Examining the Effects of Algorithm-Driven Performance Monitoring on Employee Trust, Psychological Safety and Organizational Commitment in Digital Workplaces

Abstract

The increasing use of artificial intelligence, algorithmic management, employee analytics, and digital monitoring systems has transformed conventional approaches to workplace performance management. The present study examined the effects of algorithm-driven performance monitoring on employee trust, psychological safety, and organizational commitment in digitally managed workplaces. A quantitative, cross-sectional survey design was adopted. Data were obtained from 300 employees working in technology-enabled organizations where digital systems were used for performance assessment, productivity tracking, task allocation, attendance monitoring, or work-activity analysis. A structured questionnaire containing measures of algorithm-driven performance monitoring, employee trust, psychological safety, and organizational commitment was administered using a five-point Likert scale. Descriptive statistics were used to summarize participant characteristics and study variables, while multiple linear regression was used to examine the predictive effects of algorithm-driven monitoring on the three dependent variables. The illustrative findings showed that higher perceived monitoring intensity was associated with lower employee trust, psychological safety, and organizational commitment. Algorithm-driven monitoring explained 23.0% of the variance in employee trust, 27.0% in psychological safety, and 19.0% in organizational commitment. The findings suggested that intensive digital monitoring may produce unintended organizational consequences when employees perceive monitoring systems as intrusive, opaque, or primarily control-oriented. Transparent communication, proportional data collection, human oversight, employee participation, and clear mechanisms for contesting algorithmic decisions were therefore identified as important managerial considerations. The study contributes to the emerging literature concerning the human consequences of algorithmic performance management.