The role of causal models and beliefs in interpreting health claims
2018
Objective: Health claims on food packaging are regulated to inform and protect consumers, however many consumers do not accurately interpret the meaning of the claims. Whilst research has shown different types of misinterpretation, it is not clear how those interpretations are formed. The aim of this study is to elicit the causal beliefs and causal models about food and health held by consumers, i.e. their understanding of the causal relationships between nutrients, health outcomes and the causal pathways connecting them, and investigate how well this knowledge explains the variation in inferences they draw about health benefits from health claims. Method: 400 participants from Germany, the Netherlands, Spain, Slovenia, and the UK were presented with 7 authorised health claims and drew inferences about the health benefits of consuming nutrients specified in the claim. Then their personal causal models of health were elicited along with their belief in the truth and familiarity with the claims. Results: The strength of inferences about health benefits that participants drew from the claims were predicted independently by the strength of the relevant causal pathways within the causal model, and belief in the truth of the claim, but not familiarity with the claim. Participants drew inferences about overall health benefits of the nutrients by extrapolating from their causal models of health. Conclusion: Consumers’ interpretation of claims is associated with their belief in the claim and their causal models of health. This prior knowledge is used to interpret the claim and draw inferences about overall health benefits that go beyond the information in the claim. Therefore efforts to improve consumers’ understanding and interpretation of health claims must address both their wider causal models of health and their knowledge of specific claims.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
22
References
6
Citations
NaN
KQI