JOURNAL ARTICLE

Hostile media perceptions and consumption of genetically modified and organic foods: Examining the mediating role of risk‐benefit assessments.

  • Published In: Risk Analysis: An International Journal, 2023, v. 43, n. 8. P. 1587 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Wang, Sai 3 of 3

Abstract

Drawing upon the hostile media effect, this study examined how perceived media bias in covering genetically modified (GM) food influences individuals' risk–benefit assessments of it and their food consumption behaviors. The results of a nationally representative survey (N = 1364) showed that individuals seeing media coverage as more biased in favor of GM food perceived it as more hazardous, which was related to a higher proportion of organic food consumption in their diets. In contrast, perceived media coverage as less slanted toward GM food was associated with more benefit perceptions of it, thereby predicting its higher proportion in individuals' diets. More importantly, the indirect effect of perceived media bias on GM food consumption through benefit perceptions was more pronounced among males than females. The findings of this study not only provide empirical evidence of the perceptual and behavioral outcomes of hostile media perceptions, but also offer valuable insights for journalists and education practitioners to improve public understanding of emerging food technologies. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Risk Analysis: An International Journal. 2023/08, Vol. 43, Issue 8, p1587
  • Document Type:Article
  • Subject Area:Applied Sciences
  • Publication Date:2023
  • ISSN:0272-4332
  • DOI:10.1111/risa.14054
  • Accession Number:169873448
  • Copyright Statement:Copyright of Risk Analysis: An International Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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