Safety Leadership and Performance in Indonesia’s Construction Sector: The Role of Project Owners’ Marurity
Bibliographic record
Abstract
The construction sector in Indonesia witnesses a significant number of work accidents, with construction sites being particularly prone to such incidents. It is imperative for stakeholders, especially project owners, to prioritize safety performance. The authorization of safety plans empowers project owners, granting them substantial influence over safety outcomes. This research employs Structural Equation Modeling (SEM) to investigate the relationship between project owners' safety leadership and safety performance, with valuable input obtained from contractors who directly interact with project owners. The identified variables encompass leader's maturity attributes, psychosocial factors, participatory approaches, communication practices, and competence levels. All interrelationships between the variables demonstrate high significance in shaping safety performance (with z-scores exceeding 1.96). Two distinct patterns are identified to characterize project owners' leadership styles. The first pattern relates to the personal maturity of the owner, while the second pattern focuses on the owner's ability to foster effective stakeholder relationships. To manifest maturity, project owners must make three key contributions: 1) ensuring safety costs are factored into the project value, 2) procuring contractors with well-defined safety policies, and 3) ensuring swift responses to accidents. These findings underscore the importance of project owners in enhancing construction safety practices, emphasizing their role beyond that of contractors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".