Background: Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps. Objective: This study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings. Methods: We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias. Results: Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention. Conclusions: This study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.

Background: Mobile health (mHealth) is becoming more popular as a way of sharing medical information. For the patient, it saves time, reduces the need for travel, reduces the cost of searching for information, and brings medical services “to your fingertips.” However, it also brings information overload and makes the patient’s choice of physician more difficult. Objective: This study aimed to identify the types of physician information that play a key role in patients’ choice of physician and to explore the mechanism by which this information contributes to this choice. Methods: Based on the stimulus-organism-response (SOR) model and online trust theory, we proposed a research model to explain the influence of physician information on patients’ choice of physician. The model was based on cognitive trust and affective trust and considered the moderating role of patient expertise. Study 1 was an eye-tracking experiment (n=42) to identify key factors affecting patients’ choice of physician. Study 2 was a questionnaire study (n=272); Partial Least Squares Structural Equation Modeling was used to validate the research model. Results: The results of Study 1 revealed that seven types of physician information played a key role in patients’ choice of physician. The results of Study 2 revealed that (1) physicians’ profile photo information affected patients’ choice of physician by positively influencing affective trust (P<.001); (2) physicians’ nonprofile photo information affected patients’ choice of physician by positively influencing cognitive trust (P<.001); (3) patient-generated information affected patients’ choice of physician by positively affecting cognitive trust (P<.001) and affective trust (P<.001), and patient expertise played a positive moderating role on both (P=.04 and P=.01, respectively); and (4) cognitive trust and affective trust both positively affected patients’ choice of physician, with affective trust playing a more significant role (P<.001 and P<.001, respectively). Conclusions: Seven types of physician information were mainly used by patients when choosing physicians offering mHealth services; trust played an important role in this choice. In addition, the level of patient expertise was an important variable in moderating the influence of physician information and patients’ trust. This paper supports the theoretical basis of information selection and processing by patients. These findings can help guide app developers in the construction of medical apps and in the management of physician information in order to facilitate patients’ choice of physician.

Purpose: A counterintuitive finding of existing research is that negative reviews can produce positive effects; for example, they can increase purchase likelihood and sales by increasing product awareness. It is important to continue highlighting this fact and to develop further insights into this positive effect, as a more thorough analysis can provide online retailers with a more comprehensive understanding of how to effectively manage and use negative reviews. Thus, by using an eye-tracking method, this paper attempts to provide a further thorough analysis of positive effects of negative reviews from a cognitive perspective.Design/methodology/approach: An eye-tracking experiment with two tests over a time delay was performed to examine whether negative reviews have some positive effects. Review valence (positive vs. negative), brand popularity (popular vs. unpopular) and advertising exposure (no repetition vs. repetition) were considered in the experiment.Findings: The results show that a cognitive process of attention allocation happens when consumers deal with brand popularity cues and that arousal evoking and attention allocation occur when handling review valence. Allocation of more attention to unpopular brands helps improve brand awareness and enhance brand memory, and larger arousal from negative reviews narrows attention and leads to a better memory of products and brands. However, with the passage of time, the memory of review valence can dissociate and fade, and the remaining awareness of and familiarity with unpopular brands with negative reviews contribute to a positive reversion, which leads to the production of positive effects from negative reviews.Originality/value: This paper contributes to the literature on online reviews by examining the visual processing of review valence and brand popularity with an eye-tracking method and by revealing the cognitive mechanism of positive effects of negative reviews from a visual attention perspective.