Why did I say sorry? Apology motives and transgressor perceptions of reconciliation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Despite the importance of apology in reconciling interpersonal transgressions, little research has focused on the people engaging in the behavior. Why do transgressors apologize in the workplace, and do apology motives shape transgressor perceptions of reconciliation? We conducted three field studies using qualitative and quantitative methodologies to examine these questions. In Studies 1 and 2 (total N = 781), we identified four distinct apology motives—self‐blame, relational value, personal expedience, and fear of sanctions—and developed self‐report scales to measure the motives. In Study 3 ( N = 420), we examined relations between apology motives and transgressor perceptions of victim forgiveness and relationship reconciliation through the lens of motivated cognition. We found that apologizing due to self‐blame, relational value, and personal expedience increases perceptions of victim forgiveness, whereas apologizing due to fear of sanctions decreases perceived forgiveness. Moreover, mediation analyses revealed that motives indirectly influence transgressor perceptions of relationship reconciliation through perceived forgiveness. Taken together, our research presents a novel multidimensional perspective on apology‐giving in the workplace, suggesting that why transgressors apologize can affect their perceptions of reconciliation. Overall, our research highlights the need to incorporate transgressor cognitive and motivational processes into reconciliation research.
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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 | 0.000 | 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) | 0.009 | 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 it