Algorithmic Profiling in the Workplace: Employee Perceptions and Technostress

Abstract

Algorithmic profiling is becoming a common practice in workplaces, aimed at enhancing productivity and security. However, it raises concerns about employee privacy, algorithmic aversion, and technostress. This paper examines two cases of algorithmic profiling in a Norwegian municipality: a Security Awareness Program tailored to employee behaviors and a User Behavior Analytics (UBA) system that monitors endpoint activities. Using technostress theory, we investigated how algorithmic profiling affects employee sentiments, focusing on privacy concerns, perceived invasiveness, and stress responses. Our mixed-method case study reveals concerns about algorithmic fairness and heightened stressors such as techno-overload and techno-insecurity. The findings suggest that while algorithmic profiling can enhance productivity, it also can induce technostress, particularly through techno-insecurity, techno-complexity, and techno-invasion. To mitigate these challenges, ethical implementation and transparency are critical. We also provide recommendations for organizational practices and future research directions.