Bibliographic record
Abstract
Purpose – This paper aims to provide greater understanding of how the composition of pay reduces agency cost to the shareholders by examining how firms pay their chief executive officers (CEOs). More specifically, this study examines the relationship between CEOs’ social capital, measured as external directorships, and their contingency pay, the proportion of their compensation that depends on achieving long-term performance goals. Design/methodology/approach – The authors use a panel sample of Standard & Poor 500 CEOs to test two contrasting theoretical perspectives. From a board perspective, boards attempt to retain executives with more social capital working longer for the firms to utilize executives’ social capital and pay them more in the form of contingency pay. The CEO power perspective argues that CEOs wield social capital as a form of power to lower contingency pay in an attempt at preserving wealth. Findings – CEO social capital does not exacerbate agency pressures. Boards reward the long-term benefits of social capital accumulated by CEOs through higher proportions of contingency pay. Research limitations/implications – The authors considered CEOs of well-capitalized, publicly-traded US-based firms. So the results may not generalizable to other contexts. Practical implications – Boards do recognize and reward CEOs for their social capital, and use higher levels of contingency pay to lock in CEOs with social capital. Originality/value – This is the first study to explicitly examine the impact of CEO social capital on both non-equity and equity compensation.
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.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".