E-consultation is considerably significant in today’s highly competitive environment in reducing medical costs, improving operational efficiency and effectiveness, enhancing the equity of medical resources, and meeting customers’ satisfaction. However, the prosperity of e-consultation depends on doctors’ consistent participation and continued contribution. In addition, receiving a monetary return may be deemed as one of the major factors for doctors to stay on e-consultation platforms and provide the needed online services. This study investigates how a physician can achieve economic success in the online consultation market from the perspective of signaling theory. A secondary data econometric analysis using transaction data obtained from an e-consultation Website (haodf.com) was conducted. The obtained results indicate that both seller signals (knowledge contribution) and market signals (reputation), which are observable from the Website, may positively affect physicians’ income. In fact, the two types of online signals are the substitutes for each other; in addition, the impacts of both seller signals (knowledge contribution) and market signals (reputation) obtained from the Website were negatively moderated (weakened) by the offline signals (status). This study contributes to the literature by making a clear distinction between the online and offline signals, as well as between the seller signals and market signals. This study also provides practical implications, indicating that knowledge contribution and reputation are two important factors for physicians to increase their online income.

The purpose of this paper is to investigate the moderating effect of disease risk in terms of the major signals (i.e. status, reputation and self-representation) on the e-consultation platforms. In this study, the proposed research hypotheses are tested using the transaction data collected from xywy.com (in Need of Therapy). In fact, xywy.com is one the leading e-consultation service websites in China that provides a platform for the interactions between the physicians and patients (Yu et al., 2016, Peng et al., 2015). Generally speaking, it has all the needed design elements and in other words, a standard e-consultation website should have such items/components as physician homepage, physician review, free consultation, paid consultation and recommendation systems. The obtained results reveal that all attributes including status, reputation and self-representation have a positive impact on physician’s online order volume. Moreover, there is a positive moderating effect of disease risk onto the online reputation, indicating a higher effect exists for the diseases with high risk. However, the effect of offline status and online self-representation is not moderated by the disease risk, indicating market signals (online reputation) may have a stronger predictive power than seller signals (offline status and online self- representation), and therefore market signals are more effective when/if the disease risk is high. E-consultation has gradually become a significant trend to provide the healthcare services, in the emerging economy such as China because of shortage of medical resources but having an adequate access in internet usage. The impacts of signals on the health care market have been validated by previous studies. However, the research focusing on the moderating effect of signaling environment in the health care industry is still lacking. As a result, the value of this research helps to bridge the aforementioned research gap.

Videos, especially short videos, have become an increasingly important source of information in these years. However, many videos spread on video sharing platforms are misleading, which have negative social impacts. Therefore, it is necessary to find methods to automatically identify misleading videos. In this paper, three categories of features (content features, uploader features and environment features) are proposed to construct a convolutional neural network (CNN) for misleading video detection. The experiment showed that all the three proposed categories of features play a vital role in detecting misleading videos. Our proposed approach that combines three categories of features achieved the best performance with the accuracy of 0.90 and the F1 score of 0.89. It also outperformed other baselines such as SVM, k-NN, decision tree and random forest models by more than 22%.

(1) Background: Traditional one-to-one online consultations with doctors often fail to provide timely and accurate treatment plans; consequently, creating cross-hospital and cross-regional teams has become a new pattern for doctors aiming to offer Internet medical services. Because the online doctor team is a new virtual organizational model, it remains to be explained and investigated. (2) Methods: Combining the information processing view and the social categorization view, this study takes the perspective of team diversity and empirically investigates the effect of team diversity on team performance. We consider four kinds of team diversity, including status capital diversity, decision capital diversity, online reputation diversity, and professional knowledge diversity, and we investigate how team composition from the diversity perspective affects online doctor team performance and how leader reputation moderates the effect of team diversity on team performance. We use secondary data from a leading online medical consultation platform in China (Good Doctor), and our research data include 1568 teams with a total of 5481 doctors. (3) Results: The results show that status capital diversity and decision capital diversity negatively affect team performance; diversity in terms of online reputation and professional knowledge positively affect team performance; and leader reputation moderates the impact of status capital diversity and online reputation on team performance. (4) Conclusions: Our study offers management suggestions on how to form a high-performance doctor team and provides advice for the future development of online doctor teams.