CEO Current and Prospective Wealth Option Compensation and Corporate Social Responsibility: The Behavioral Agency Model
Bibliographic record
Abstract
This study examines the relationship between CEO options compensation and corporate social responsibility (CSR) based on the behavioral agency model (BAM). The BAM assumes that the CEO is bounded by loss-aversion behavior. Using constructs from the BAM, i.e., CEO current and prospective wealth from their options compensation, this study examines the differing effects of CEO current wealth and prospective wealth on firms’ CSR strengths, CSR concerns, institutional CSR and technical CSR. Based on a sample of 1565 U.S. firms during 1996 to 2018, the study finds that CEO current wealth is negatively related to firms’ CSR strengths and CSR concerns. The study also finds that CEO prospective wealth is positively related to firms’ CSR strengths but is unrelated to CSR concerns. CEO current wealth is negatively related to institutional CSR, whereas CEO prospective wealth is positively related to institutional and technical CSR. CEO current (prospective) wealth is more strongly and negatively (positively) related to institutional CSR than technical CSR. This study indicates that designing CEO option compensation to align top managers’ interests with the stakeholder interests requires a greater understanding of how CEO bounded rationality behavior toward loss aversion and risk taking is influenced by their option compensation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".