The Risk-Taking Propensity of Construction Workers-An Application of Quasi-Expert Interview

Research on psychophysiological characteristics of construction workers during consciously unsafe behaviors

Workers' unsafe behavior is a primary cause leading to falling accidents on construction sites. This study aimed to explore how to utilize psychophysiological characteristics to predict consciously unsafe behaviors of construction workers. In this paper, a psychological questionnaire was compiled to measure risky psychology, and wireless wearable physiological recorders were employed to real-timely measure the physiological signals of subjects. The psychological and physiological characteristics were identified by correlation analysis and significance test, which were then utilized to develop unsafe behavior prediction models based on multiple linear regression and decision tree regressor. It was revealed that unsafe behavior performance was negatively correlated with task-related risk perception, while positively correlated with hazardous attitude. Subjects experienced remarkable increases in skin conductivity, while notable decreases in the inter-beat interval and skin temperature during consciously unsafe behavior. Both models developed for predicting unsafe behavior were reliably and well-fitted with coefficients of determination higher than 0.8. Whereas, each model exhibited its unique advantages in terms of prediction accuracy and interpretability. Not only could study results contribute to the body of knowledge on intrinsic mechanisms of unsafe behavior, but also provide a theoretical basis for the automatic identification of workers' unsafe behavior.

How Optimism Bias and Safety Climate Influence the Risk-Taking Behavior of Construction Workers

Risk taking among construction workers is a critical topic in construction safety research. The aim of this study was to empirically investigate how optimism bias and safety climate influence construction worker risk-taking behavior. A survey with a designed questionnaire was conducted to collect data from construction workers. A total of 183 construction workers participated in this study and completed the designed questionnaire. The collected data were subjected to statistical analysis by using structural equation modeling. Results show that optimism bias related to work risks positively influences construction worker risk-taking behavior, whereas safety climate and optimism bias related to hazard perception skills negatively affect the risk-taking behavior. These findings can enrich the literature on construction worker risk-taking behavior from the perspective of optimism bias and safety climate. Practical implications are provided for discouraging construction workers from taking risks at work.

Research on factors affecting the risk-taking behavior of personnel working in the forest products sector

Objectives. The fact that occupational accidents are a permanent problem in the forest products sector encouraged this research to be conducted on the factors affecting the risk-taking behavior (RTB) of employees in the sector. Understanding the RTB of employees in the sector would help managers to reduce occupational accidents and to develop effective safety interventions. Therefore, this study aimed to determine the effects of individual, organizational and workplace factors and sub-factors on the RTB of employees by using the structural equation model (SEM). Methods. A survey was conducted on 623 employees of the forest products sector in 64 enterprises in the provinces of Düzce, Bolu, Sakarya, Kocaeli and Yalova. Results. The results revealed that organizational and workplace factors had a significant effect on the RTB of the employees. However, no effect was found for individual factors, although the sub-factor of cognitive bias had a positive impact on RTB. In contrast, safety climate, safety training, use of personal protective equipment (PPE)-1 and working conditions negatively impacted the RTB of the employees. Conclusions. In terms of occupational health and safety, this study could serve to guide both sector managers and decision-makers on ways to improve the safety perception of their employees.

Accident Cause Factor of Fires and Explosions in Tankers Using Fault Tree Analysis

Fire and explosion accidents occur frequently in tankers because they transport large quantities of dangerous cargo. To prevent fire and explosion accidents, it is necessary to analyze factors that cause accidents and their effects. In this study, factors that cause fire and explosion accidents were classified using the 4M disaster analysis method, and each factor’s effect on the accident was analyzed using fault tree analysis (FTA). First, the unsafe tank atmosphere environment was identified as a primary cause of fire and explosion accidents in tankers, and the underlying causes of these accidents were investigated. The probability of underlying causes leading to primary causes was derived using an expert survey. The results showed that management and media factors had a greater impact on the unsafe tank atmosphere environment than human factors. To prevent fire and explosion accidents, it is necessary to ensure sufficient working and resting times for seafarers and compliance with procedures and work guidelines. A generalization of the results of present and future studies will enable the identification of the cause and preventive measures for fire and explosion accidents in tankers. Furthermore, this will reduce accidents in tankers and contribute to future safety management measures of ships and companies.

Developing an Ensemble Predictive Safety Risk Assessment Model: Case of Malaysian Construction Projects

Occupational Health and Safety (OHS)-related injuries are vexing problems for construction projects in developing countries, mostly due to poor managerial-, governmental-, and technical safety-related issues. Though some studies have been conducted on OHS-associated issues in developing countries, research on this topic remains scarce. A review of the literature shows that presenting a predictive assessment framework through machine learning techniques can add much to the field. As for Malaysia, despite the ongoing growth of the construction sector, there has not been any study focused on OHS assessment of workers involved in construction activities. To fill these gaps, an Ensemble Predictive Safety Risk Assessment Model (EPSRAM) is developed in this paper as an effective tool to assess the OHS risks related to workers on construction sites. The developed EPSRAM is based on the integration of neural networks with fuzzy inference systems. To show the effectiveness of the EPSRAM developed, it is applied to several Malaysian construction case projects. This paper contributes to the field in several ways, through: (1) identifying major potential safety risks, (2) determining crucial factors that affect the safety assessment for construction workers, (3) predicting the magnitude of identified safety risks accurately, and (4) predicting the evaluation strategies applicable to the identified risks. It is demonstrated how EPSRAM can provide safety professionals and inspectors concerned with well-being of workers with valuable information, leading to improving the working environment of construction crew members.

Safety Doesn't Happen by Accident: A Longitudinal Investigation on the Antecedents of Safety Behavior

Research recognizes the shared perceptions of the priority attributed to safety in comparison to other organizational goals (i.e., safety climate) as a potential antecedent of safety behavior among construction workers. This type of climate can dismantle barriers to the promotion of effective strategies to mitigate workplace hazards. On the other hand, the current understanding of the underlying process that links the perception of a safety climate to the implementation of safety behavior is far from being exhaustive. Accordingly, this study aimed to explore the role of risk perception and safety knowledge in explaining the positive impact of safety climate before attending a training course (Time 0) and safety behavior after the training completion (Time 1). Data were collected at two time-points on a sample of N = 278 construction workers taking part in different safety training courses promoted by a vocational training organization in Northern Italy. The hypothesized relationships were tested using a serial mediation model bootstrapping approach. The obtained results indicated that the perception of a safety climate at Time 0 (T0) among construction workers is associated with higher risk perception and safety knowledge that, in turn, resulted in a higher implementation of safety behavior at Time 1 (T1). These findings contribute to the understanding of those factors that constitute a fertile ground for preventing injuries and accidents in the construction sector.

Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China

The metallurgical industry is a significant component of the national economy. The main purpose of this study was to establish a composite risk analysis method for fatal accidents in the metallurgical industry. We collected 152 fatal accidents in the Chinese metallurgical industry from 2001 to 2018, including 141 major accidents, 10 severe accidents, and 1 extraordinarily severe accident, together resulting in 731 deaths. Different from traffic or chemical industry accidents, most of the accidents in the metallurgical industry are poisoning and asphyxiation accidents, which account for 40% of the total number of fatal accidents. As the original statistical data of fatal accidents in the metallurgical industry have irregular fluctuations, the traditional prediction methods, such as linear or quadratic regression models, cannot be used to predict their future characteristics. To overcome this issue, the grey interval predicting method and the GM(1,1) model of grey system theory are introduced to predict the future characteristics of fatal accidents in the metallurgical industry. Different from a fault tree analysis or event tree analysis, the bow tie model integrates the basic causes, possible consequences, and corresponding safety measures of an accident in a transparent diagram. In this study, the bow tie model was used to identify the causes and consequences of fatal accidents in the metallurgical industry; then, corresponding safety measures were adopted to reduce the risk.

Exerting Explanatory Accounts of Safety Behavior of Older Construction Workers within the Theory of Planned Behavior

Older construction workers are vulnerable to accident risks at work. Work behavior affects the occurrence of accidents at construction sites. This study aims to investigate the organizational and personal factors that underlie the safety behaviors of older construction workers considering their age-related characteristics. A cross-sectional questionnaire survey, which involves 260 older construction workers (aged 50 and over), was conducted, and an integrative old-construction-worker safety behavior model (OSBM) was established on the basis of the theory of planned behavior (TPB). Results showed that the OSBM provides a considerably good explanation of the safety behaviors of older construction workers. The explained variances for safety participation and compliance are 74.2% and 63.1%, respectively. Subjective norms and perceived behavioral control are two critical psychological drivers that proximally affect the safety behaviors of workers. Moreover, safety knowledge, management commitment, and aging expectation are the distal antecedents that significantly influence psychological drivers. This study proves the mediating role of psychological factors on predicting safety behaviors among older construction workers, thereby promoting an understanding of "how" and "why" their safety behaviors occur. Furthermore, the identified effects of several critical organizational and personal factors, particularly age-related factors, provide new insights into the safety behaviors of older construction workers.

Construction Worker Risk-Taking Behavior Model with Individual and Organizational Factors

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.

Emerging Issues in Occupational Safety and Health

Working environments have various risks, which result in accidents and casualties. [...].