Breaking the flow: how do workplace hazing and co-worker bullying disrupt employee knowledge sharing?
Bibliographic record
Abstract
Purpose Employees’ failure to share knowledge ruins organizational performance and innovation worldwide. Drawing on the job demands-resources (JD-R) model, the present study aims to explore how workplace hazing (WH) and co-worker bullying affect knowledge sharing (KS) via workplace alienation and fear-based silence (FBS) – an unexplored serial mechanism. In addition, it examines friendship prevalence (FPP) as a moderator in the association between FBS and KS. Design/methodology/approach A time-lagged study on 319 IT industry employees from Northern India, using partial least squares structural equation modeling to test hypothesized relationships. Findings The findings reveal that WH and co-worker bullying lead to workplace alienation among employees. Furthermore, results confirm workplace alienation and FBS as serial mediators. However, FPP does not moderate the association between FBS and KS. Practical implications The authors' findings suggest that expecting employees to engage in positive voluntary behaviors, such as KS, without tackling the challenges that deplete the work environment’s social capital may be quixotic. Thus, managers must give close and thoughtful attention to preventing and remedying WH and co-worker bullying to encourage employees’ voluntary behaviors, such as KS. Originality/value Past research has underscored the importance of an encouraging work environment in the knowledge creation and exchange process; hence, by administering the theoretical framework of the JD-R model, this study meaningfully contributes to the extant literature on hostile workplace conditions, namely, WH and co-worker bullying in influencing employees’ KS. Further, the results elucidate the dynamics of the sequential role of work alienation and FBS, offering constructive awareness to practitioners’ organizations.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".