How Organizational Transgressions Can Prompt Prosocial and Deviant Behavior in Employees via Guilt
Bibliographic record
Abstract
Despite efforts to reduce ethical misconduct by organizations, many organizations occasionally act unethically. Understanding the outcomes of such organizational transgressions is important, as this can facilitate organizational recovery and prevent future transgressions. However, whereas scholars have identified numerous detrimental outcomes of organizational transgressions for organizations, we have a poor understanding of how transgressions at the organizational level can impact employees. Drawing on attribution theories of emotion, we argue that employees can experience vicarious guilt in response to an organizational transgression. Guilt, in turn, may prompt employees to engage in prosocial behaviors targeted at external parties as well as deviant behaviors targeted at the organization. Our hypotheses were supported across three studies: a critical incident recall experiment with a diverse sample of full-time employees, an interactive scenario-based experiment with a student sample, and a two-wave survey with members of a single organization. Theoretically, our studies provide insight into individual-level responses to organizational transgressions, advance our understanding of vicarious guilt in organizational settings, and identify antecedents of prosocial and deviant employee behaviors. Practically, we provide insights that are important for the development of interventions aimed at helping organizations and employees recover from transgressions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".