The impact of hiring directors' choice‐supportive bias and escalation of commitment on CEO compensation and dismissal following poor performance: A multimethod study
Bibliographic record
Abstract
Abstract Research Summary Boards of directors make high‐stake decisions that involve hiring, compensating, and dismissing CEOs. Building on theory about choice‐supportive bias and escalation of commitment, we theorize that “hiring directors” (directors who were present during a CEO's hiring) will display choice‐supportive bias and escalate commitment to poorly performing CEOs. Primary data from 73 directors indicate that directors are indeed biased toward CEOs they help hire. Archival data from S&P 1500 firms reveal that, following poor performance, the number of hiring directors is positively related to the increase in CEO pay and lower likelihood of CEO dismissal. Building on theory about board experience, we also predict and find that more experienced boards reduce the tendency to escalate. Thus, bias among hiring directors can be mitigated via experience. Managerial Summary Making a choice such as casting a vote or selecting a restaurant leads people to view their selection favorably even if evidence emerges suggesting it was a bad choice. We examine whether corporate directors fall prey to this choice‐supportive bias when involved in CEO hiring. We found that directors who are part of the hiring process tend to have an overly rosy view of the person selected. Moreover, if the firm is performing poorly, a board with more directors who helped hire the current CEO will tend to increase the CEO's pay more and are less likely to fire the CEO than a board with fewer such directors. This problem is reduced if the board has highly experienced directors among its ranks.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".