready to leverage the potential of this technology can significantly improve their performance. Therefore, it is crucial to understand the relationship between internal capabilities and contextual factors on one hand, and Artificial Intelligence adoption and its impact on performance on the other. Within this research framework, this study introduces an extended dynamic capability framework to analyze the interplay between internal factors, Artificial Intelligence adoption, and companies performance. The proposed model is tested using Partial Least Squares—Structural Equation Modeling on survey data from 210 Italian innovative startups. The findings indicate that companies with well‐developed dynamic capabilities, enabling them to adapt more effectively to environmental changes, are also better equipped to adopt Artificial Intelligence, leading to positive social, economic, and environmental performance.

Purpose: Using AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors – agile leadership (AL), innovation orientation (IO) and individual creativity (IC) – impact innovation managers' use of generative AI tools, such as ChatGPT (CGA). Design/methodology/approach: The research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance. Findings: The research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA. Originality/value: Our findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.