Beyond adherence to justice rules: How and when manager gender contributes to diminished legitimacy in the aftermath of unfair situations
Bibliographic record
Abstract
Summary Unfair situations are a reality of organizational life. Although managers are typically advised to enact justice (i.e., adhere to justice rules) to mitigate negative employee reactions to unfair situations, the subjective nature of fairness suggests that employees may still react negatively to managers, regardless of managers' adherence to justice rules. Integrating fairness theory with social role theory, we propose that prescriptive gender stereotypes can differentially influence employees' reactions toward female (versus male) managers in the aftermath of unfair situations. Across two studies, female (versus male) managers were especially likely to experience diminished legitimacy in the aftermath of unfair situations, regardless of their adherence to justice rules. Moreover, these effects were especially likely to emerge for situations that reflected isolated versus ongoing issues. In turn, diminished legitimacy prompted negative employee behaviors that can detract from managerial effectiveness (e.g., withdrawal of manager‐directed citizenship behaviors, enhanced negative gossip about the manager, and increased resistance behaviors). Theoretical and practical contributions include recognizing the importance of broadening focus beyond adherence to justice rules to understand employees' reactions and managers' experiences, acknowledging the impact of gender in the context of fairness, and highlighting that upward‐directed gender bias may contribute to the (un)intentional undermining of female managers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".