Large Language Models and the Future of Organization Theory
Bibliographic record
Abstract
In this editorial essay, we explore the potential of large language models (LLMs) for conceptual work and for developing theory papers within the field of organization and management studies. We offer a technically informed, but at the same time accessible, analysis of the generative AI technology behind tools such as Bing Chat, ChatGPT, Claude and Gemini, to name the most prominent LLMs currently in use. Our aim in this essay is to go beyond prior work and to provide a more nuanced reflection on the possible application of such technology for the different activities and reasoning processes that constitute theorizing within our domain of scholarly inquiry. Specifically, we highlight ways in which LLMs might augment our theorizing, but we also point out the fundamental constraints in how contemporary LLMs ‘reason’, setting considerable limits to what such tools might produce as ‘conceptual’ or ‘theoretical’ outputs. Given worrisome trade-offs in their use, we urge authors to be careful and reflexive when they use LLMs to assist (parts of) their theorizing, and to transparently disclose this use in their manuscripts. We conclude the essay with a statement of Organization Theory’s editorial policy on the use of LLMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".