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Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents

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Listed:
  • Ziwei Fa

    (School of Management, China University of Mining & Technology, Xuzhou 221116, China)

  • Xinchun Li

    (School of Management, China University of Mining & Technology, Xuzhou 221116, China)

  • Quanlong Liu

    (School of Management, China University of Mining & Technology, Xuzhou 221116, China)

  • Zunxiang Qiu

    (School of Management, China University of Mining & Technology, Xuzhou 221116, China)

  • Zhengyuan Zhai

    (School of Management, China University of Mining & Technology, Xuzhou 221116, China)

Abstract

It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China’s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing “trajectories” in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.

Suggested Citation

  • Ziwei Fa & Xinchun Li & Quanlong Liu & Zunxiang Qiu & Zhengyuan Zhai, 2021. "Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents," IJERPH, MDPI, vol. 18(9), pages 1-16, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:5020-:d:551273
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    References listed on IDEAS

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    1. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
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    Cited by:

    1. Xiaofang Wo & Guichen Li & Yuantian Sun & Jinghua Li & Sen Yang & Haoran Hao, 2022. "The Changing Tendency and Association Analysis of Intelligent Coal Mines in China: A Policy Text Mining Study," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    2. Xiangmei, Wang & Xiaoxiao, Geng & Wang, Yingchen, 2023. "Research on the network topology characteristics of unsafe behavior propagation in coal mine group from the perspective of human factors," Resources Policy, Elsevier, vol. 85(PA).
    3. Li Yang & Xue Wang & Junqi Zhu & Liyan Sun & Zhiyuan Qin, 2022. "Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD," IJERPH, MDPI, vol. 19(17), pages 1-29, August.
    4. Lei Chen & Hongxia Li & Shuicheng Tian, 2022. "Application of AHP and DEMATEL for Identifying Factors Influencing Coal Mine Practitioners’ Unsafe State," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    5. Roger Jensen & David P. Gilkey, 2023. "Risk-Reduction Research in Occupational Safety and Ergonomics: An Editorial," IJERPH, MDPI, vol. 20(6), pages 1-4, March.

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