Algorithmic Control: A Legitimacy Perspective on Worker-level Implications (AlgoWork)

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The main outcomes of our initial research project are (1) a conceptual framework (5RSM framework) that distinguishes seven forms of algorithmic control (AC) and a corresponding scale for measuring perceived AC; (2) empirical insights into the use of AC and its effects on workers in the context of traditional organizations; as well as (3) a nomological network that sheds light on how platform workers judge the legitimacy (e.g., in terms of autonomy, fairness, privacy, technostress) of different AC forms and how these assessments in turn affect their cognitive reactions (e.g., the intention to continue their platform work). Our project work and results to date have indicated that further research is needed to develop a holistic understanding regarding the worker-level implications of AC systems (as opposed to the implications of individual AC forms), as well as to gain a deeper understanding of workers’ behavioral resistance reactions (so-called “algoactivistic” practices) to such systems (as opposed to their predominantly cognitive reactions to distinct AC forms). This gives rise to three research questions (RQs), which represent the focus of the follow-up project: (1) What are key conceptual dimensions (and related characteristics) of AC systems and what archetypical combinations of these dimensions/characteristics can be found in platform-based and/or traditional organizations? (2) What AC system configurations used in traditional organizations are judged as (not) legitimate by workers, and why? (3) What algoactivistic practices do platform workers use to ‘push back’ against AC systems, and how are these practices connected to one another from a process perspective? To address RQ1, we intend to develop a taxonomy of AC systems and, based on this, empirically derive archetypes of such systems. To address RQ2, we will first extract the dimensions and characteristics of AC systems that are critical for legitimacy judgments by traditional workers, and then identify AC system configurations that are judged as (not) legitimate by workers. To address RQ3, a dynamic process model of algoactivistic practices in the platform context will be developed, building on a categorization of such practices. The overarching project results will provide actionable insights and recommendations for the ethically responsible design and use of AC systems and support corresponding regulatory efforts. In addition, the project aims to contribute new insights to the broader societal discussion regarding the use of AC systems and their implications for the future of work.