What boards need to know about CEO compensation: the strategic role of CEO origin and company size
Bibliographic record
Abstract
Purpose This article helps boards and executive teams understand the conditions under which incentive pay is more or less likely to align with performance—particularly when factoring in CEO origin and firm size. It highlights two often-overlooked strategic variables: the CEO’s origin (internal promotion vs. external hire) and firm size. Design/methodology/approach We analyze a comprehensive dataset of publicly traded firms in the USA and Canada. By examining how CEO background and company scale moderate the pay–performance relationship, the study integrates agency theory and the Resource-Based View (RBV) to reveal important contextual effects. Findings Internally promoted CEOs consistently deliver stronger returns for each dollar of compensation, especially in small and mid-sized firms where their firm-specific knowledge and networks create immediate value. External hires can bring fresh perspectives but face integration challenges that weaken the compensation–performance link in the short term. Practical implications Boards should avoid one-size-fits-all pay packages. Instead, they should tailor compensation strategies to CEO origin and firm size, aligning incentives with the leader’s ability to deliver results. This approach improves succession planning, onboarding processes, and overall governance effectiveness. Originality/value This article provides actionable guidance for directors, compensation committees, and executive recruiters. By integrating succession planning and compensation design, it offers a clear framework for aligning CEO pay with firm performance in diverse strategic contexts.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".