Moral Foundations, Himpathy, and Punishment Following Organizational Sexual Misconduct Allegations
Bibliographic record
Abstract
We build on deontic justice and moral foundations theories to shed light on responses to sexual misconduct at work by proposing a model that explains why some third parties punish accusing victims and support alleged perpetrators. We theorize that when third parties are given conflicting he-said, she-said information, they intuitively evaluate organizational injustice based on moral values. We further theorize that binding moral foundations (loyalty, authority, purity) give rise to sympathy toward men accused of sexual misconduct and anger toward female accusers. Across five studies (total n = 5,413) utilizing archival, field, and vignette designs, we examined third-party responses to sexual misconduct accusations ranging in severity across several industries. Third-party endorsement of binding moral foundations was linked to increased perpetrator-directed sympathy and victim-directed anger (Studies 1–4). These emotions jointly mediated the relationship between binding values and credibility perceptions of the accusing victim and the alleged perpetrator (Studies 2 and 3). Moreover, victim credibility was negatively associated with social sanctions and punishment severity levied toward the accusing victim, and perpetrator credibility was negatively associated with the same punishment outcomes for the alleged perpetrator (Studies 3 and 4). In Study 5, we found that managers framing the accusing victim as disloyal exacerbated negative judgments and emotions toward the victim and positive judgments and emotions toward the perpetrator for individuals who highly ascribe to binding moral foundations. We discuss the theoretical contributions and practical implications of moral concerns on third parties’ emotions, judgments, and motivations to punish actors involved in sexual misconduct allegations. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1652 .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".