Alternative Pathways of Change in Professional Services Firms: The Case of Management Consulting
Bibliographic record
Abstract
Abstract This paper contributes to the debate about new organizational forms in professional service firms (PSFs) by suggesting an alternative to extant accounts of how change takes place. To explain the displacement of community forms of organizing by more corporate forms, much of the literature has so far focused on intra‐archetype adaptation and evolutionary processes, looking mainly at establishedPSFsin law and accounting. Drawing on ideas from the sociology of professions and institutional theory, we suggest that, in more weakly regulated and open professional fields, change might also come from firms entering from the margins or the outside and bringing with them different models of organizing. We explore this possibility through a historical case study of the management consulting field in theUKover a 50 year period, based on a wide range of data sources. Our study shows that despite good intentions at the outset the main professional association was unable and – increasingly – unwilling to restrict entry. This resulted in growing fragmentation of the field through new entrants and, consequently, in greater diversity of organizational forms. Such findings draw attention not only to alternative pathways of change inPSFs, but also to the importance of distinguishing between professional organizational fields more generally.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".