Eating meat can have detrimental effects on the environment, animal welfare, and a person’s health. However, consumers are often reluctant to reduce their meat consumption and public information-based awareness campaigns show little effect. As an alternative, some vegan activists and pressure groups employ emotion-based campaigns using meat-shaming techniques in the hope to change people’s meat consumption behavior. By publicly and often drastically criticizing consumers, they try to make them experience negative emotions and ultimately change their behavior. In three experimental studies, we explore whether a confrontational approach of putting meat-shaming messages on products is likely to affect consumer behavior. Specifically, we find that meat-shaming messages trigger shame but also other negative emotions that translate into reduced purchase intentions. The content of the message largely determines the different emotions that are evoked. The messages can activate both restore and protect motivations, either stimulating or hindering behavioral change. Interestingly, it does not seem to matter whether the meat-shaming message stems from a governmental organization, activist group, or private person and whether it is framed with a personal or informational appeal. If the source looks credible, the message influences consumer experience and behavioral intentions

In Design-Driven Innovation (D-DI) the meaning of a product or service is radically innovated to introduce a new paradigm that ideally can benefit people, companies, and society as a whole. However, due to the associated risks, most companies are hesitant to engage with and adopt D-DI. Human Centered Design (HCD) is preferred while innovation is limited to incremental change. This dichotomy is also reflected in design literature where D-DI is pitted against HCD. We propose the symbiosis of the two approaches as a strategy to create space for and the adoption of D-DI within companies. An instrumental design case study explores a design-driven service innovation and its adoption in a renowned airline. Results show an adopted D-DI where HCD evidence mitigates for the market and organization uncertainty while D-DI enabled a paradigm shift in the company's current service operation. Advantages and limitations of this mitigation strategy are discussed. With this design precedent, we aim to encourage designers and companies to further explore the benefits of a symbiotic use of D-DI and HCD.

Tables 4, 5 and 6 in the original version of this article contained some incorrect calculations. The correct tables are shown below. (Table presented.).

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 (meanpatient positive/expert negative=3.06, SD=0.94; meanexpert positive/patient negative=2.55, SD=0.89; F1,108=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 (meanpatient positive/expert negative=2.36, SD=0.76; meanexpert positive/patient negative=3.01, SD=0.81; F1,108=8.42, P=.004). This effect occurred despite the fact that they considered the patients to be less knowledgeable than experts. Conclusions: When confronted with information from both sources simultaneously, prospective patients are influenced more strongly by other patients. This effect reverses when the patient rating has been aggregated from a (very) small number of individual opinions. This has important implications for how to present health care provider ratings to prospective patients to aid their decision-making process.