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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Introduction: A recent trend in health information seeking and sharing is the use of social media. Although there are several benefits to the use of social media for health communication, the quality of health information exchanged on social media is troubling due to its informal, unregulated mechanisms for information collection, sharing and promotion. Therefore, it is important to understand how users adopt health information from social media. Method: Considering the user-generated and storytelling nature of social media messages, this research employed the narrative paradigm perspective to explain the social media health information adoption phenomenon. Specifically, narrative coherence (NC) and narrative fidelity (NF) were hypothesised to have positive effects on the intention to adopt (IA). Additionally, socio-economic status (SES) was viewed as a proxy variable to cognitive capability and was hypothesised to moderate the effects of NC and NF. A scenario-based survey was conducted to test the proposed research model. Results: We obtained a total of 257 valid questionnaires. The results indicated that NF ( p < 0.001) had a positive effect on the IA social media health information. The NC ( p < 0.01) had no impact on the low SES users but a positive impact on the high SES users. Further, the effect of NF ( p < 0.01) on the IA was higher for high SES users than low SES users. Conclusions: NC and NF are two major driving forces in social media health information adoption, and the effect of both narrative paradigm variables depends on the SES users. Implications Results of this study show how the narrative paradigm, with a focus on the storytelling method of communication rather than logical scientific argument, can not only explain the uptake of health messages from social media, but also provide guidance as to how to create health messages on social media that more effectively target end users.

Sponsored advertisement(ad) has already become the major source of revenue for most popular search engines. One fundamental challenge facing all search engines is how to achieve a balance between the number of displayed ads and the potential annoyance to the users. Displaying more ads would improve the chance for the user clicking an ad. However, when the ads are not really relevant to the users' interests, displaying more may annoy them and even "train" them to ignore ads. In this paper, we study an interesting problem that how many ads should be displayed for a given query. We use statistics on real ads click-through data to show the existence of the problem and the possibility to predict the ideal number. There are two main observations: 1) when the click entropy of a query exceeds a threshold, the CTR of that query will be very near zero; 2) the threshold of click entropy can be automatically determined when the number of removed ads is given. Further, we propose a learning approach to rank the ads and to predict the number of displayed ads for a given query. The experimental results on a commercial search engine dataset validate the effectiveness of the proposed approach.

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