Safety Climate on Safety: The Mediating Role of Management Commitment
Bibliographic record
Abstract
Past research on safety climate has tended to focus on measurement and operationalization issues or predictive validity (Zohar, 2010). The present research furthers this work by investigating the interplay between different facets of safety climate on safety effectiveness. In a sample of oil and gas workers (N = 263) we show that the relationship between several distal safety climate variables (training effectiveness, procedure effectiveness, and work pressure) on safety effectiveness is partly mediated by management commitment (study 1). In study 2, we replicate these findings in a sample of railway construction workers (N = 167). We further extend these findings to show a boundary condition for this indirect path; perceptions of co-worker disengagement. When perceptions of co-worker disengagement are high the negative effect of low management commitment on safety effectiveness is diminished. Study 3 further explores the key role of management commitment to safety by conducting qualitative focus group interviews in oil and gas organizations (N = 49 participants) to delve into safety culture. Analyses suggest that employee sensemaking regarding safety are perceived through a lens of low management commitment to safety operationalized as management blame. Taken together, these findings highlight the role of tacit management-oriented climate perceptions as key to explaining the role of other distal safety climate factors for predicting safety effectiveness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".