Perceived organizational injustice and counterproductive work behaviours: mediated by organizational identification, moderated by discretionary human resource practices
Bibliographic record
Abstract
Purpose This research unpacks the relationship between employees' perceptions of organizational injustice and their counterproductive work behaviour, by detailing a mediating role of organizational identification and a moderating role of discretionary human resource (HR) practices. Design/methodology/approach The hypotheses were tested with a sample of employees in Pakistan, collected over three, time-lagged waves. Findings An important reason that beliefs about unfair organizational treatment lead to enhanced counterproductive work behaviour is that employees identify less strongly with their employing organization. This mediating role of organizational identification is less salient, however, to the extent that employees can draw from high-quality, discretionary HR practices that promote their professional development and growth. Practical implications For management practitioners, this study pinpoints a key mechanism – the extent to which employees personally identify with their employer – by which beliefs about organizational favouritism can escalate into purposeful efforts to inflict harm on the organization and its members. It also reveals how this risk can be subdued by discretionary practices that actively support employees' careers. Originality/value This study adds to previous research by detailing why and when employees' frustrations about favouritism-based organizational decision making may backfire and elicit deviant responses that likely compromise their own organizational standing.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".