激励
煤矿开采
博弈论
可靠性(半导体)
风险分析(工程)
环境经济学
煤
损失厌恶
产量(工程)
进化稳定策略
进化博弈论
业务
生产(经济)
管理策略
成本效益分析
前景理论
营销
理论(学习稳定性)
灵敏度(控制系统)
运营管理
战略管理
心理会计
可持续发展
风险管理
环境资源管理
风险厌恶(心理学)
订单(交换)
作者
Shuicheng Tian,Shiqiang Ning,Fangyuan Tian,Lei Chen,Zilong Pan,Hongxia Li
摘要
ABSTRACT Effective near‐miss reporting is important for achieving high reliability in safety management. To enhance the effectiveness of near‐miss reporting, this paper examines the dynamic evolution of stakeholders' decision‐making behaviors using coal mines as a case study. A tripartite evolutionary game model is developed, involving enterprises, management, and employees, based on prospect theory and mental accounting theory (PT‐MA theory). Stability analysis and simulations under varying parameters yield the following insights: (i) A higher initial probability of active decision‐making promotes positive strategy evolution, with sensitivity ranked as enterprise management employee. (ii) Near‐miss reporting is mainly driven by cost considerations, with stakeholders' sensitivity to cost changes ranked as employee management enterprise. Lowering the perceived cost of active strategies and raising that of passive strategies encourages active decisions. (iii) A combined reward–punishment strategy is more effective than either alone in motivating employees and management. (iv) The psychological stress experienced by management when the enterprise adopts passive strategies is greater than that employees feel in response to management's passive strategies. Reducing such stress facilitates active strategy evolution. (v) Modifying reference points for perceived benefits and costs, reducing risk preference, and increasing sensitivity to loss aversion can facilitate active strategy evolution. This research offers practical implications for improving near‐miss reporting management in the coal mine industry, including optimizing incentive mechanisms, implementing role‐specific psychological interventions, and adjusting cost–benefit perceptions. These insights may also be applicable to other high‐risk industries facing similar challenges, contributing to more effective and sustainable safety management practices.
科研通智能强力驱动
Strongly Powered by AbleSci AI