Are Platform Workers Simply Victims of Algorithmic Management?

In a new publication from our chair, “How Platform Workers Contest Algorithmic Management: Theorizing the Dynamics of Algoactivistic Practices,” published in Information Systems Research (VHB A+; UTD; FT50), Prof. Adam and fellow researchers use the example of Uber to show how platform workers actively challenge algorithmic management.

Research by Martin Adam shows that platform workers are not passive recipients of algorithmic management. How they respond depends on the resources available to them—leading to strategies such as self-optimizing, distancing, and confronting.

The takeaway: algorithmic management is a contested terrain: a continuous interplay in which platforms and workers adapt to each other and renegotiate control over working conditions.

Martin Adam: “A special thank you to Rideshare Drivers United in San Francisco for their invaluable support, openness, and feedback throughout the project over so many years. Many late-night calls, thoughtful conversations, and shared insights went into making this paper happen - grateful for the opportunity to learn from those experiencing algorithmic management firsthand.”

The paper is available here:


Link:
https://pubsonline.informs.org/doi/epdf/10.1287/isre.2024.0927

The following interview highlights the main idea of the publication:

When the Algorithm Becomes the Boss: How Platform Workers Regain Control

Platforms like Uber increasingly coordinate work through algorithms: they assign tasks, evaluate performance, and set incentives—often without direct contact with a human supervisor. Yet platform workers are far from powerless in the face of "Algorithmic management". A recent study published in Information Systems Research examines how they try to regain control over their working conditions. We spoke with the author, Prof. Dr. Martin Adam, about the key findings.

Professor Adam, what did you want to find out with your study?


We were interested in how platform workers deal with algorithmic control and, in particular, why some actively resist it while others do not. Previous research has often assumed that reduced autonomy almost automatically triggers resistance. Our findings reveal a much more complex picture: workers continuously reassess their situation and consider whether and how they can develop opportunities to act. A key factor is which resources they are able to draw on.

What kinds of resources are these?


We distinguish between three forms of so-called Resourcing. In Algorithm Resourcing, drivers, for example, try to understand how the algorithm works. In Market Resourcing, they create alternatives for themselves, for instance, by using multiple platforms. And in Voice Resourcing, they build opportunities to make themselves heard by the platform, for example through documentation, networks, or collective actions. These resources are distributed unevenly. Therefore, a lack of resistance does not automatically imply agreement with the system.

And what does resistance to the algorithm actually look like?


We identify three fundamentally different strategies. In Self-optimizing, workers try to improve their position within the system—for example, by selectively accepting tasks or taking advantage of loopholes. In Distancing, they reduce their dependence on a single platform. And in Confronting, they openly challenge platform decisions or structures, for instance through complaints or collective protest. Importantly, “Algoactivism” is therefore not merely reactive pushback. Workers also proactively seek to increase their autonomy and create greater scope for action.

Who wins this struggle for control—the platform or the workers?


It is not that simple. Instead, we observe a dynamic interplay. Drivers discover ways to use the algorithm to their advantage—and the platform then adjusts its system in response. Self-optimizing can therefore turn into a real “cat-and-mouse game”: workers may exploit a loophole for a time, until the platform closes it and they have to find a new strategy. Forms of Confronting can have more lasting effects, especially when they contribute to institutional or regulatory changes.

What can platform operators learn from this?


Resistance should not be seen simply as something platforms need to suppress. Complaints and other forms of Confronting can also reveal problematic rules, flawed decisions, or weaknesses in system design. Platforms should therefore communicate changes to their algorithmic systems more transparently and provide meaningful channels for feedback and escalation. These mechanisms can not only reduce conflict but also contribute to more sustainable and legitimate forms of Algorithmic Management.

What is the most important message of the study for you?


Algorithmic management is not simply a top-down form of control. Algorithms control work—but workers simultaneously interpret, circumvent, use, and reshape these systems through their behavior. Platform work is therefore a “contested terrain”: an ongoing struggle over who controls working conditions.