Institutional Logics : Guiding decision-making in Machine Learning adoption within financial operations

This research investigates how institutional logics which refers to rules, norms, and beliefs affect the adoption of Machine Learning (ML) in financial operations. It explores how these logics interact with technological advances and how organizations adjust their strategies to align with dominant logics within finance. The research focuses on understanding how institutional logics shape decision-making for ML adoption, including the motivations, challenges, and success factors. It addresses the question: "How do institutional logics impact the adoption of Machine Learning in financial operations?" It provides practical insights and recommendations for organizations looking to effectively integrate ML, align with prevailing norms, enhance efficiency, and gain a competitive edge.