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1. chinaXiv:202104.00085 [pdf]

中国情境下家长式领导与员工绩效关系的元分析

刘豆豆; 胥彦; 李超平
Subjects: Psychology >> Management Psychology

本研究采用元分析技术和效标剖面元分析技术探讨中国情境下家长式领导与员工绩效之间的关系。通过文献收集与筛选, 共纳入139项研究400个效应值(N = 44605)。元分析结果发现:(1)仁慈领导、德行领导与任务绩效和组织公民绩效有较强的正相关关系,与反生产绩效有较强的负相关关系。与之相反,威权领导与任务绩效和组织公民绩效之间有显著的负相关关系,与反生产绩效显著正相关。(2)低威权领导剖面(仁慈领导和德行领导水平高)对任务绩效和组织公民绩效的预测力最强,高威权领导剖面(仁慈领导和德行领导水平低)对反生产绩效的预测力最强。(3)年龄能够调节家长式领导部分维度和绩效之间的关系强度,性别对家长式领导分维度和绩效关系的调节效应不显著。研究结果进一步揭示了中国情境下家长式领导与个体绩效之间关系的“真相”。

submitted time 2021-04-20 Hits57Downloads27 Comment 0

2. chinaXiv:202104.00084 [pdf]

共情对公平决策的影响——来自ERP的证据

何怡娟; 胡馨木; 买晓琴
Subjects: Psychology >> Social Psychology
Subjects: Psychology >> Cognitive Psychology

本研究运用事件相关电位技术(event-related potential, ERP)和最后通牒博弈范式(ultimatum game, UG)考察了共情关怀对公平决策的调节作用, 其中被试作为响应者分别对需要帮助的提议者(高共情条件)和不需要帮助的提议者(低共情条件)提出的分配方案(公平、劣势不公平和优势不公平)进行接受或拒绝的选择。行为结果显示劣势不公平条件下, 高共情条件的接受率高于低共情条件, 而优势不公平条件下呈现相反的结果。ERP结果显示: 他人提出的优势不公平方案, 在低共情条件较高共情条件下诱发了更负的前部N1(anterior N1, AN1), 在高共情条件比低共情条件下诱发了更大的P2波幅; 高共情条件下, 他人提出的劣势不公平方案较优势不公平和公平方案诱发了更负的内侧额叶负波(medial frontal negativity, MFN); P3在公平条件下的波幅较劣势不公平条件下更大, 但并未受到共情关怀的调节。这些结果表明共情关怀不仅调节了公平决策行为, 还调节了公平加工的早期注意和动机及之后的认知和情绪加工, 但由P3表征的高级认知过程仅受到公平性的调节而不受共情水平的影响。

submitted time 2021-04-20 Hits63Downloads27 Comment 0

3. chinaXiv:202104.00083 [pdf]

Method Based on Support Vector Machine and Sequential Backward Selection for Seismic Liquefaction Potential Evaluation

Jianping LI; Runrun DONG; Jiansheng WANG; Ling CHEN
Subjects: Civil Engineering and Building Construction >> Civil Construction Structures

[Object] In the paper, the support vector machine(SVM) is utilized to evaluate the earthquake-induced site liquefaction potential, and an optimization algorithm based on cross validation and sequential backward selection(SBS) is proposed to improve the generalization ability of the classifier for seismic liquefaction potential evaluation(SLPE). [Methods] Usually, the accuracy of SLPE using the SVM varies greatly when the training dataset and test dataset change, so the classifier is not reliable enough in practice. Because cross validation is more convincing for evaluating the classifier performance in machine learning , the algorithm in the paper tries to reduce the maximum error of cross validation through adopting SBS to determine the input variables of the SVM. The performance of the classifier is assessed by the area under the curve (AUC) on the basis of confusion matrix. [Results] As shown by data validation, the algorithm can reduce the maximum error of cross validation and the variation of accuracy in SLPE while maintaining good performance of the classifier. [Conclusions] In conclusion, a method that can improve the reliability of SVMs for classification in SLPE is put forward in the paper.

submitted time 2021-04-19 Hits45Downloads22 Comment 0

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