Generative AI-Powered Innovation on Data-Driven Financial Decision-Making in Operations and Supply Chain Management: A Moderated Mediation Analysis

The study aims to understand the effect of Generative AI-powered innovation on data-driven financial decision-making in operations and supply chain management (OSCM), directly and indirectly through ethical tension. Additionally, it seeks to explore how Generative AI ethics policy uncertainty and ethical leadership moderate the impact of Generative AI innovation on ethical tension and knowledge behavior in organizations. The study develops a research model based on socio-technical systems theory and contingency theory. The primary data is collected through surveys, and further direct relationships are tested using variance-based structural equation modeling, and moderated mediation hypotheses are tested using PROCESS. The study offers several novel insights. It shows that Generative AI-powered innovation increases ethical tensions. Moreover, ethical tensions have a significant negative impact on financial decision-making in OSCM. Our results indicate that while Generative AI enhances financial decision-making in OSCM and subsequently improves firm performance, high levels of policy uncertainty exacerbate ethical tensions arising from Generative AI innovation, thereby further impeding financial decision-making. Consequently, the mediation effect of ethical tension is stronger under these conditions. Finally, the study finds that ethical leadership serves as a protective buffer. Even when Generative AI creates ethical tensions, strong ethical leadership helps maintain decision-making integrity and mitigates the negative impact of ethical tensions on financial outcomes. This study is one of the first studies that theorizes the relationship between generative AI-powered innovation, ethical tensions, decision-making, ethical policy uncertainty, and ethical leadership.