Organizational injustice and knowledge hiding: the roles of organizational dis-identification and benevolence
Bibliographic record
Abstract
Purpose With a basis in social identity and equity theories, this study investigates the relationship between employees' perceptions of organizational injustice and their knowledge hiding, along with the mediating role of organizational dis-identification and the potential moderating role of benevolence. Design/methodology/approach The hypotheses were tested with three-wave survey data collected from employees in Pakistani organizations. Findings The experience of organizational injustice enhances knowledge hiding because employees psychologically disconnect from their organization. This mediation by organizational dis-identification is buffered by benevolence or tolerance for inequity, which reduces employees' likelihood of reacting negatively to the unfavourable experience of injustice. Practical implications For practitioners, this study identifies organizational dis-identification as a key mechanism through which employees' perceptions of organizational injustice spur their propensity to conceal knowledge, and it reveals how this process might be mitigated by a sense of obligation to contribute or “give” to organizational well-being. Originality/value This study establishes a more complete understanding of the connection between employees' perceptions of organizational injustice and their knowledge hiding, with particular attention devoted to hitherto unspecified factors that explain or influence this process.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".