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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.

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PurposeA 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/approachAn 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.FindingsThe 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/valueThis 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.

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