The impact of subordinate disrespect on leader justice
Bibliographic record
Abstract
Purpose Employees are affected by the interpersonal treatment received from leaders (i.e. interactional justice), especially when being informed of negative outcomes (Brockner, 2010). Although respectful treatment may be expected from leaders generally, little is known about when leaders are more likely to display interactional justice and whether less interactional justice might be acceptable under certain circumstances. Drawing on reciprocity theory (e.g. Gouldner, 1960), and leader–member exchange (LMX) theory (e.g. Gerstner and Day, 1997), the purpose of this paper is to test the hypothesis that employees who are disrespectful and inconsiderate toward their supervisors (i.e. who are themselves interactionally unjust) would and should receive less interactional justice when being informed of a negative outcome. Design/methodology/approach The authors conducted three experimental studies ( Ns =87, 47 and 114), in the context of leaders communicating a layoff decision to their subordinates. Findings The results supported the predictions albeit the effect of subordinate interactional justice on supervisor justice was modest, yet consistent, across studies. Research limitations/implications The findings are consistent with reciprocity theory and the LMX literature and suggest that leader actions when communicating bad news are dependent on employee conduct. Limitations of the studies include a primary reliance on students as participants and the measurement of behavioral intentions rather than behavior. Originality/value The studies are among the first to examine interactional injustice perpetrated by subordinates toward their leaders, and its impact on leader behavior when delivering negative outcomes. There is a paucity of literature understanding the causes of leader fairness behavior, in addition to a consideration of unfairness from perpetrators of lower positional power.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".