Expertise Put to the Test: How Clients Continually Assess the Worth of Management Consultants
Bibliographic record
Abstract
Clients hire management consultants as experts to tackle complex problems and legitimize decisions. However, the expertise of these consultants often sparks debate. While consultants may seek to prove the distinctiveness and relevance of their expertise, clients must continually evaluate the value of consultants’ expertise in treating their problems. This study examines the dynamic and contested nature of expertise in consultant–client relationships by exploring the practical tests through which clients assess and recognize consultants’ expertise as distinct and valuable in addressing their concerns. Based on an ethnographic study of a four-month consultancy project involving a team of consultants at a large energy company, we identify three distinct forms of tests – skill tests, results tests, and loyalty tests. These tests are used by clients to evaluate consultants’ expertise and in turn influence how consultants approach clients’ problems and conduct their work. Our study advances a relational perspective on expertise, emphasizing the client’s role and the consultants’ ability to handle distinct tests. We conclude by discussing the implications for studies of expertise and management consulting.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".