As a common organizational phenomenon, employee silence behavior has various negative implications for organizations, making it critical to understand what factors can reduce employee silence. Drawing upon self-verification theory, this study explores the inhibiting effect of moral identity on silence via felt obligation towards organization. Meanwhile, we also examine the moderating effect of corporate social responsibility perception. We collected three waves of data with a two-month interval from 402 Chinese employees. Results indicated that moral identity positively predicted felt obligation towards (...) organization, which in turn negatively predicted silence behavior. The negative indirect effect of moral identity on silence behavior via felt obligation towards organization was moderated by corporate social responsibility perception. Specifically, this indirect effect was stronger when employees perceived a higher level of corporate social responsibility. By integrating self-verification theory with silence behavior research, our findings provide important insights into the prevention of employee silence behavior in organizations. (shrink)

Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes (...) is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition. (shrink)