Distinguishing between consumers' positive and negative affect is a popular approach in both marketing research and practice, but such valence-based approaches sacrifice specificity and explanatory power. As emotions of the same valence can greatly differ with regard to their underlying appraisal patterns, they also differently affect consumer judgment and behavior. Our meta-analysis of 1035 effect sizes (N = 40,777) across 10 discrete emotions shows that analyzing discrete emotions clearly outperforms models of core affect (valence and arousal) when studying firm-customer encounters. Specifically, we find that the greatest impact stems from the medium-arousal emotion of gratitude and that positive emotions show consistently stronger effect sizes than do negative emotions. We also examine how effects are moderated by situational characteristics of the experience triggering the emotion. Based on our findings, we develop recommendations that help marketers identify and manage consumers' emotions more effectively.

Background Increasing numbers of patients consult Web-based rating platforms before making health care decisions. These platforms often provide ratings from other patients, reflecting their subjective experience. However, patients often lack the knowledge to be able to judge the objective quality of health services. To account for this potential bias, many rating platforms complement patient ratings with more objective expert ratings, which can lead to conflicting signals as these different types of evaluations are not always aligned. Objective This study aimed to fill the gap on how consumers combine information from 2 different sources—patients or experts—to form opinions and make purchase decisions in a health care context. More specifically, we assessed prospective patients’ decision making when considering both types of ratings simultaneously on a Web-based rating platform. In addition, we examined how the influence of patient and expert ratings is conditional upon rating volume (ie, the number of patient opinions). Methods In a field study, we analyzed a dataset from a Web-based physician rating platform containing clickstream data for more than 5000 US doctors. We complemented this with an experimental lab study consisting of a sample of 112 students from a Dutch university. The average age was 23.1 years, and 60.7% (68/112) of the respondents were female. Results The field data illustrated the moderating effect of rating volume. If the patient advice was based on small numbers, prospective patients tended to base their selection of a physician on expert rather than patient advice (profile clicks beta=.14, P <.001; call clicks beta=.28, P =.03). However, when the group of patients substantially grew in size, prospective patients started to rely on patients rather than the expert (profile clicks beta=.23, SE=0.07, P =.004; call clicks beta=.43, SE=0.32, P =.10). The experimental study replicated and validated these findings for conflicting patient versus expert advice in a controlled setting. When patient ratings were aggregated from a high number of opinions, prospective patients’ evaluations were affected more strongly by patient than expert advice (mean patient positive/expert negative =3.06, SD=0.94; mean expert positive/patient negative =2.55, SD=0.89; F 1,108 =4.93, P =.03). Conversely, when patient ratings were aggregated from a low volume, participants were affected more strongly by expert compared with patient advice (mean patient positive/expert negative =2.36, SD=0.76; mean expert positive/patient negative =3.01, SD=0.81; F 1,108 =8.42, ...

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