Seizing Opportunity in Emerging Fields: How Institutional Entrepreneurs Legitimated the Professional Form of Management Consulting
Bibliographic record
Abstract
We draw on the early history of the management consulting field to build theory about how institutional entrepreneurs legitimate new kinds of organizations in emerging fields. We study the professional form of management consulting organization, which came to dominate other alternatives. Pioneers of this organizational form seized opportunities arising from broad institutional change to discredit the status quo and legitimate their model of how to advise organizations on strategic and operational issues. Similar to institutional entrepreneurs seeking to change mature fields, those in this emerging field engaged in theorization, undertook collective action, and established affiliations with recognized authorities and elites. But unlike institutional entrepreneurs in mature fields, the actors we studied could not leverage logics, positions, or collectivities within their emerging field; instead, they drew on logics from outside their field, sought affiliations with external authorities and elites, and emphasized the benefits of their activities for society at large. Our analysis thus suggests important differences in how actors legitimate novel organizational forms in emerging versus mature fields and underscores the need for theories of institutional entrepreneurship that explicitly account for field context.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".