The effect of abusive supervision on safety behaviour of Chinese underground miners: a multi-level moderated mediation analysis
Bibliographic record
Abstract
Purpose Prior research has suggested that abusive supervision has negative impacts on various work outcomes. However, little attention has been paid to the relationship between abusive supervision and employees’ safety behaviour. The purpose of this study is, therefore, to address these limitations by developing and testing a theoretically based conceptual model that explicitly considers the underlying mechanism and boundary condition of the relationship between abusive supervision and safety behaviour of underground coal miners in China. Design/methodology/approach At Time 1, the authors conducted a survey of 630 employees to assess their supervisors’ abusive leadership behaviours, their own power distance beliefs and their self-reflection. At Time 2, the authros sent questionnaires to the leaders and invited them to evaluate employees’ safety behaviour in the workplace. After cleaning the survey data, the authors tested our model using a multi-level analysis on a sample (n = 458) of underground miners across 96 coal mining sites in China. Findings The authors propose that abusive supervision decreases employees’ safety compliance/participation by reducing reflection but strengthening rumination. The authors further find that the linkage from abusive supervision to reflection/rumination to safety compliance/participation is affected by power distance. Originality/value To the best of the authors’ knowledge, This is one of the first empirical studies to investigate the mediating effects of a deep cognitive processing variable – namely, self-reflection – and the moderating effects of power distance on the relationship between abusive supervision and safety behaviour.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".