Effect of Algorithmic Incentive on the Work Well-being of Civil Servants

The rapid advancement of artificial intelligence (AI) technology has driven government organizations to widely adopt algorithmic tools in human resource management decision-making. However, existing research has yet to reach a consensus on the effectiveness of algorithmic decision-making, and there remains a lack of exploration into its relationship with civil servants’ work-related well-being in incentive contexts. Grounded in social information processing theory, this study constructs a theoretical model to examine how algorithmic decision-making influences civil servants’ work well-being, employing a mixed-method approach combining scenario-based experiments and critical incident techniques. The findings reveal that algorithmic HRM decisions significantly enhance organizational fairness perceived by civil servants, thereby improving their work well-being. Work meaningfulness serves as a moderator in this process, amplifying the positive effect of organizational fairness on well-being when work meaningfulness is high. This study not only expands the theoretical boundaries of algorithmic decision-making in the public sector but also offers policy insights for governments to optimize digital management and incentive practices for civil servants.