Blame it on the supervisor or the subordinate? Reciprocal relations between abusive supervision and organizational deviance.
Bibliographic record
Abstract
Drawing on various theoretical perspectives, extant research has primarily treated subordinate organizational deviance as a consequence of abusive supervision. Yet, social interaction theories of aggression and victimization perspectives provide support for the opposite ordering, suggesting that subordinate organizational deviance may be an antecedent of abusive supervision. By using a cross-lagged panel design, we empirically test the potentially reciprocal relation between abusive supervision and subordinate organizational deviance. In Study 1, we measured both abusive supervision and organizational deviance at 2 separate times with a 20-month lag between measurement occasions and found evidence that subordinate organizational deviance leads to abusive supervision, but not vice versa. In Study 2, with a shorter time lag (i.e., 6 months), the reciprocal effects of abusive supervision and organizational deviance were supported. Furthermore, we found that the effects of abusive supervision on organizational deviance were moderated by subordinate self-control capacity and intention to quit such that the effects were only significant when subordinates had low self-control capacity and high intention to quit. Theoretical and practical implications are discussed.
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 | 0.026 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".