Journal
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
Volume 141, Issue 6, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CO.1943-7862.0000972
Keywords
Cognitive analysis; Safety behavior; Theory of planned behavior; Construction safety; Neural network; Decision tree; Data mining; Migrant worker; Working at height; Labor and personnel issues
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Despite efforts in recent years, the construction industry remains one of the top contributors for workplace fatalities in many countries. One of the key concerns in the industry is the management of workers' safety behavior. This paper aims to explore the cognitive factors influencing the unsafe behavior of not anchoring a safety harness when working at height. In addition, multiple stepwise linear regression, artificial neural network, and decision tree techniques were applied in the study to assess their usefulness in evaluating survey data of safety cognitive factors. The theory of planned behavior (TPB) was adopted to model the cognitive factors influencing the unsafe behavior of scaffolders. The TPB postulates that attitude, perceived behavioral control, and subjective norms affect the intention of workers, which ultimately affects intentional behavior. The unsafe act of not anchoring harnesses while working on a scaffold was selected as the focal behavior based on observations and interviews with safety supervisors. Supervisors also provided their opinions on the underlying reasons for the unsafe act. A questionnaire was then developed based on the site observations, interviews, and literature review. Subsequently, 40 migrant workers from Bangladesh, India, and China were surveyed. Stepwise multiple linear regression, neural network, and decision tree analyses were implemented. The analyses revealed that subjective norm was the key variable influencing a worker's decision to anchor the safety harness. The significance of subjective norm was probably affected by the national culture of the migrant workers. In addition, the analyses showed that the relationships between the variables were probably nonlinear, thus neural network and decision tree are suitable techniques. The exploratory study provides the basis for design of an in-depth study on the cognitive factors influencing safety behavior and it expands the choice of analyses techniques. (C) 2015 American Society of Civil Engineers.
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