Repairing damaged professional relationships with leader apologies: An examination of trust and forgiveness
Bibliographic record
Abstract
The purpose of this research is to investigate the effectiveness of different leader apology expressions in restoring workplace relationships after transgressions. We propose that “ideal” apology expressions, such as those that are sincere, can act as signals that the transgressing leader is trustworthy and that the relationship between the victim and offender is safe to restore through forgiveness. We support this contention with findings from four studies. In a hypothetical scenario involving a mid-level leader’s transgression (Study 1), we found that a sincere apology expression was the most effective at facilitating forgiveness compared to alternative expressions (basic, amends, remorse, responsibility, insincere). Additional field studies (Studies 2a and 2b) and an experiment that manipulated leader trust (Study 3) also supported the role of a sincere apology in facilitating forgiveness through the mechanism of increased leader trust. Our findings have implications for leadership theory and practice.
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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".