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Fear of COVID-19 has been understandably widespread, given continual exposure to dire information from pandemic media coverage and interpersonal communications. The present study addresses a limitation of the extended parallel process model in predicting fear of COVID-19 by inclusion of the concept of emotional contagion. The main gap in the literature is filled by the study’s distinctive contribution that broadens and upgrades the extended parallel process model. The model is extended by its integration with the theory of emotional processing. The study is based on a national panel of adults (N = 206). The methods include path modeling by SmartPLS. In addition, multigroup analyses examine overall model differences between gender classifications. Findings and conclusions can be used to minimize excessive fear, and at the same time to promote confidence in following official public guidance and protective regulations to cope with the pandemic. © The Author(s) 2023.
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This study presents an original model that features the emotion of fear of COVID-19 as a direct effect on vaccination intentions. A central research question addressed in the study is what roles do the emotion of fear of contracting COVID-19 and the threat posed by uptake of the COVID-19 vaccination play in levels of vaccination intention? The study used a structural equation model (SEM) and applied the SmartPLS 3.2.6 data analysis tool for model estimation and multivariate analysis variables. A key finding is that vaccination resistance is strongest when fear of COVID-19 is lower, and vaccination threat higher. Vaccination threat appraisal and vaccination intention were found to have a negative relationship. Response costs at higher levels lessen motivation for COVID-19 vaccination. Research implications include research-based targeting of differing segments by their primary fear, either fear of COVID-19 or of the preventative vaccine.
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With the increasing use of AI in marketing, ethical repercussions are beginning to emerge. From privacy issues, through discrimination of marginalized groups, to emergent systemic social distortions, AI is changing the marketing ethical landscape. In this paper we conduct a structured literature review of the emerging ethical issues posed by AI in the domains of marketing and consumer behavior. We identify three clusters of ethical issues (algorithm, society and existential) and map these to the marketing domains of systems, brands, and consumers. We conclude that the field of ethical marketing AI is still very much in its infancy, but such is the rate of development ethical marketing AI is likely to become an important field for academics and practitioners alike. © 2023 IEEE Computer Society. All rights reserved.
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Social media platforms have become more polarizing with the emergence of polarizing influencers. This research investigates how polarizing influencers can improve the effectiveness of brand-posts with the help of three experiments and field-data from Instagram. The results of the first experiment suggest that the polarizing nature of the communication source triggers defensive motivated reasoning among fans, even when the message being communicated is non-polarizing. This, in turn, has downstream consequences on post engagement and purchase intention. Analysis of 779 brandposts of Instagram influencers suggests that the polarization effect on post engagement is stronger for mega (vs. macro) influencers. By exploring the role of motivated reasoning, this research expands our understanding of the factors that drive consumers to engage with brand content on social media. The findings suggest that marketers can take advantage of the existing polarization among online users regarding polarizing influencers to enhance the effectiveness of their brand communication.
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