Evaluating and monitoring CEO performance: evidence from US compensation committee reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it