CEO compensation as a process and a product of negotiation
Bibliographic record
Abstract
Purpose The purpose of this paper is to extend our understanding of CEO compensation by looking into the CEO pay‐setting process. Particularly, a process model is proposed to specify the interaction between situational indicators, process variables, contextual factors and CEO pay. Design/methodology/approach A modest review the major theories that are driving the field of CEO compensation study reveals several interesting findings. These models or perspectives provide valuable but incomplete understanding of the multifaceted phenomenon. Especially, the realm of CEO pay‐setting process is still unexplored. A process model of CEO compensation is developed to fill in this gap. Findings CEO compensation is a negotiation between a CEO and a principal. Negotiated CEO pay is better predicted by CEO aspirations and principal reservations, rather than economic indicators. CEO power and the institutional environment have a moderating effect. Practical implications The study suggests that a better theory is critically in demand in order to improve effectiveness of corporate governance. This paper underscores that a real challenge for a principal in influencing CEO pay is to anticipate CEO aspirations and to monitor the gaps between CEO aspirations and principal reservations, rather than to control economic indicators. Unfortunately, until now there has been very limited information about principal reservation and CEO aspiration. Originality/value This inquiry seeks to make a difference by moving CEO compensation research into a fruitful direction. To our knowledge, this inquiry is the first attempt that provides systematic explanation as to how and why situational indicators do not directly influence the negotiated CEO pay. The newly proposed model is much realistic, much integrative and much dynamic, compared with existing conceptualizations. Eight propositions are presented to guide empirical research as well as future theory development.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".