Why compose single cases

Studies of single cases do not add up

Our field inherited a limit from economics: studies of single cases do not add up. When interviews and surveys showed that managers do not set prices and wages at the margin (Hall & Hitch, 1939; Lester, 1946), economists replied that "to be important ... a hypothesis must be descriptively false in its assumptions" (Friedman, 1953:14; Machlup, 1946). Machlup (1967) later gave three reasons not to study real firms: cost, observability and composition, since he "might not be able to rely on the composite results obtained from a firm-by-firm analysis" (Machlup, 1967:30). Cyert and March (1963:128) had already answered the first two: their computer model of one department store's pricing, built from interviews with its buyers, made the mechanisms observable and cost only machine time. Composition is the constraint he kept: such a model "has usually not been transferable to other cases" (Machlup, 1967:16). Organization Science's inaugural editorial accepted the same limit, setting "in-depth understanding of a few cases" apart from "generalizability" (Daft & Lewin, 1990:6). Eisenhardt (1989:545) identified the practical obstacle: "With more than 10 cases, it quickly becomes difficult to cope with the complexity and volume of the data."

Why qualitative research

We focus on qualitative research for three reasons. First, the limit binds hardest there. Qualitative findings rest on case detail that existing approaches struggle to preserve at population scale. Our field remains "largely a science of verbal reports" (Daft & Lewin, 1990:3), and LLMs can now read such reports at population scale. Second, forms that emerge instead of being specified in advance need categories that also emerge from the record. Third, accumulation is least settled there. For some, transparency is a required step toward "replicable and cumulative knowledge" (Aguinis & Solarino, 2019:1292), and knowledge "accumulates vertically, building higher and higher on tested foundations" (Pratt et al., 2020:4); for others, it accumulates horizontally, as "a stock of broad principles generated across many past situations, each of them unique" (Pratt et al., 2020:5). Costly methods and unobservable phenomena deny others the chance to replicate or extend a finding; results that cannot be related are harder to build on. Our model organism addresses all three constraints: LLMs reduce the cost of reading the record, researchers can inspect the discussions and pixel changes, and the instrument composes the coded cases while retaining their detail.

Humans and LLMs are differently bounded

Humans and LLMs are differently bounded. Humans trade detail about any one case for being able to compose knowledge of many cases (Ragin, 1987). When LLMs form concepts, they compress towards statistical efficiency and drop fine-grained distinctions that humans keep (Shani et al., 2026). They are predisposed "to identify, replicate, and reinforce dominant language and patterns" (Jowsey et al., 2025:1) and are "wildly conservative (tied to past data) as they seek to reduce surprise" (Felin & Holweg, 2024:351). Abduction "starts with the identification of an anomaly, that is, an observation which cannot be explained by existing theories" (Goldenstein et al., 2026:10). Our instrument makes departures from the population model explicit by separating case coding from population composition. Each coder first reads only its own community's full record. When the population model returns, the coder checks it against that record and reports where it needs to change.

This motivates a division of learning. Qualitative researchers aim "to generate novel theoretical insights" (Lê & Schmid, in Gioia et al., 2022:243) and "formulate the issues and troubles that we notice in the world as problems of social science" (Bechky & Davis, 2025:11). LLMs appear "(currently) unqualified to assess whether a conjecture represents a theoretical contribution" (O'Sullivan & Ravasi, 2026:17). We give the community coders a common procedure to follow (Grodal et al., 2021; Than et al., 2025), "so that results are comparable and knowledge can be accumulated" (Günther et al., 2026:863). We judge whether these accounts explain the evidence and contribute to theory. We remain responsible for interpretation and reflexivity.

The instrument: one model, detailed at every level

The instrument is a harness in which LLM agents read the record under explicit virtual context management (Packer et al., 2024). One coding agent per community reads that community's full record and codes its organizing; an aggregator composes the coded cases into a population-level qualitative model (Gioia et al., 2013; Grodal et al., 2021). The model returns to the coders, who identify where it fails their cases. The aggregator revises the model and identifies organizational forms by grouping communities that request similar changes. An agent need not hold everything at once: it keeps notes outside its context window, fetches the material a question needs, and no case is dropped to make room for another. Because one agent studies each community, the model can build a census of this curated population (Hannan & Freeman, 1977) while retaining the nuance needed to distinguish how organizational forms differ (McKelvey, 1975).

The community

Two fleet coders coded r/PlaceNL's 701 posts and comments, each a batch of whole threads, into 246 first-order codes tied to their quotes; its community coder read every thread's opening and grouped the codes into 21 second-order themes. r/PlaceNL's coding

The population

The aggregator composed the 705 coded communities, with 115,388 first-order codes and 7,844 second-order themes, into 197 third-order themes, 28 fourth-order themes and eight aggregate dimensions. The population model

The return

Our instrument lets the community coders challenge the population model with their case evidence. An initial iteration of the population model stated that communities keep their work by repairing changed pixels. The coder of r/PlaceNL (a community founded to coordinate the Dutch artworks) objected with its community's record: members agreed, "we are going to make blue canal houses and a pretty little windmill on our flag" (r/PlaceNL, post 636e4q, H68:19, our translation). Yet the community's own defense script kept erasing them: "who, oh who, will turn off the defense script? The little houses are all being wiped away" (r/PlaceNL, comment dfrvgvk, H71:40, our translation). The coder asked for a revision. The aggregator found similar evidence in 67 communities and, with it, revised the model: repair restores the form the keepers know, so a planned change is erased as damage until the keepers learn of it. r/PlaceNL's return

The forms

The aggregator grouped communities that asked for similar changes into six candidate organizational forms; r/PlaceNL is in form B. The six forms · r/PlaceNL's form

References

Aguinis, H., & Solarino, A. M. (2019). Transparency and replicability in qualitative research: The case of interviews with elite informants. Strategic Management Journal, 40(8), 1291–1315.

Bechky, B. A., & Davis, G. F. (2025). Resisting the algorithmic management of science: Craft and community after generative AI. Administrative Science Quarterly, 70(1), 1–22.

Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Prentice-Hall.

Daft, R. L., & Lewin, A. Y. (1990). Can organization studies begin to break out of the normal science straitjacket? An editorial essay. Organization Science, 1(1), 1–9.

Daft, R. L., & Lewin, A. Y. (1993). Where are the theories for the "new" organizational forms? An editorial essay. Organization Science, 4(4), i–vi.

Denrell, J. (2003). Vicarious learning, undersampling of failure, and the myths of management. Organization Science, 14(3), 227–243.

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.

Felin, T., & Holweg, M. (2024). Theory is all you need: AI, human cognition, and causal reasoning. Strategy Science, 9(4), 346–371.

Friedman, M. (1953). The methodology of positive economics. In Essays in positive economics (pp. 3–43). University of Chicago Press.

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31.

Gioia, D., Corley, K., Eisenhardt, K., Feldman, M., Langley, A., Lê, J., Golden-Biddle, K., Locke, K., Mees-Buss, J., Piekkari, R., Ravasi, D., Rerup, C., Schmid, T., Silverman, D., & Welch, C. (2022). A curated debate: On using "templates" in qualitative research. Journal of Management Inquiry, 31(3), 231–252.

Goldenstein, J., Jancsary, D., Grodal, S., Forgues, B., & Jennings, P. D. (2026). Studying culture and meaning through interpretative computational methods: From theory to method and back. Organization Studies, 47(1), 7–32.

Grodal, S., Anteby, M., & Holm, A. L. (2021). Achieving rigor in qualitative analysis: The role of active categorization in theory building. Academy of Management Review, 46(3), 591–612.

Günther, W. A., Joshi, M., & Constantinides, P. (2026). Trustworthiness in computational theory construction: Dimensionalization and category surfacing. MIS Quarterly, 50(3), 851–878.

Hall, R. L., & Hitch, C. J. (1939). Price theory and business behaviour. Oxford Economic Papers, 2, 12–45.

Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929–964.

Jowsey, T., Braun, V., Clarke, V., Lupton, D., & Fine, M. (2025). We reject the use of generative artificial intelligence for reflexive qualitative research. Qualitative Inquiry.

Lester, R. A. (1946). Shortcomings of marginal analysis for wage-employment problems. American Economic Review, 36(1), 63–82.

Lewin, A. Y., & Volberda, H. W. (1999). Prolegomena on coevolution: A framework for research on strategy and new organizational forms. Organization Science, 10(5), 519–534.

Machlup, F. (1946). Marginal analysis and empirical research. American Economic Review, 36(4), 519–554.

Machlup, F. (1967). Theories of the firm: Marginalist, behavioral, managerial. American Economic Review, 57(1), 1–33.

McKelvey, B. (1975). Guidelines for the empirical classification of organizations. Administrative Science Quarterly, 20(4), 509–525.

O'Sullivan, D., & Ravasi, D. (2026). Prompt-making and sensemaking: Using generative artificial intelligence to interpret empirical patterns in qualitative research. Strategic Organization. Advance online publication.

Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2024). MemGPT: Towards LLMs as operating systems (arXiv:2310.08560v2). arXiv.

Pratt, M. G., Kaplan, S., & Whittington, R. (2020). Editorial essay: The tumult over transparency: Decoupling transparency from replication in establishing trustworthy qualitative research. Administrative Science Quarterly, 65(1), 1–19.

Ragin, C. C. (1987). The comparative method: Moving beyond qualitative and quantitative strategies. University of California Press.

Shani, C., Soffer, L., Jurafsky, D., LeCun, Y., & Shwartz-Ziv, R. (2026). From tokens to thoughts: How LLMs and humans trade compression for meaning. In International Conference on Learning Representations.

Than, N., Fan, L., Law, T., Nelson, L. K., & McCall, L. (2025). Updating "the future of coding": Qualitative coding with generative large language models. Sociological Methods & Research, 54(3), 849–888.