Bibliographic record
Abstract
ABSTRACT This article empirically examines the role of a firm's second‐in‐command in monitoring the chief executive officer (CEO) from the bottom up to mitigate agency problems. Although CEOs have long been the focus, little research has addressed No. 2 executive. This study provides a comprehensive understanding of these top executives and their roles in bottom‐up monitoring. The results suggest that (1) bottom‐up monitoring by No. 2 executives enhances firm value, (2) the effect is stronger in firms with weak corporate governance or poor CEO incentive alignment, (3) the effect is weaker when No. 2 is promoted internally or lacks experience, and (4) such monitoring becomes more critical in the post‐Sarbanes–Oxley (SOX) regulatory environment. Further analysis reveals that bottom‐up monitoring creates firm value by fostering a more transparent information environment, increasing CEO turnover‐performance sensitivity, and reducing the CEO's ability to pursue the “quiet life,” but has no effect on “empire building.”
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".