Pluralistic Organizations in Management: One Phenomenon and Multiple Theoretical Developments
Bibliographic record
Abstract
Formally organized collaborations in pluralistic settings are currently rising and wide-spread phenomena. Over the past three decades, authors from different research traditions in management have proposed various concepts of organizations in pluralistic settings. However, these concepts remain largely unrelated, and they are currently stuck in a “fragmentation trap.” Research on pluralistic organizations will not become cumulative unless it receives a “walking stick” that allows a scientific conversation to occur. As a “walking stick,” this literature review proposes the umbrella concept of pluralistic organization. The pluralistic organization is broadly defined as a structure enabling actors with diffuse power and divergent perspectives to cooperate on substantive issues. We review 101 articles published in 12 leading management and organization journals, and bring to light 21 concepts of pluralistic organizations from four schools of thought: inter-organizational collaboration, institutional change, deterministic approaches, and the social study of science. Bridging theoretical developments between and within these research traditions, this paper organizes the scientific conversation on this rising phenomenon. It offers a comprehensive synthesis on pluralistic organizations, future avenues of research, and recommendations on how to pull this topic out of the fragmentation trap.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.024 | 0.041 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".