Modeling the Efficiency of Nudging in Public Administration

The study focuses on developing a predictive model to evaluate efficiency of soft behavioral regulation methods (nudging) in public administration. Despite widespread adoption of nudging techniques in government management globally, the research specifically aimed at identifying and assessing criteria for their efficiency remains limited. The paper analyzes existing approaches to evaluating the efficiency of nudging and proposes a new predictive model based on supervised machine learning techniques. The research involves collecting and processing data related to nudging practices in governmental organizations, determining key factors that influence the efficiency of these methods, and building a predictive model using classification algorithms. The resulting model incorporates 15 essential features and achieves the prediction accuracy of 82%. Findings of this study can be applied in both academic research and practical evaluations of nudging implementations in public administration.