ALGORITHMIC MANAGEMENT AND OPERATIONS MANAGEMENT IN THE FUTURE OF HUMAN WORK: A CRITICAL REVIEW AND THE H.U.M.A.N DESIGN FRAMEWORK
Keywords:
algorithmic management, operations management, employee autonomy, workplace trust, psychosocial risks, human-centered design, future of workAbstract
The global diffusion of algorithmic management—the process where data-driven computer systems automatically allocate work tasks, follow task execution, select execution steps, evaluate employee performance, and enforce organizational discipline—has fundamentally transformed labor relations across modern business operations globally. Initially focused only within digital labor platforms as a mechanism of gig economy coordination, these systems have rapidly expanded to traditional, standard workplaces. However, these algorithmic systems have then rapidly expanded to traditional, standard workplaces, bridging physical labor in logistics and warehousing with cognitive tasks in remote-first and hybrid knowledge economies. This paper conducts a critical review of the literature regarding algorithmic management and its deep intersections with operations management in the business sector. While algorithmic management oversight can lead to greater operational effectiveness, higher productivity, a more consistent working pace, and lower coordination costs, research indicates that unchecked systems have serious negative effects on human work. However, research shows that these algorithmic management systems can also have serious negative effects on human work. Specifically, unmanaged algorithmic management systems usually reduce employee autonomy, bring much higher-tension surveillance anxiety, downgrade tacit and experiential knowledge, and disrupt traditional supervisory (control) and relational networks inside operational functions in an organization. By analyzing these outcomes through the lenses of the Job Demands-Resources model, Socio-Technical Systems theory, and the Demand-Control-Support stress model, we identify how automated "6Rs" sub-mechanisms—restricting, recommending, recording, rating, replacing, and rewarding—can lead to severe psychosocial risks. To bridge the existing gap between using algorithmic management technology for some managerial function such as planning and control on one side and employee well-being on the other, we propose the H.U.M.A.N framework (mnemonic involving Human-in-Command, User Involvement, Mitigation of stress, Algorithmic transparency, and Negotiated parameters) as an actionable socio-technical design protocol to audit the ethical implementation of these new workplace technologies. We also establish a 2x2 Algorithmic Management Seeding Matrix as a specific tool that can guide information technology companies, software developers, and operations management to transition algorithmic management systems from strong-arm surveillance mechanisms to collaborative, trust-based augmentation tools
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