Why we harm the organization for a perpetrator's actions: The roles of unforgiveness, group betrayal, and group embodiment in displaced revenge
Bibliographic record
Abstract
Abstract The present article aims to elucidate whether offender unforgiveness predicts organizationally targeted displaced revenge and whether this effect occurs because offender‐directed feelings spill over to shape feelings towards the group. Two studies (Study 1a/1b) showed that unforgiveness predicts organizationally directed displaced revenge in the form of counterproductive workplace behaviours against an organization, mediated by perceived group betrayal. Study 2 investigated whether the relationships between unforgiveness, perceived group betrayal, and displaced revenge are moderated by group embodiment: the extent to which the offender is closely connected to, identified with, and in alignment with the group. With an experimental design that manipulated group embodiment and transgressor status, we found that unforgiveness and perceived group betrayal predict higher levels of displaced revenge under conditions of high rather than low group embodiment. Study 2 further showed that displaced revenge intentions operate in addition to, not in place of, revenge intentions towards the offender. Implications are discussed.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".