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为避免制造业企业员工反生产行为(CWB)对组织安全及绩效的负面作用,基于贝叶斯网络(BN)和计算试验方法,研究群体CWB的演化模型。首先将个体CWB发生机制和制造业企业调研数据相结合,通过参数学习构建出员工CWB的BN结构模型;然后基于该模型,结合社会影响和观点交换理论,设计员工群体行为交互规则,并嵌入计算试验模型中进行仿真分析。研究表明:员工规模增长不会导致群体CWB的扩散,而员工尽责性和组织分配公平对CWB的影响显著;员工间沟通范围和地缘关系会导致CWB的恶化,但控制关键在于防范初期的极化舆情;平行管理和有机式组织虽然能控制群体CWB,但在实施时存在风险。
To avoid the negative effect of CWB on organizational safety and performance, Bayesian Networks (BN) and computational test methods are used to study the evolutionary model of group CWB. Firstly, we combine the individual CWB mechanism with the manufacturing enterprise survey data to construct the BN structural model of employee CWB through parameter learning. Then, based on the model and the social influence and exchange of opinions theory, the interaction rules of employee group behavior are designed and embedded into the calculation Experimental model for simulation analysis. The research shows that the growth of employees does not lead to the spread of CWB group, while the employee responsibility and organizational distribution have a significant impact on the CWB. The communication range and geo-relationship between employees lead to the deterioration of CWB, but the key to control lies in preventing the initial polarization Public opinion; Although parallel management and organic organizations can control group CWB, there are risks in their implementation.