The Interpretation And Resolution Of Resource Allocation Issues In Professional Organizations: A Critical Examination Of The Professional‐Manager Dichotomy*
Bibliographic record
Abstract
Professional organizations have long been depicted as rife with conflict between professionals, who are assumed to represent the interests of their profession, and managers, who are assumed to represent the potentially competing interests of the organization. This study examines the validity of this assumption. Based on past research on both professional organizations and knowledge structure development, we predict that to the extent that professionals and managers conflict, they may do so because they interpret ‘identical’ issues differently. The results of a study of resource allocation decision preferences with 350 chief financial officers, chief medical officers, and physicians revealed strong support for our issue interpretation predictions, and virtually no support for the simple professional–manager dichotomy. Specifically, using structural equation modeling, we found that: (1) single resource allocation issues could be interpreted in multiple ways; (2) issue interpretations were strong predictors of decision preferences; (3) professionals and managers tended to interpret issues differently, although many of the differences were not consistent with past theorizing about professionals; (4) the interpretations and decision preferences of professionals who occupied management positions were like those of other professionals but different from those of managers; and (5) decision maker status (i.e., professional and/or manager) was only modestly related to decision preference. Our findings suggest that the sources and manifestations of a professional–manager dichotomy are more complex than previously reported.
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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 | 0.082 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".