Influence of Safety Leadership Styles on Safety Behaviour: The Mediating Role of Safety Climate, Knowledge, and Motivation in Indonesia's Oil and Gas Construction Project
Bibliographic record
Abstract
Workplace injuries in the mining, oil, gas, and construction sectors in developing countries continue to increase.These work accidents occur because worker safety behaviors are low.Oil and gas construction projects in Indonesia are increasing because the capacity of oil and gas refineries is raised to meet the national demand for fuel oil.As such, occupational accidents at one of the construction projects in Indonesia have also increased.This study examines how worker safety behaviors are impacted by safety programs and transformational and transactional leadership styles.In addition, several mediation variables of the impact of leadership styles on safety behavior variables were also analyzed, including safety climate, knowledge, and motivation.The hypothesis was proposed using twenty-two direct and indirect tests with 675 workers as respondents.This study uses the structural equation modeling method for the tests.The results show that several hypothesis tests were accepted and there was positive relationship between variables, such as the relationship between safety climate and safety knowledge, as well as safety knowledge and safety behavior.These findings provide insight for HSE managers to provide examples of safety climate in the oil and gas construction projects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".