Risk management and workers’ safety behavior control in coal mine

Cao, Qing; Li, Kai; Ye-jiao, Liu; Qi-hua, Sun; Zhang, Jian

doi:10.1016/j.ssci.2011.08.005

Cited by 56 publications

(20 citation statements)

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“…Third, as our data were only collected from three large, state-owned coal mines in northern China, the generalizability of our findings to other organizations is questionable. There are huge differences in the job characteristics and environment between coal mines and other industries ( Qing-gui et al, 2012 ). Thus, to enhance the universality of the current conclusions future research should test our model in more diverse industries.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Job Demands and Employees’ Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety

Chen

Xia

et al. 2017

Front. Psychol.

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This study aims to explore the relation between job demands and counterproductive work behaviors (CWBs). A cross-sectional sample of 439 coal miners completed a self-report questionnaire that assessed their job demands, psychological detachment, job anxiety, and CWBs in a Chinese context. The conceptual model, based on the stressor-detachment model, was examined using structural equation modeling. The results suggest that psychological detachment mediates not only the relation between job demands and job anxiety but also that between job demands and CWBs. Furthermore, the relation between job demands and CWBs is sequentially mediated by psychological detachment and job anxiety. Our findings validate the effectiveness of the stressor-detachment model. Moreover, we demonstrate that the underlying mechanism of the relation between job demands and CWBs can be explained by psychological detachment and job anxiety.

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Section: Discussionmentioning

confidence: 99%

The Relationship between Job Demands and Employees’ Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety

Chen

Xia

et al. 2017

Front. Psychol.

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“…Risk management [19][20][21][22][23][24] Predicting the expected risk levels by using decomposition technique in time series analysis; analyzing and optimizing the risk management system; using public communication system to monitor unsafe behavior in real time; reducing the effects of coal mining on social and ecological exploitation; constructing potential hazards database in an underground mine; evaluating the reliability of human safety barrier in coal mine emergency evacuation; identifying the risk factors and evaluating the safety control capability.…”

Section: Themementioning

confidence: 99%

“…Sari et al [19] developed a stochastic model to predict the number of accidents according to the randomness in the occurrence of accidents. Qing-gui et al [20] constructed a system to supervise unsafe behavior, release early warning information, and improve controlling measures in coal mine. Based on case studies, Kowalska [21] identified and assessed the risk sources.…”

Section: Themementioning

confidence: 99%

An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

et al. 2017

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Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

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“…In the study of unsafe behavior factors in underground coal mine safety management, Paul et al found that many unsafe factors are the direct causes of accidents and industrial injuries, and some unsafe behavior factors will still make the production of coal mine enterprises in dangerous state even if they do not lead to accidents and injuries [22]. If the unsafe behavior of miners cannot be forewarned and controlled in time, it may have serious consequences on the safety of production and safety of workers in coal mines [23]. Therefore, the study on unsafe behavior by coal miners has important practical significance for preventing and reducing the number of accidents.…”

Section: Introductionmentioning

confidence: 99%

Characteristic Analysis of Unsafe Behavior by Coal Miners: Multi-Dimensional Description of the Pan-Scene Data

Tong

Zhang

Pengcheng

et al. 2018

IJERPH

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As a high-risk occupation, coal mining has many accidents, primarily due to the unsafe behavior of coal miners. Based on the research of analysis of unsafe behavior and pan-scenario data of miners, a theoretical framework for the analysis of unsafe behavior characteristics was proposed in this paper. The collected data were divided into realistic scenes and abstract scenes according to different manifestations; the pan-scene data were described from the eight dimensions of time, behavioral trace, location, behavioral property, behavioral individual, degree, unsafe action, and specialty using a quantitative method for the structure conversion; and the rules were discovered through cluster analysis and association analysis. A total of 225 coal mine gas explosion accidents were used for analysis, and the pan-scene data description and structure conversion of unsafe behavior that caused these accidents were realized. In a certain cluster, the distribution rules of dimensions and the interaction between different dimensions of unsafe behavior were explored after analysis. The results show that the proposed eight dimensions can fully explain the basic characteristics and attributes of the unsafe behavior of coal miners. The structure conversion can reduce the workload of managers and effectively improve the safety data processing capabilities, and the result of data analysis can provide data support and a management basis for safety management. A new method and thought for the data analysis of miners’ unsafe behavior is provided.

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Risk management and workers’ safety behavior control in coal mine

Cited by 56 publications

References 0 publications

The Relationship between Job Demands and Employees’ Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety

The Relationship between Job Demands and Employees’ Counterproductive Work Behaviors: The Mediating Effect of Psychological Detachment and Job Anxiety

An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

Characteristic Analysis of Unsafe Behavior by Coal Miners: Multi-Dimensional Description of the Pan-Scene Data

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