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Improving Safety Performance of Construction Workers through Learning from Incidents

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  • Albert P. C. Chan

    (Shenzhen Research Institute of the Hong Kong Polytechnic University, Shenzhen 518057, China
    Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Junfeng Guan

    (Shenzhen Research Institute of the Hong Kong Polytechnic University, Shenzhen 518057, China)

  • Tracy N. Y. Choi

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Yang Yang

    (Shenzhen Research Institute of the Hong Kong Polytechnic University, Shenzhen 518057, China
    Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

  • Guangdong Wu

    (School of Public Policy and Administration, Chongqing University, Chongqing 400044, China)

  • Edmond Lam

    (College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China)

Abstract

Learning from incidents (LFI) is a process to seek, analyse, and disseminate the severity and causes of incidents, and take corrective measures to prevent the recurrence of similar events. However, the effects of LFI on the learner’s safety performance remain unexplored. This study aimed to identify the effects of the major LFI factors on the safety performance of workers. A questionnaire survey was administered among 210 construction workers in China. A factor analysis was conducted to reveal the underlying LFI factors. A stepwise multiple linear regression was performed to analyse the relationship between the underlying LFI factors and safety performance. A Bayesian Network (BN) was further modelled to identify the probabilistic relational network between the underlying LFI factors and safety performance. The results of BN modelling showed that all the underlying factors were important to improve the safety performance of construction workers. Additionally, sensitivity analysis revealed that the two underlying factors—information sharing and utilization and management commitment—had the largest effects on improving workers’ safety performance. The proposed BN also helped find out the most efficient strategy to improve workers’ safety performance. This research may serve as a useful guide for better implementation of LFI practices in the construction sector.

Suggested Citation

  • Albert P. C. Chan & Junfeng Guan & Tracy N. Y. Choi & Yang Yang & Guangdong Wu & Edmond Lam, 2023. "Improving Safety Performance of Construction Workers through Learning from Incidents," IJERPH, MDPI, vol. 20(5), pages 1-26, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4570-:d:1087727
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    References listed on IDEAS

    as
    1. Ran Gao & Albert P.C. Chan & Wahyudi P. Utama & Hafiz Zahoor, 2016. "Multilevel Safety Climate and Safety Performance in the Construction Industry: Development and Validation of a Top-Down Mechanism," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
    2. Peter Love & Heng Li & Zahir Irani & Olusegun Faniran, 2000. "Total quality management and the learning organization: a dialogue for change in construction," Construction Management and Economics, Taylor & Francis Journals, vol. 18(3), pages 321-331.
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    8. Elijah Frimpong Boadu & Cynthia Changxin Wang & Riza Yosia Sunindijo, 2020. "Characteristics of the Construction Industry in Developing Countries and Its Implications for Health and Safety: An Exploratory Study in Ghana," IJERPH, MDPI, vol. 17(11), pages 1-20, June.
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    Cited by:

    1. Ayodele Alejo & Clinton Aigbavboa & Douglas Aghimien, 2024. "How Can Safety Contribute to Working Conditions in the Construction Industry? A Conceptual Framework," Sustainability, MDPI, vol. 16(18), pages 1-14, September.

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