Peers Are as Persuasive as Experts in Reducing Willingness to Pay for Sugary Foods

FRONTIERS IN NUTRITION. 2025.

Arzumanyan N., Shestakova A., Moiseeva V., Blank I. B., Klucharev V., Davidovich A., Ntoumanis I.

Non-communicable diseases are influenced by multiple genetic, physiological, environmental, and behavioral factors, with dietary sugar consumption representing one of the key modifiable risk determinants. Interventions aimed at reducing sugar intake often rely on persuasive health messaging by experts, yet it remains unclear whether the expertise of the narrator of the message is an indispensable component of a persuasive healthy eating call. To address this question, the present study directly compares the effect of different types of social endorsers on willingness to pay (WTP) for sugary food, including experts and peer endorsers. Eighty-eight healthy participants were randomly assigned to three groups: expert (N = 30), peer (N = 29), and multiple peers (N = 29). They evaluated their WTP for sugar-containing, sugar-free, and non-edible products before and after exposure to audio healthy eating interventions delivered by either a nutrition expert or university student peer(s). All audio interventions significantly decreased participants' WTP for sugar-containing products compared to sugar-free products. No significant differences were found between the effectiveness of peer and expert persuasion in reducing WTP for sugar-containing products. Peer-delivered healthy eating calls can be as effective as expert-delivered interventions in decreasing WTP for sugar-containing products. These findings highlight the potential of peer social influence in public health interventions in addition to expert calls.

Keywords: willingness to pay, social norms, expert persuasion, peer persuasion, healthy eating intervention, sugar consumption

Publication based on the results of: Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)