The double‐edged sword of going “overboard”: Board connectedness, debt quality, and the cost of debt
Bibliographic record
Abstract
Abstract Board connectedness, a result of directors serving the boards of more than one firm, impacts companies positively (access to resources provided by connected directors) and negatively (directors being “spread too thin”). Given that debt quality has a high influence on the cost of debt, this paper examines how the cost of debt (bond yield spread) is influenced by a firm's board connectedness while considering the effect that debt quality (bond ratings) may have on such a relationship. Results show that the relationship between connectedness and the cost of debt is highly dependent on debt quality, where connectedness is associated with a lower cost of debt if debt quality is high; however, connectedness is also associated with a higher cost of debt if debt quality is low. We also note that as the debt quality for connected firms decreases as their cost of debt increases. We are contributing to administrative sciences by studying one of its key elements: governance, more specifically the connectivity of boards of directors. This contributes to the current debate on the effect of board connectivity: prior research has not provided conclusive results, and we show that the effect is different based on the characteristics of the firm's debt.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".