Call for Papers

Call for short papers (pdf)

As in poker, roulette, or dice games, organizations today find themselves operating in environments marked by unforeseeable dynamics. Emerging technologies, climate change, and geopolitical turmoil confront organizations with profound uncertainty about the future. How organizations come to terms with this uncertainty has become a matter of great interest for organizational scholars. A popular diagnosis is that “old” attempts to control the future through calculative practices have ultimately failed and are now being replaced by speculative and performative forms of future-making (Wenzel et al., 2020). Yet, we see this diagnosis to be at odds with empirical accounts of how organizations across sectors manage the uncertainty of the future (Heimstädt et al., 2021). Rather than witnessing a decline in calculative practices, we can observe what Power (2004) aptly describes as “metrological drama”: the crisis of calculative practices has not led to their abandonment but instead provoked their revision and expansion (Huber & Scheytt, 2013). The goal of this sub-theme is therefore to develop a deeper understanding of these novel, future-oriented, and data-centric calculative practices in and around organizations—arrangements we call machineries of anticipation.

Machineries of anticipation are already put to work in a wide range of organizational contexts. In police departments, algorithmic systems provide spatiotemporal predictions of crime (Heimstädt & Egbert, 2025; Waardenburg et al., 2022). In social services, scoring and calculation methods are used to anticipate social issues and allocate preventive measures (Ratner & Elmholdt, 2023). In medical institutions, machine-learning algorithms detect health issues and suggest treatment plans (Guler & Sundaresan, 2025). Insurance firms operationalize vast amounts of data to shift their anticipations from group-level averages to individualized forecasts (Cevolini & Esposito, 2020; Kiviat, 2019). In banking, financial models predict the profitability of economic transactions (Beunza, 2019; Hansen & Borch, 2021) and are increasingly complemented by algorithms that accelerate anticipatory trading processes (MacKenzie, 2021). More recently, Large Language Models and their expanding range of user-centric applications have begun to cut across all these domains. These “epistemic monsters” (Faraj et al., 2025) also function—often less visibly—as machineries of anticipation. Their rapid emergence underscores the need to understand more deeply the “family resemblance” (Wittgenstein, 1958) between diverse forms of anticipatory machinery.

Understanding these machineries of anticipation is crucial for organizational research because they do not provide neutral knowledge about the future. Instead, they constitute particular modes of knowledge production and powerful instruments of “anticipatory governance” (Flyverbom & Garsten, 2021). We need to examine the techniques and rationalities involved, how human and technological forms of intelligence shape anticipation, and how these processes affect organizational conceptions of truth and knowledge (Amoore, 2013). In this sense, machineries of anticipation represent some of the most significant contemporary configurations of power/knowledge (Foucault, 1982; Rose, 1999; Weiskopf & Hansen, 2023), and exploring their development, spread, and installment in organizations is more crucial in an era marked by widespread excitement about AI and other digital technologies as drivers of increased efficiency, information access, and societal transformation.

Although their history in organizational contexts is long (Desrosières, 1998; Porter, 1995), digitalization and datafication have profoundly transformed the meaning and scope machineries of anticipation have (Power, 2022). They are no longer limited by organizational boundaries but operate across globally connected infrastructures (Thylstrup et al., 2022), drawing on heterogeneous forms of labor—from invisible digital work to high-status data science (Justesen & Plesner, 2024; van den Broek, 2025). Being dispersed across organizational functions, the machinery of anticipation is an important component of how organizations experience and process the futures of different crises and (grand) challenges. For instance, anticipating the potential effects of climate change requires new forms of foresight, given its unprecedented scope and uncertainty. Yet even as such machineries help organizations navigate their societal responsibilities, we must also critically examine how new technologies are packaged and promoted as solutions to coordination problems while often reproducing existing inequalities.

To better understand how machineries of anticipation organize ahead of the game, we welcome conceptual, empirical, and methodological contributions from diverse theoretical traditions, including process and practice theory, sociomateriality, critical data/algorithm/organization studies, and science and technology studies. Topics and questions that we look forward to discussing during this sub-theme could include, but are not limited to, the following:

The politics of anticipatory machineries

• How are machineries of anticipation enrolled as part of political programs or ideologies?

• How can we understand and study machineries of anticipation as sites of political struggle in and around organization?

• What are competing techniques and rationalities involved in anticipation and prediction?

The people of anticipatory machineries

• How do human and technological forms of intelligence shape anticipation and prediction processes?

• How do human choices and algorithmic forms of selection and ordering shape prediction, such as creating increased homogeneity or divergence and fragmentation?

The practices of anticipatory machineries

• How do different forms of data collection and reuse shape predictions?

• How does the emergence of digital twins/digital doubles—both of human subjects and physical objects—lead to new forms of prediction?

• How do machineries of anticipation reshape how organizations perceive and process (grand) challenges in their surroundings?

• ​Amoore, L. (2013). The politics of possibility: Risk and security beyond probability. Duke University Press.
• Beunza, D. (2019). Taking the floor: Models, morals, and management in a Wall Street trading room. Princeton University Press.
• Cevolini, A., & Esposito, E. (2020). From pool to profile: Social consequences of algorithmic prediction in insurance. Big Data & Society, 7(2). https://doi.org/10.1177/2053951720939228
• Desrosières, A. (1998). The politics of large numbers: A history of statistical reasoning. Harvard University Press.
• Faraj, S., Torrents, J. P., Mantere, S., & Bhardwaj, A. (2025). A time for monsters: Organizational knowing after LLMs. Strategic Organization. Advance online publication. https://doi.org/10.1177/14761270251410675
• Flyverbom, M., & Garsten, C. (2021). Anticipation and organization: Seeing, knowing and governing futures. Organization Theory, 2(3). https://doi.org/10.1177/26317877211020325
• Foucault, M. (1982). The subject and power. Critical Inquiry, 8(4), 777–795.
• Guler, I., & Sundaresan, S. (2025). Algorithmic recommendation tools and experiential learning in clinical care. Organization Science, 36(5), 1786–1802. https://doi.org/10.1287/orsc.2022.16738
• Hansen, K. B., & Borch, C. (2021). The absorption and multiplication of uncertainty in machine-learning-driven finance. The British Journal of Sociology, 72(4), 1015–1029. https://doi.org/10.1111/1468-4446.12880
• Heimstädt, M., & Egbert, S. (2025). Actionable predictions: How designers of algorithmic systems calibrate criminal futures. Big Data & Society, 12(2). https://doi.org/10.1177/20539517251340636
• Heimstädt, M., Egbert, S., & Esposito, E. (2021). A pandemic of prediction: On the circulation of contagion models between public health and public safety. Sociologica, 14(3), 1–24. https://doi.org/10.6092/issn.1971-8853/11470
• Huber, C., & Scheytt, T. (2013). The dispositif of risk management: Reconstructing risk management after the financial crisis. Management Accounting Research, 24(2), 88–99. https://doi.org/10.1016/j.mar.2013.04.006
• Justesen, L., & Plesner, U. (2024). Invisible digi-work: Compensating, connecting, and cleaning in digitalized organizations. Organization Theory, 5(1). https://doi.org/10.1177/26317877241235938
• Kiviat, B. (2019). The moral limits of predictive practices: The case of credit-based insurance scores. American Sociological Review, 84(6), 1134–1158. https://doi.org/10.1177/0003122419884917
• MacKenzie, D. (2021). Trading at the speed of light: How ultrafast algorithms are transforming financial markets. Princeton University Press.
• Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity in science and public life. Princeton University Press.
• Power, M. (2004). Counting, control and calculation: Reflections on measuring and management. Human Relations, 57(6), 765–783. https://doi.org/10.1177/0018726704044955
• Power, M. (2022). Theorizing the economy of traces: From audit society to surveillance capitalism. Organization Theory, 3(3). https://doi.org/10.1177/26317877211052296
• Ratner, H. F., & Elmholdt, K. (2023). Algorithmic constructions of risk: Anticipating uncertain futures in child protection services. Big Data & Society, 10(2), Article 20539517231186120. https://doi.org/10.1177/20539517231186120
• Rose, N. (1999). Powers of freedom: Reframing political thought. Cambridge University Press.
• Thylstrup, N. B., Hansen, K. B., Flyverbom, M., & Amoore, L. (2022). Politics of data reuse in machine learning systems: Theorizing reuse entanglements. Big Data & Society, 9(2). https://doi.org/10.1177/20539517221139785
• Van den Broek, E. (2025). Unpacking AI at work: Data work, knowledge work, and values work. Information and Organization, 35(3), Article 100584. https://doi.org/10.1016/j.infoandorg.2025.100584
• Waardenburg, L., Huysman, M., & Sergeeva, A. V. (2022). In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms. Organization Science, 33(1), 59–82. https://doi.org/10.1287/orsc.2021.1544
• Weiskopf, R., & Hansen, H. K. (2023). Algorithmic governmentality and the space of ethics: Examples from “people analytics.” Human Relations, 76(3), 483–506. https://doi.org/10.1177/00187267221075346
• Wenzel, M., Krämer, H., Koch, J., & Reckwitz, A. (2020). Future and organization studies: On the rediscovery of a problematic temporal category in organizations. Organization Studies, 41(10), 1441–1455. https://doi.org/10.1177/0170840620912977
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