Login / Signup

The impact of COVID-19 on consumer evaluation of authentic advertising messages.

Jooyoung ParkJungkeun KimDaniel C LeeSeongseop S KimBenjamin G VoyerChangju KimBilly SungHector Gonzalez-JimenezFernando FastosoYung K ChoiSukki Yoon
Published in: Psychology & marketing (2021)
This study investigates the relationship between the COVID-19 threat and consumer evaluation of a product with authenticity appeals in advertisements. We propose that threatening situations like COVID-19 motivate consumers to lower their uncertainty and increase their preference for products with authentic advertising messages. Because individuals react differently to threatening environments according to their early-life experiences, commonly reflected in childhood socioeconomic status, we examined whether childhood socioeconomic status moderates the relationship between threat and consumer evaluation of authenticity in advertisements. First, secondary data from Google Trends provided empirical support for our predictions. In additional experimental studies, participants evaluated different target products in four studies that either manipulated (Studies 2 and 3) or measured (Studies 4 and 5) COVID-19 threat. Our results provide converging evidence that consumers positively evaluate products with authentic advertising messages under the COVID-19 threat. Consumers' motivation to lower their uncertainty underlies the effect of COVID-19 threat on their evaluation of authentic messages (Study 3). This attempt to reduce uncertainty is more likely to occur for consumers with relatively higher childhood socioeconomic status (Studies 4 and 5). These findings suggest that using authenticity appeals during a pandemic could effectively reduce consumers' perceived uncertainty and generate positive consumer evaluations.
Keyphrases
  • coronavirus disease
  • sars cov
  • early life
  • respiratory syndrome coronavirus
  • case control
  • health information
  • mental health
  • depressive symptoms
  • physical activity
  • childhood cancer
  • deep learning