<![CDATA[Digital transformation has accelerated the adoption of algorithmic management in human resource management (HRM) through the use of algorithm-based systems and artificial intelligence (AI) for task allocation, performance evaluation, and decision making. Despite its promise of efficiency and objectivity, empirical evidence on its impact on human-centered work outcomes remains limited. This study examines the effects of algorithmic management on job autonomy, team engagement, and employee innovation, as well as the mediating roles of job autonomy and team engagement. A quantitative explanatory research design was employed. Data were collected from 150 employees working in digital organizations that have implemented algorithmic management systems. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that algorithmic management has a significant negative effect on job autonomy (β = −0.42, p < 0.01), while exerting significant positive effects on team engagement (β = 0.31, p < 0.01) and employee innovation (β = 0.24, p < 0.01). Furthermore, job autonomy (β = −0.16) and team engagement (β = 0.10) significantly mediate the relationship between algorithmic management and employee innovation. These findings suggest that the influence of algorithmic management on innovation is indirect and operates through employees’ psychological and social mechanisms. This study highlights the necessity of human-centric design in digital HRM to balance efficiency and employee innovation.]]>
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