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Choice architecture: nudging food choice behaviour

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  • Full or part time
    Dr H Ensaff
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Choice architecture, the presentation and framing of a choice and its influence on subsequent decision-making behaviour, is well established. Within public health, there is growing impetus behind using choice architecture to ‘nudge’ behaviour, thereby avoiding strong-armed regulation. Ultimately, appropriate interventions that emphasise the role of nudge strategies and shift individuals’ food choice behaviour towards better choices will reap benefits in terms of the population’s health and wellbeing. The role of choice architecture in promoting better food choice shows promise. However further research is needed, e.g. in understanding unintended consequences and the relative effectiveness of different nudge strategies.

This PhD will focus on food choice architecture (all aspects of how a food choice is framed, including the relative availability, positioning and presentation) and will investigate nudge strategies to shift food choice and dietary patterns. The work will examine nudge theory and its potential to influence subsequent food selections, through the development of a theory driven public health intervention in a real world setting.

The research will adopt a mixed methods approach, and the quantitative element will entail the development and subsequent statistical analysis of large datasets of food choice. The qualitative element will comprise data collection (observation, interviews and focus group discussions) and qualitative analysis to explore food choice parameters, and individuals’ perceptions and perspectives on being nudged.

Funding Notes

This project is open to self-financing students and may be eligible for funding through University or external research bodies.

How good is research at University of Leeds in Agriculture, Veterinary and Food Science?

FTE Category A staff submitted: 14.40

Research output data provided by the Research Excellence Framework (REF)

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