领导预期下属非工作时间随时保持联系、并且及时回复工作信息已成为当下数字经济时代中的职场新常态。文章基于资源保存理论,构建领导非工作时间电子通信预期(After-hours electronic communication expectations,AECE)影响下属工作绩...领导预期下属非工作时间随时保持联系、并且及时回复工作信息已成为当下数字经济时代中的职场新常态。文章基于资源保存理论,构建领导非工作时间电子通信预期(After-hours electronic communication expectations,AECE)影响下属工作绩效的多路径模型。通过实验研究,以及多时点、多来源的问卷调查研究,结果发现:(1)在资源获益路径,领导AECE会通过增强下属组织自尊,提升工作绩效;(2)在资源损耗路径,领导AECE会增加下属的压力感知;(3)在资源威胁路径,领导AECE会引发下属的名声担忧,进而降低工作绩效;(4)自我领导调节资源威胁路径,即当下属自我领导水平较高时,领导AECE通过名声担忧降低工作绩效的间接效应被削弱。整合的理论框架为解释领导AECE对下属工作绩效的复杂影响提供更全面的解释,这不仅丰富了AECE相关文献,拓展了资源保存理论在数字经济管理背景下的应用,同时也为“随时待命”这一职场新常态提供管理实践启示。展开更多
The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) m...The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.展开更多
文摘领导预期下属非工作时间随时保持联系、并且及时回复工作信息已成为当下数字经济时代中的职场新常态。文章基于资源保存理论,构建领导非工作时间电子通信预期(After-hours electronic communication expectations,AECE)影响下属工作绩效的多路径模型。通过实验研究,以及多时点、多来源的问卷调查研究,结果发现:(1)在资源获益路径,领导AECE会通过增强下属组织自尊,提升工作绩效;(2)在资源损耗路径,领导AECE会增加下属的压力感知;(3)在资源威胁路径,领导AECE会引发下属的名声担忧,进而降低工作绩效;(4)自我领导调节资源威胁路径,即当下属自我领导水平较高时,领导AECE通过名声担忧降低工作绩效的间接效应被削弱。整合的理论框架为解释领导AECE对下属工作绩效的复杂影响提供更全面的解释,这不仅丰富了AECE相关文献,拓展了资源保存理论在数字经济管理背景下的应用,同时也为“随时待命”这一职场新常态提供管理实践启示。
文摘The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
