 Algorithmic Control: Workers’ Perceptions, Judgments, and Reactions in the Era of Artificial IntelligenceArmin Alizadeh Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Abstract: Organizational control—the mechanisms through which organizations align individual behaviors towards organizational goals—has evolved continuously with the emergence of new technologies. Recently, with the surge of ubiquitous computing, expanding data collection, and increasingly powerful algorithms, organizational control is once again undergoing a fundamental transformation. Workers are now assigned tasks, evaluated, and even compensated by algorithmic systems rather than human supervisors—a phenomenon referred to as algorithmic control (AC). AC first emerged in online labor platforms, where millions of distributed workers are controlled through smartphone apps without any human oversight. Now it is spilling over into traditional organizations as well. This rapid and continuous adoption of AC across organizations has outpaced our theoretical and empirical understanding of organizational control, leaving many of its consequences for workers uncertain. Specifically, the information systems (IS) and related literature lacks a sound conceptual framework and measurement instrument for worker-level perceptions of AC; one that integrates the distinct characteristics of AC and applies across diverse organizational settings. Furthermore, while prior studies demonstrate that workers’ judgments and reactions to AC can be positive, negative and even contradictory, little is known about which specific system design choices trigger such judgments and reactions. Against this background, this dissertation examines the worker-level implications of AC through two overarching research questions. First, it asks what the key conceptual dimensions of AC systems are from the worker’s perspective and how these dimensions can be measured. Second, it investigates how specific design choices in AC systems give rise to positive or negative worker judgments and reactions. These questions are addressed through four interrelated articles. The first article contributes to the first research question by developing a framework and measurement instrument for worker-level perceived AC (PAC). Drawing on recent conceptual frameworks and interviews with workers, it identifies seven forms of AC: algorithmic recommending, restricting, requiring, monitoring, rating, rewarding, and sanctioning. Following established scale development methodology, the study examines the PAC scale’s psychometric properties in both platform-based and traditional work contexts, demonstrating its reliability and validity. This provides a solid foundation for cumulative and comparable research on worker-level implications of AC. Continuing on the first research question, the second article examines the broader, system-level characteristics of AC. It applies established taxonomy development methodology and draws prior literature and on both real-world examples of AC systems to identify their key dimensions and characteristics. Specifically, it highlights four organizational dimensions (organizational scope, control authority, human supervisor involvement, coerciveness) and three technological dimensions (digital interface, data sources, control tasks) that together characterize AC systems. To illustrate the taxonomy’s applicability and usefulness, the article applies it to three real-world cases at Uber, Amazon Warehouses, and Microsoft Viva Insights. The resulting taxonomy enables researchers to systematically classify and compare AC systems, highlight similarities and differences, and situate them within their broader socio-technical context. Taken together with the worker-level PAC dimensions identified in the first article, it provides a more holistic understanding of how AC is embedded and perceived in the workplace. The third article focuses on the first part of the second research question by examining how design choices in AC systems shape workers’ legitimacy judgments. Building on a three-layered conceptual framework consisting of a control, data, and organizational embedding layer, it identifies important AC system design choices within each layer. An experimental vignette study with 329 workers systematically varies these design choices and measures their effects on workers’ perceptions of autonomy, exploitation, and ultimately their legitimacy judgments of AC systems. The findings show that design choices in the data and organizational embedding layers are at least as, if not more, critical than AC mechanisms themselves in shaping workers’ judgments of AC systems. The fourth article turns to the second part of the second research question by investigating how workers react to configurations of AC system design choices. It focuses on configurations that foster algorithmic self-control, defined as an individual’s use of AC systems and related digital devices to align their behavior with self-set, work-related goals. To study this, it conducts an online experiment in which design choices such as data visibility, data granularity, and goal-setting authority are varied, and applies fuzzy-set qualitative comparative analysis to examine how these choices combine to shape workers’ engagement with AC. The findings show that engagement is highly contingent on these combinations and that multiple pathways exist for fostering algorithmic self-control. The article provides both theoretical and practical insights into resolving the tension of control and resistance thereby leveraging the benefits of AC for organizations and workers alike. Taken together, these four articles contribute to a more comprehensive understanding of the worker-level implications of AC. By conceptualizing how workers perceive AC forms and broader systems configurations, and by developing and validating the PAC measurement instruments, they provide researchers with robust frameworks for systematic and cumulative investigation of AC. At the same time, the findings offer managers valuable insights into which design choices trigger positive or negative perceptions and how organizations can move beyond a potential tug-of-war between control and resistance by fostering algorithmic self-control. As AC continues to profoundly reshape our organizations, this dissertation aspires to contribute to a future in which these technologies create value for both organizations as well as the people that make up this organization. Date: 2026-03-03 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:160778 Access Statistics for this paper More papers in Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Contact information at EDIRC. |
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