Balancing Algorithmic Precision and Human Judgment: The Impact of AI Management Tools on Managerial Efficacy and Employee Burnout

Abstract

The rise of algorithmic and intelligent performance management platforms (AIPMPs) is redefining managerial roles and employee experiences in digital workplaces. Grounded in Social Cognitive Theory and the Job Demands–Resources (JD–R) Model, this study examines how AIPMPs influence managerial self-efficacy and employee burnout. Using cross-sectional data from 312 managers across technology and professional service sectors in the United States, the analysis reveals that AIPMP use significantly enhances managerial self-efficacy by providing real-time feedback and predictive insights. However, increased algorithmic oversight also intensifies job demands, indirectly contributing to higher levels of employee burnout. Moderation analysis demonstrates that team size plays a pivotal role: in smaller teams, AIPMPs function as supportive resources, whereas in larger teams, they act as stress-inducing demands. The findings extend Social Cognitive Theory by identifying algorithmic feedback as a new form of technological mastery experience and refine the JD–R framework by positioning AI systems as both resources and demands. Practically, the study highlights the need for human-centered AI design, leadership training for digital empathy, and ethical governance of algorithmic decision-making. Overall, the results underscore that sustainable digital leadership depends on balancing algorithmic precision with human judgment to promote both performance and well-being in AI-mediated management environments.

Keywords: AI-Assisted Performance Management, Algorithmic Management, Digital Leadership, Employee Burnout, Human-Centered AI, Job Demands–Resources Model, Managerial Self-Efficacy, Social Cognitive Theory

View PDF