Construction Worker Risk-Taking Behavior Model with Individual and Organizational Factors
Bibliographic record
Abstract
Behavioral-based safety is an important application of behavioral science that can be used to address safety problems in the construction sector. An understanding of construction worker risk-taking behavior is deemed to be a crucial basis on which concerned authorities and construction companies can develop effective safety interventions to reduce construction accidents. However, no studies have been conducted to examine the effects of safety climate, work condition, attitude toward risk, cognitive bias, and risk perception on construction worker risk-taking behavior through a quantitative approach. Accordingly, this study aims to propose a research model that explains construction worker risk-taking behavior. A total of 188 valid datasets were obtained through a series of questionnaire surveys conducted in representative construction projects in Hong Kong. Confirmatory factor analysis with structural equation modeling was adopted to validate the hypothesized research model. Results show that attitudes toward risk and cognitive bias have a positive influence, whereas risk perception and work conditions have a negative influence on construction worker risk-taking behavior. In addition, safety climate was negatively correlated with construction worker risk-taking behavior. Practical recommendations for reducing construction worker risk-taking behavior are also discussed in this paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".