Communicating Per- and Polyfluoroalkyl Substances (PFAS) Contamination to the Public Through Personal Relevance

Abstract

It is important to understand people’s risk perception to identify effective pathways for risk communication about per- and polyfluoroalkyl substances (PFAS) because they present emerging environmental health risks. Guided by dual-process theories of information processing, this study focuses on personal relevance as a key variable that influences risk perception, systematic processing, and information seeking intention. Through an experimental survey, we found that participants in the high personal relevance condition (n = 497) were more likely to process information systematically compared to those in the low personal relevance condition (n = 486). Results also revealed that personal relevance influenced systematic processing through risk judgment and emotional response. Message-specific systematic processing was positively associated with information seeking intention. Lastly, trust in government and trust in science had different relationships with systematic processing, demonstrating the importance of distinguishing different types of institutional trust in future research.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10810730.2023.2183284

Notes

¹ A large proportion of online panel participants do not pay sufficient attention to research, which warrants attention check (Arevalo et al., 2022; Peer, Rothschild, Gordon, Evernden, & Damer, 2022). Therefore, the survey added an attention check question (“This is an attention check! Please select “strongly agree”) to allow us to identify participants who did not pay sufficient attention to the study.

² Exploratory factor analysis with principal component extraction and varimax rotation showed that these four items loaded on one factor (Kaiser-Meyer-Olkin criterion =.69, Bartlett’s χ² = 1871.99, p < .001).

³ None of the control variables was a significant predictor in the final model, so we removed these variables to improve the parsimony of the structural model.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.