In the face of increasing environmental pressures, environmentally friendly behaviour can help companies achieve truly sustainable growth. The issue of how to promote environmental behaviour among employees is a new challenge for leaders. However, studies do not systematically reveal the mechanisms of the effects of self-sacrificial leadership on employees’ organisational citizenship behaviour for the environment (OCBE). Based on social learning theory and the attitude–behaviour–context model, we investigated the impact of self-sacrificial leadership on employees’ OCBE by focusing on the mediating role of the corporate social responsibility (CSR) as perceived by employees, and the moderating role of the pro-environmental organisational climate (PEOC). The results of a field survey of 461 employees (small- and medium-sized enterprises) in China indicate that self-sacrificial leadership was positively related to employees’ OCBE; this relationship was partially mediated by employees’ perception of CSR. Moreover, PEOC strengthened the effect of employees’ perceived CSR on OCBE, and the mediating effect of employees’ perceived CSR on the relationship between self-sacrificial leadership and OCBE. Our findings not only help scholars understand the mechanism of the effect of self-sacrificial leadership on employees’ OCBE, but also provide insights for recommending integrated management models, social responsibility, and environmental protection.

The aim of change mining is to discover changes in process models based on execution data recorded in event logs. There may be hidden transitions in the process models related to, for example, business integration and user requirements that do not exist in event logs. Behavioral change mining in the case of hidden transitions is a fundamental problem in the field of change mining. Existing research on change mining has not considered the effects of hidden transitions. This paper proposes a novel method based on complete logs with hidden transitions for mining behavioral changes. We analyze the behavioral relations of activities based on changed logs under the condition that the original model is unknown. Log-driven change mining is realized by calculating the log behavioral profile, minimum successor relation, and log-weighted coefficient, which allows the mining of hidden transitions, as well as changed behavioral relations. Finally, this method is applied to disaster chain risk analysis, and the evolution of disaster chains in different scenarios is mined from disaster logs to determine the type of disaster chain. The results of this paper provide a scientific basis for the strategy of chain-cutting disaster mitigation in the emergency management of disaster chains.