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Algorithmic Management and Employee Autonomy: Impacts on Creativity in Remote First Companies Wusono, Medi; Yona, Mira; Ariyati, Yannik; Hasibuan, Rahman; Siregar, Hanafi; Nasution, Habibuddin; Simanjuntak, Wilmar; Barus, Peromikha; Yolanda, Siti
Journal of Economics and Management Scienties Volume 8 No. 1, December 2025 (Accepted)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jems.v8i1.288

Abstract

This study explores the influence of algorithmic management on employee autonomy and creativity within remote first companies. As organizations increasingly rely on automated systems to assign tasks, monitor performance, and standardize workflows, concerns arise about how such systems impact workers' creative capacities. Using a qualitative case study approach, data were collected through semi-structured interviews with 15 employees across various digital industries. Thematic analysis revealed that while algorithmic oversight enhances operational clarity and consistency, it also imposes rigid structures that often reduce discretionary decision making and psychological safety. Autonomy emerged as a key mediating factor: employees who retained some control over how they worked were more likely to report creative engagement, while those facing strict digital control reported demotivation and cognitive fatigue. Emotional responses, such as anxiety, trust, and detachment, were also found to shape creative outcomes. The study further identified design implications for algorithmic systems, emphasizing transparency, human override mechanisms, and participatory features that support innovation. These findings suggest that creativity and algorithmic management are not mutually exclusive but require careful system design that balances control with employee empowerment. The research contributes to a deeper understanding of how digital oversight affects innovation in distributed work settings and offers practical guidance for organizations navigating remote workforce management through algorithms.