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Construction Worker Risk-Taking Behavior Model with Individual and Organizational Factors
Banus Kam Leung Low1, Siu Shing Man2, Alan Hoi Shou Chan3
1Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China. kllow2-c@my.cityu.edu.hk.
Summary
Understanding construction worker risk-taking behavior is key to reducing accidents. This study found attitudes toward risk and cognitive bias increase risk-taking, while risk perception and work conditions decrease it.
Area of Science:
- Behavioral science
- Occupational safety
- Construction management
Background:
- Behavioral-based safety is crucial for addressing construction sector issues.
- Understanding construction worker risk-taking behavior is vital for effective safety interventions.
- Limited quantitative research exists on factors influencing construction worker risk-taking behavior.
Purpose of the Study:
- To propose and validate a research model explaining construction worker risk-taking behavior.
- To examine the quantitative effects of safety climate, work condition, attitude toward risk, cognitive bias, and risk perception on risk-taking behavior.
- To provide practical recommendations for enhancing safety in construction.
Main Methods:
- Quantitative approach utilizing questionnaire surveys from 188 construction workers in Hong Kong.
- Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) for model validation.
Main Results:
- Attitudes toward risk and cognitive bias positively influence construction worker risk-taking behavior.
- Risk perception and work conditions negatively influence construction worker risk-taking behavior.
- Safety climate shows a negative correlation with construction worker risk-taking behavior.
Conclusions:
- The validated model provides insights into the drivers of construction worker risk-taking behavior.
- Findings highlight the importance of addressing individual attitudes, cognitive factors, risk perception, and work conditions.
- Interventions should consider these factors to effectively reduce construction accidents.
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