Evaluating and monitoring CEO performance: evidence from US compensation committee reports
Bibliographic record
Abstract
Purpose Concerns for improving governance have focused attention on the role of boards of directors in evaluating the performance of the CEOs. There have been numerous discussions about how performance and strategic management systems aid in the evaluation and implementation of strategy and improve corporate performance. However, the value of those systems to boards of directors has not been extensively discussed. The purpose of this article is to describe the use of non‐financial metrics for CEO performance evaluations and offer specific guidance as to how boards of directors can design a performance measurement system that provides a sound basis for evaluating CEO performance. Design/methodology/approach The sample for this study was drawn from Fortune magazine's America's Most Admired Companies industry list. Compensation committee reports found in 59 proxy statements were examined. Findings Although there are a growing number of companies using non‐financial metrics, results confirm that CEOs are primarily evaluated on financial criteria, indicating a narrow definition of corporate performance. Few attempts are made to ascertain and disclose the appropriateness of the performance measures and to demonstrate how these measures are consistent with the company's vision, mission, and strategies for long‐term performance success. Originality/value While some surveys have investigated the growing trend of using non‐financial criteria, in this survey, these criteria are examined in the context of a multidimensional performance evaluation system. Also, a framework for improving the measurement and performance of CEOs is presented. This is an important part of an overall program that should be in place to improve overall corporate governance.
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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 | 0.031 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".