Call for Papers

Call for short papers (pdf)

Scale is a key category for organizational practice, politics, and research. Scalability has become a central feature of organizing today, whether in the realms of innovation, new business models, or the vast computing infrastructures currently being developed for artificial intelligence. It is also a crucial aspect of organizational politics: multicrises are often addressed through “grand challenges,” and the climate crisis in particular demands responses at a planetary scale, while critical organizational scholarship frequently calls for scaling down. In research practice, however, scale remains a thorny issue. While there is a certain hesitation toward scaling, for example in refusing to engage in “totalizing” thought, scale is integral to various methods of studying organization, which emphasize that phenomena unfold across scales and must be traced accordingly. This sub-theme asks for submissions that carefully explore contemporary issues of scaling in organizational practice, politics, and research.

As researchers, we continuously make decisions about the scale of the phenomena we observe: the measures we apply, the ways we delineate their boundaries, how we understand their relationships with larger and smaller “others.” These choices shape the levels of our analytical frameworks and the scale and scope of the theorizing we undertake. Yet, the notions of scale, scaling, and scalability remain undertheorized. Research in management and organization studies is populated with diverse and sometimes contradictory definitions of these terms (Colombo et al., 2024; Coviello et al., 2024; Palmié et al., 2023). In this sub-theme, we invite scholars to examine how scale matters in organizing and in organization scholarship. 

“Scale,” as anthropologist and computer scientist Christopher Kelty (2000) observes, is one of those words that are too rich, “taking more than their share of coincidence and teaching too much by their usage.” As a noun, it evokes a journey through balancing instruments; measuring systems; ratios, extent, size, and magnitude; progressing series of musical notes; ladders or rankings; and even the skin of fish or reptiles. As a verb, its meanings include to measure; to represent in proportional dimensions—either by reducing or increasing size according to a commonly accepted measure; to uniformly enlarge or reduce; to climb; and, not least, to form or remove the scales from fish (see Kelty, 2000).

Today, the noun scale seems to be most often used to indicate largeness, and as a verb, a new “monstrosity” of “it scales” has taken the reins (Kelty, 2000), creating intriguing connections between the term’s rich historical polysemy, organizations, and digital technology. If something scales, it is “both big and small at the same time”—for example, “a small organization whose software caters to a large number of users has a technology that scales” (Kelkar, 2016, p. 44). In management and organization studies, recent scholarship has sought to rein in plural and incoherent notions of scale: Palmié et al. (2023) identified four uses of the scaling concept in relation to markets, finances, volumes, and organizations. In a similar vein, Coviello et al. (2024) sift through current literature to sort notions of scale in terms of “(a) scaling as an organizational process, (b) scalability as an ordinary organizational capability, and (c) scale-up as a phase of organizational development.”

Inspired mainly by the impressive growth of Silicon Valley companies, scaling up has been captivating the imagination of practitioners and scholars alike (Shepherd & Patzelt, 2022). Scalability is obsessed over in various realms, from innovation to grand challenges, which in the view of science and technology studies scholars calls for a “politics of scale” (Pfotenhauer et al., 2022). But “scale thinking” (Hanna & Park, 2020) carries a deeper normative orientation: the scalability of startups, technological solutions, and, for example, social impact, appears normatively desirable. Where capitalist organizational practices such as logistics continuously measure, reconfigure, and redesign the planetary (Parikka, 2023), exhibiting a desire for “a trans-scalar smooth world” (Rossiter, 2016, p. 37), or big tech companies build cloud infrastructure for “hyper-scalability” (Narayan, 2022; van der Vlist et al., 2024), the challenge for organization scholarship is at least twofold. On the one hand, methodologically, with regard to overcoming long-held hesitations to think at scale and at the risk of totalizing, for example, when it comes to thinking about the totality of capital (Buck-Morss, 1995) or in accounting for the scale of capital’s global abstractions (Toscano & Kinkle, 2015). On the other hand, it is also a question of politics, for example, with regard to developing a “multi-scalar politics” in relation to debates around economic planning (Sorg, 2023), or to consider other forms of scalability altogether (Kostakis et al., 2024).

The anthropologist Anna Tsing (2012) sees the preference for scalability as embedded in Western history and apparent in colonial plantations and industrial factories. Following Tsing, we invite authors to consider scaling—the making of the living matter of the world into pixel-like parts, which are crafted as stable, uniform, separate, interchangeable, and nested—not as a given but instead to observe how it is achieved and what effects it gives rise to. Modern scalability projects leave behind the ruins of “nonscalability,” she observes. But scaling does not need to be seen as “inherently evil [or] intrinsically virtuous” (Hecht, 2018, p. 115). For instance, climate change is knowable thanks to digital machines, operating across scales from dispersed sensors to global networks (Edwards, 2010; Pringle et al., 2019), while the very idea of a machinic envisioning of the planetary may be contested through the multiscalar and the diffractive “ways of seeing, situating, and making sense of planetary phenomena and ecologies” (Richardson & Munster, 2023). Accounting for the complex and expansive temporal and spatial scales of the Anthropocene challenges us to carefully engage with scale in organizational practice, politics, and research. 

We invite conceptual, methodological, and empirical contributions engaging with—but not limited to—the questions below:

• Disentangling the scales: theorizing scale, (non)scalability, and growth. 

• Theories and epistemologies of scale and nonscalability in organization studies. How do we account for the lived experience of “pluripresence” (Bird-David, 2017) across scales?

• Methods for foregrounding scale: What research apparatus allows us to remain attuned to scale(s)? What devices—such as interscalar vehicles (Hecht, 2018)—can help us think across scales? What do we gain from studying phenomena at multiple levels (Burton-Jones & Gallivan, 2007), and what are the possible pitfalls? 

• Does organization and management scholarship have a bias for large organizations, similar to the bias for large mammals in nature conservation (Guénard et al., 2025)? Tracing scale thinking (Hanna & Park, 2020) and scalar blindness (Bird-David, 2017). 

• Are other scalabilities possible? Tracing the scales, practices of scaling, and notions of scalability at work in contemporary capitalist and anticapitalist organizational practice (e.g., Colombo et al., 2024).

• The scale of the world’s phenomena and organizations: previous research suggested that some issues in the world—either too small or too huge—escape organizational attention (Bansal et al., 2018). How can organizational scholarship help address this issue? 

• ​Bansal, P., Kim, A., & Wood, M. O. (2018). Hidden in plain sight: The importance of scale in organizations’ attention to issues. Academy of Management Review, 43(2), 217–241. https://doi.org/10.5465/amr.2014.0238
• Bird-David, N. (2017). Us, relatives: Scaling and plural life in a forager world. University of California Press.
• Buck-Morss, S. (1995). Envisioning capital: Political economy on display. Critical Inquiry, 21(2), 434–467.
• Burton-Jones, A., & Gallivan, M. J. (2007). Toward a deeper understanding of system usage in organizations: A multilevel perspective. MIS Quarterly, 31(4), 657–679. https://doi.org/10.2307/25148815
• Colombo, L. A., Bailey, A. R., & Gomes, M. V. P. (2024). Scaling in a post-growth era: Learning from social agricultural cooperatives. Organization, 31(6), 907–928. https://doi.org/10.1177/13505084221147480
• Coviello, N., Autio, E., Nambisan, S., Patzelt, H., & Thomas, L. D. W. (2024). Organizational scaling, scalability, and scale-up: Definitional harmonization and a research agenda. Journal of Business Venturing, 39(5). https://doi.org/10.1016/j.jbusvent.2024.106419
• Edwards, P. N. (2010). A vast machine: Computer models, climate data, and the politics of global warming. MIT Press.
• Guénard, B., Hughes, A. C., Lainé, C., Cannicci, S., Russell, B. D., & Williams, G. A. (2025). Limited and biased global conservation funding means most threatened species remain unsupported. Proceedings of the National Academy of Sciences of the United States of America, 122(9). https://doi.org/10.1073/pnas.2412479122
• Hanna, A., & Park, T. M. (2020). Against scale: Provocations and resistances to scale thinking. arXiv. https://arxiv.org/abs/2010.08850
• Hecht, G. (2018). Interscalar vehicles for an African Anthropocene: On waste, temporality, and violence. Cultural Anthropology, 33(1). https://doi.org/10.14506/ca33.1.05
• Kelkar, S. (2016). Platformizing higher education: Computer science and the making of MOOC infrastructures [Doctoral dissertation, Massachusetts Institute of Technology]. https://dspace.mit.edu/bitstream/handle/1721.1/107312/972736338-MIT.pdf?sequence=1
• Kelty, C. (2000). Scale, or the fact of [Unpublished manuscript]. https://kelty.org/or/papers/unpublishable/Kelty_Scale_2000.pdf
• Kostakis, V., Lemos, L., & Kouvara, A. (2024). Another scalability is possible! From non-scalability to cosmolocal scalability. tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, 22(2), 620–629. https://doi.org/10.31269/triplec.v22i2.1535
• Narayan, D. (2022). Platform capitalism and cloud infrastructure: Theorizing a hyper-scalable computing regime. Environment and Planning A: Economy and Space, 54(5), 911–929. https://doi.org/10.1177/0308518X22109402
• Palmié, M., Parida, V., Mader, A., & Wincent, J. (2023). Clarifying the scaling concept: A review, definition, and measure of scaling performance and an elaborate agenda for future research. Journal of Business Research, 158. https://doi.org/10.1016/j.jbusres.2022.113630
• Parikka, J. (2023). “A natural history of logistics” and other studio briefs. Cultural Politics, 19(2), 177–199. https://doi.org/10.1215/17432197-10909672
• Pfotenhauer, S., Laurent, B., Papageorgiou, K., & Stilgoe, J. (2022). The politics of scaling. Social Studies of Science, 52(1), 3–34. https://doi.org/10.1177/03063127211048945
• Pringle, T. P., Koch, G., & Stiegler, B. (2019). Machine. University of Minnesota Press; meson press.
• Richardson, M., & Munster, A. (2023). Pluralising the planetary: The radical incompleteness of machinic envisioning. Media+Environment, 5(1). https://doi.org/10.1525/001c.87980
• Rossiter, N. (2016). Software, infrastructure, labor: A media theory of logistical nightmares. Routledge.
• Shepherd, D. A., & Patzelt, H. (2022). A call for research on the scaling of organizations and the scaling of social impact. Entrepreneurship Theory and Practice, 46(2), 255–268. https://doi.org/10.1177/1042258720950599
• Sorg, C. (2023). Failing to plan is planning to fail: Toward an expanded notion of democratically planned postcapitalism. Critical Sociology, 49(3), 475–493. https://doi.org/10.1177/08969205221081058
• Toscano, A., & Kinkle, J. (2015). Cartographies of the absolute. Zero Books.
• Tsing, A. L. (2012). On nonscalability: The living world is not amenable to precision-nested scales. Common Knowledge, 18(3), 505–524.
• Vlist, F. van der, Helmond, A., & Ferrari, F. (2024). Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence. Big Data & Society, 11(1). https://doi.org/10.1177/20539517241232630