Algorithmic impact in daily work

Abstract

With a focus on day-to-day work dynamics, the thesis investigates how algorithmic management (AM) is changing the role of traditional frontline service personnel. Although AM in gig economies has been the subject of much research, less is known about the ramifications for frontline-labour environments. This study combines qualitative information from nine semi-structured interviews conducted in a variety of industries with additional internet reviews, all within the theoretical framework of the Job Demands-Resources (JD-R) model. The results show that AM provides few job resources, such as operational efficiency, while increasing job demands, such as surveillance and decreased autonomy. Workers experience major cognitive, emotive, and behavioural reactions as a result of this imbalance, which might range from increased stress to decreased organisational commitment. On the other hand, several individuals observed increased efficiency and autonomy, which reflects the dual nature of AM's influence. By modifying the JD-R model to account for employee responses, the study adds to the body of knowledge by providing complex insights into how AM influences cognitive, affective, and performance-based outcomes. The necessity of balanced AM integration techniques that reduce demands while boosting supportive resources is high-lighted by the practical advice. This study contributes to the understanding of human-technology interaction in frontline roles by addressing the opportunities and constraints of AM. It also offers managers and organisations practical insights for navigating the future of work.