Algorithmic Management and Psychological Safety in AI-Driven Hybrid Workplaces

Amena Muhammed Ali

Abstract

The increasing adoption of algorithmic management systems in AI-driven organizations is transforming how employees are monitored, evaluated, and directed. While artificial intelligence enhances efficiency and data-driven decision-making, its impact on employee psychological safety remains underexplored. This study investigates how algorithmic management intensity influences psychological safety and employee engagement in digitally monitored workplaces. Drawing on organizational behavior theory, a quantitative research design was employed using survey data collected from 312 employees across technology-enabled firms. Structural Equation Modeling (SEM) was applied to examine the relationships among algorithmic monitoring, perceived transparency, human managerial support, psychological safety, and engagement. The findings reveal that high levels of algorithmic control negatively affect psychological safety when transparency and human oversight are limited. However, when AI systems are perceived as transparent and supportive, psychological safety significantly improves, leading to higher employee engagement. Furthermore, psychological safety mediates the relationship between algorithmic management and engagement outcomes. These results highlight the importance of responsible AI governance and balanced human–AI interaction in maintaining trust within organizations. The study contributes to emerging research on AI in management by integrating algorithmic management into the psychological safety framework and offers practical guidance for organizations implementing AI-driven performance systems.

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