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Analysis on the Acceptance Degree of the Concept of Amateur Safety Management for Enterprise Employees

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Application of Intelligent Systems in Multi-modal Information Analytics (MMIA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 929))

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Abstract

The safety of employees outside working hours is equally important to the enterprise. The safety accidents of employees within working hours and those of employees outside working hours will cause losses to the enterprise. This paper analyzes the acceptance degree of the concept of “amateur safety management” in 10 coal mining enterprises in China, and draws the following conclusions: in the first measurement, the scores of employees at different levels in understanding the concept of “amateur safety management” ranged from high to low: the management, foremen, professionals and the front-line staff; In the second measurement, that is, after training, the scores of employees at different levels in understanding the concept of “amateur safety management” ranged from high to low: foremen, the management, professionals and the front-line staff. The understanding of the concept of “amateur safety management” by enterprises is not good for our employees in the overall understanding. Before the training, the average understanding of the concept of “amateur safety management” of the 10 state-owned enterprises was 67 points, slightly higher than the average score of 65 of the 5 poor enterprises in North America. After training, the understanding of this concept was 74 points, which is between the average score of 100 points scored by five enterprises with advanced safety performance and the average score of 65 points scored by five enterprises with poor safety performance in North America.

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Correspondence to Wei Jiang .

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Jiang, W., Han, W. (2019). Analysis on the Acceptance Degree of the Concept of Amateur Safety Management for Enterprise Employees. In: Sugumaran, V., Xu, Z., P., S., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2019. Advances in Intelligent Systems and Computing, vol 929. Springer, Cham. https://doi.org/10.1007/978-3-030-15740-1_36

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