Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts

Jianwei Huang, Mei Po Kwan, Zihan Kan, Minh Kieu, Jiyeong Lee, Tim Schwanen, Ikuho Yamada

Research output: Contribution to journalArticlepeer-review

Abstract

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

Original languageEnglish
Article number117247
JournalSocial Science and Medicine
Volume358
DOIs
StatePublished - Oct 2024

Keywords

  • COVID-19 control measures
  • Individual-level georeferenced data
  • Multi-cultural contexts
  • Network analysis
  • Privacy concerns

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