Banus Kam Leung LOW

Dr. Banus Kam Leung LOW

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
BLBanus Kam Leung LOW
Banus Kam Leung LOW
Siu Shing ManSiu Shing Man
Siu Shing Man
Alan Hoi Shou ChanAlan Hoi Shou Chan
Alan Hoi Shou Chan
SASaad Alabdulkarim
Saad Alabdulkarim
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.

BLBanus Kam Leung LOW
Banus Kam Leung LOW
Siu Shing ManSiu Shing Man
Siu Shing Man
Alan Hoi Shou ChanAlan Hoi Shou Chan
Alan Hoi Shou Chan
High accident rates have been a complicated and persistent problem in the Hong Kong construction industry. This situation has stimulated this investigation into factors that influence the risk-taking propensity of construction workers. However, interviewing workers who had a bad experience is problematic because changes in attitude and perception may occur as a result of such an experience. Using quasi-expert interviews can reduce this problem. The objective of this study was to identify factors that influence the risk-taking propensity of construction workers. Semi-structured interviews were conducted with 16 safety professionals all with accident inspection experience and six super-safe workers with no incident record for the past five years. Seven factors that affect the risk-taking propensity of construction workers were successfully identified. Each factor is thoughtfully discussed, and this study shows that quasi-expert interview is a pragmatic approach for deepening the understanding of risk-taking propensity among construction workers. Findings of this study will hopefully help and encourage further quantitative research on the risk-taking propensity of construction workers with different perspectives.

1 January 2017 in The Japanese Journal of Ergonomics
Verified
Journal Article
Open access
PDF file
BLBanus Kam Leung LOW
Banus Kam Leung LOW
Alan Hoi Shou ChanAlan Hoi Shou Chan
Alan Hoi Shou Chan
Accident prevention is a major health and safety concern in the construction sector and understanding risk-taking behavior plays an essential role in this initiative. The efficient measurement of workers' risk-taking propensity would be a pragmatic approach. Several instruments can be used for measuring risk-taking behavior, but instruments pertinent for construction workers are rare. This study aims to establish a questionnaire for construction workers' risk taking (Q-CWRT) by adapting the explained theory of planned behavior (ETPB), which consists of organizational, workplace and individual-related factors. This questionnaire includes 188 valid samples (156 male and 32 female), and 12 factors with 41 items were extracted based on the results of exploratory factor analysis. The Q-CWRT goodness-of-fit results were achieved (CMIN = 1.4, CFI = 0.925, TLI = 0.912, RMSEA = 0.046, SRMR = 0.0625, and p < 0.05) using confirmatory factor analysis. This study demonstrated the potential of the proposed new and prominent instrument that would help employers in acquiring better understanding of factors that contribute to improving safety performance.