Generative artificial intelligence (Gen AI) has changed how businesses manage their supply chains by giving them new ways to use data to enhance decision‐making and improve their resilience, unlike regular automation systems, which only work within pre‐established parameters. While past studies acknowledge the influence of Gen AI on supply chain operations and management, the majority of studies have concentrated on its general implications, failing to thoroughly investigate the impact of Gen AI on supply chain resilience (SCR). The domain of Gen AI and SCR is still in its nascent stages, highlighting the necessity for a comprehensive bibliometric study and conceptual frameworks. This research work has used a bibliometric analysis of 299 articles from the Web of Science and Scopus databases up to December 2025. This research work also performed a systematic literature review of 37 articles and suggested a research framework for future researchers. It reveals that Gen AI has a significant impact on SCR and supply chain optimization to improve the predictive capability of the supply chains and finally impacts the overall supply chain performance. This study is a very useful resource because it shows where there are gaps in existing research, suggests new directions for research, and gives practical advice on how to put these ideas into practice in the industry.
This study aims to explore the ethical dilemmas posed by generative artificial intelligence (AI) and frames a responsible development framework for AI. It studies influential factors in ethical AI development. The primary study further discusses concerns about data privacy, autonomy and accountability in the context of generative AI systems.
The suggested framework then relies on gray influence analysis (GINA) to assess the degree of influence, which is essential to ethical AI development. It identifies eight key factors, which include transparency, accountability and human bias, as important for ethical AI development. The framework focuses on interventions through GINA to reduce bias and achieve equal AI systems.
This study shows that the most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility.” In contrast, the “Automation and replacement” factor was ranked the least influential.
These factors are systematically considered, with stakeholders developing strategies to facilitate ethical AI development and societal welfare. Future research directions would include the necessary competencies and resources for controlling generative AI, studies of biases in training data sets and the identification of optimal contexts for the deployment of generative AI systems.
This study uniquely explores and identifies influential factors in ethical AI development to address bias and advance fairness.