Based on self-determination theory, we conceptualized the effect of empowering leadership on employee’s voice via harmonious passion. This paper further explored the moderating effect of job characteristics in voice process, and proposed a moderated mediation model. Using a sample of 674 supervisor-subordinate dyads, we found that (1) empowering leadership was positively related to employees’ harmonious passion and voice behavior, (2) harmonious passion played a mediating role in the relationship between empowering leadership and employee voice, and (3) job autonomy strengthened the (...) effect of harmonious passion on employee voice, which, in turn, enhanced the mediated relationship between empowering leadership and employee voice via harmonious passion. We discuss the implications of these findings for research and practice. (shrink)

Drawing on social information processing theory, the present study examines how and when leader coaching can be beneficial for team performance. Based on a sample of 58 teams from a sanitary product company in China, we found that peer coaching served as a mediator linking leader coaching and team performance. Moreover, the team individualistic/collectivism value moderated the first-stage relationship that the relationship between leader coaching and peer coaching was more positive when the team individualism value was low, but not significant (...) when the team individualism value was high; while team task interdependence moderated the second-stage relationship that the relationship between peer coaching and team performance was more positive when the team task interdependence was high, but not significant when it was low. The findings enrich our understandings of the effectiveness of leader coaching behavior by uncovering the theoretical mechanism and boundary conditions. The study also provides important implications for coaching practice in organizations. (shrink)

Employees frequently engage in job crafting to better match their jobs with their personal abilities and skills. Compared with its benefits, the potential detrimental consequences of job crafting have received less attention from researchers. Drawing on relative deprivation theory, we examined employees’ potential negative reactions to coworkers’ job crafting. We proposed that coworkers’ job crafting is positively related to employees’ feelings of relative deprivation, thus reducing prosocial behaviors and giving rise to social undermining. We further argued that employees’ zero-sum mindset (...) moderates the relationship between coworkers’ job crafting and employees’ feelings of relative deprivation, such that the relationship is more positive when the zero-sum mindset is high rather than low. Our hypotheses were generally supported by time-lagged data collected from a sample of 313 employees and their leaders from 85 teams. Our findings advance the understanding of the unintended consequences of job crafting in organizations. (shrink)

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation. Here, we proposed a DA method for the motor imagery EEG signal called brain-area-recombination. For the BAR, each sample was first separated into two ones by left/right brain channels, and the artificial samples were generated by (...) recombining the half-samples. We then designed two schemas corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification. To make a comparative investigation, we selected two common DA methods, and the BAR method beat them. Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM, note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI. (shrink)