Dominant Choices? How CEO/Board Power Predicts Compensation Consulting Firm Relationships
Bibliographic record
Abstract
While research examining the relevance of differences in CEO/Board relative power across firms has typically focused on intra-organizational antecedents and consequences, this study analyzes the potential inter-organizational implications of such power differences. Specifically, we consider how existing differences in CEO/Board power within prospective client firms makes third-party service providers differentially attractive to these client firms, and we examine how such differences in client firms will affect the use and choice of compensation consulting (CC) firms. More specifically, we suggest that client firms with more powerful CEOs will tend to hire CC firms that have exhibited a consistent history of CEO favoritism (i.e., excess CEO compensation). We also address the possible consequences of such hiring choices in terms of subsequent CC behaviors, and we examine the implications of an initial and ongoing client/CC firm misalignment. We test our hypotheses using an original dataset consisting of over 1500 firms across 5 years, and discuss the implications of our theoretical perspective and supportive empirical findings for future research on corporate governance, CEO/Board power, and third-party service providers.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".