Algorithmic Transformation in Performance Management: The Ethical and Psychological Effects of AI-Based Workplace Surveillance on Employee Calmness and Organizational Citizenship Behaviors in Smart Organizations

Abstract

This study examines how the algorithmic transformation of performance management through AI-based workplace surveillance affects employee calmness and organizational citizenship behaviors within Iraqi smart organizations. The study adopts a descriptive–analytical design and collects primary data using a structured questionnaire with a five-point Likert scale administered to a sample of 420 employees drawn from five organizations that actively rely on digital monitoring practices. The independent variable, AI-based workplace surveillance, is operationalized through three dimensions: surveillance intensity, perceived transparency and fairness, and algorithmic control with automated intervention. The dependent variable is measured through two dimensions: employee calmness at work and organizational citizenship behaviors. Reliability results indicate strong internal consistency, with Cronbach’s alpha reaching 0.922 for the full instrument. Construct adequacy and internal consistency are supported by KMO of 0.914 and a significant Bartlett’s test at p < 0.001. Normality tests indicate non-normal distributions, justifying the use of a generalized linear model. The findings show that surveillance intensity and algorithmic control have statistically significant negative effects on the combined outcome of calmness and citizenship behaviors, whereas perceived transparency and fairness exert a statistically significant positive effect. The study offers practical implications for governance of AI surveillance by reducing excessive monitoring, strengthening transparency and fairness, and embedding human review to mitigate psychological strain while sustaining discretionary, pro-organizational behaviors.