Unbalanced, Unfair, Unhappy, or Unable? Theoretical Integration of Multiple Processes Underlying the Leader Mistreatment-Employee CWB Relationship with Meta-Analytic Methods
Bibliographic record
Abstract
Although a litany of theoretical accounts exists to explain why mistreated employees engage in counterproductive work behaviors (CWBs), little is known about whether these mechanisms are complementary or mutually exclusive, or the effect of context on their explanatory strength. To address these gaps, this meta-analytic investigation tests four theoretically-derived mechanisms simultaneously to explain the robust relationship between leader mistreatment and employee CWB: (1) a social exchange perspective, which argues that mistreated employees engage in negative reciprocal behaviors to counterbalance experienced mistreatment; (2) a justice perspective, whereby mistreated employees experience moral outrage and engage in retributive behaviors against the organization and its members; (3) a stressor-emotion perspective, which suggests that mistreated employees engage in CWBs to cope with their negative affect; and (4) a self-regulatory perspective, which proposes that mistreated employees are simply unable to inhibit undesirable behaviors. Moreover, we also examine whether the above model holds across cultures that vary on power distance. Our meta-analytic structural equation model demonstrated that all but the justice mechanism significantly mediated the relationship between leader mistreatment and employee CWBs, with negative affect emerging as the strongest explanatory mechanism in both high and low power distance cultures. Given these surprising results, as the stressor-emotion perspective is less frequently invoked in the literature, this paper highlights not only the importance of investigating multiple mechanisms together when examining the leader mistreatment-employee CWB relationship, but also the need to develop more nuanced theorizing about these mechanisms, particularly for negative affect.
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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 | 0.085 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.027 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".