Examine the enablers of generative artificial intelligence adoption in supply chain: a mixed method study
Ashish Jagdish Sharma, Bhawana Rathore
Abstract
Generative Artificial Intelligence (Gen-AI) is a burgeoning subfield of artificial intelligence that focuses on creating new content which is poised to revolutionise different industries by 2028. This study aims first to identify key enablers for the successful integration of Gen-AI into the supply chain with the help of Delphi and AHP techniques. Then, we screened these enablers categories and identified seven key enabler categories using the Delphi method. We computed the weights of those categories and ranked them on the basis of their weights with Ethical and Fair AI Practices and Public Trust and Societal Impact among the most significant. Second, this study categorised the tweet into positive, neutral, and negative sentiments using sentiment analysis and identified fifteen topics from secondary data. The research concludes with actionable strategies for practitioners and outlines the significance of ethical and trust-related enablers in the adoption of Gen-AI in the supply chain.