When individuals interact with others, perceived information is transmitted among their brains. The EEG-based hyperscanning technique, which provides an approach to explore dynamic brain activities between two or more interactive individuals and their underlying neural mechanisms, has been applied to study different aspects of social interactions since 2010. Recently there has been an increase in research on EEG-based hyperscanning of social interactions. This paper summarizes the application of EEG-based hyperscanning on the dynamic brain activities during social interactions according to the (...) experimental designs and contents, discusses the possibility of applying inter-brain synchrony to social communication systems and analyzes the contributions and the limitations of these investigations. Furthermore, this paper sheds light on some new challenges to future EEG-based hyperscanning studies and the emerging field of EEG-based hyperscanning for pursuing the broader research field of social interactions. (shrink)

The issues of organizational wrongdoing damage organizational performance and limit the development of organizations. Although organizational members may know the wrongdoing and have the opportunity to blow the whistle, they would keep silent because of the interpersonal risks. However, leaders can play an important role in shaping employee whistleblowing. This study focuses on discovering the mechanisms of how authentic leaders influence employee whistleblowing with a sample from China. Results demonstrate that authentic leadership is positively related to internal whistleblowing. Team psychological (...) safety partly mediates the relationship between authentic leadership and internal whistleblowing. Personal identification partly mediates the relationship between authentic leadership and internal whistleblowing. The study contributes to the extant theory by filling the gap between leadership and whistleblowing. (shrink)

Drawing upon conservation of resources theory, we develop and test a theoretical model that specifies how and when employee humor toward leaders affects leader abusive supervision. We propose that employee humor is negatively associated with leader abusive supervision via leader relational energy. Furthermore, the negative indirect relationship between employee humor and leader abusive supervision via leader relational energy is stronger for female leaders than for male leaders. An experiment and a multi-wave, multi-source field study provide substantial support for our hypotheses. (...) Our findings contribute to the abusive supervision literature by identifying employee humor as a safe and effective bottom-up approach to prevent leader abusive supervision. (shrink)

The recent COVID-19 crisis caught many by surprise. Yet some firms were better prepared to weather the downturn than others. Using a comprehensive data set that observes over 15,000 firms in 27 countries, including several developing countries, shortly before and after the pandemic, we document that pre-crisis innovation affected firm’s survival odds and performance thereafter. The results show that innovative firms are less likely to close and perform better than non-innovators during the pandemic crisis. Innovative firms are also more optimistic (...) about the future than non-innovators. Our results further indicate that firm’s adaptability mediates the relationship between innovation and survival outcomes. The study finding shows that innovative firms are more likely to introduce new products, remote work arrangements, increase delivery, pivoting, and online activities than non-innovators during the pandemic crisis. (shrink)

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are (...) inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. (shrink)