Using consumption and reward simulations to create desire for plant-based foods

The production of animal-based foods has a significant, negative impact on the environment. Compared to plant-based protein sources, such as beans and lentils, the production of beef and other red meat requires 20 times more land and emits 20 times more greenhouse gas emissions per unit of edible protein. To curb climate change and sustain the planet, consumers urgently need to shift towards more plant-based diets. Most food choices, however, are driven by expectations of taste, rather than sustainability, and plant-based foods are perceived as less tasty than meat-based foods. How can these perceptions be changed to promote sustainable, plant-based food choices?

This project proposes that desire for plant-based foods can be enhanced by food descriptions that emphasise sensory features, eating context, and hedonic consequences. Such descriptions will trigger spontaneous simulations (i.e., re-experiences) of eating and enjoying a food, and therefore create rewarding expectancies and desire. Supporting this account, previous work has shown that people describe attractive foods in terms of sensory experiences, eating context, and hedonic enjoyment. Such descriptions, in turn, are associated with increased desire for a food. This suggests that people spontaneously simulate (re-experience) eating attractive food, and that this contributes to the desire to eat. In addition, our pilot work has shown that adding such simulation features to labels for plant-based foods increases attractiveness of these food. Here, we will build on these preliminary findings and develop and test a systematic strategy for labeling plant-based foods such that they trigger desire.

In Work Package 1, we will systematically establish the simulation features that induce desire for a large number of existing dishes. In a task adapted from cognitive psychology, participants will list features of a large number of foods. We will ask different groups of participants to focus on specific kinds of features (e.g., sensory features, eating context, etc). We will assemble these features and their links with attractiveness ratings in a comprehensive data-base that we will make openly available and disseminate among stakeholders in the food industry.

In Work Package 2, we will use this simulation feature database to create desire-inducing labels for plant-based foods. Based on ratings by plant-based food consumers and mainstream consumers, we will select features that fit a number of relatively novel plant-based foods. These foods will be selected in collaboration with our partner, the Better Buying Lab, who have a strong network of industry partners actively engaged in the shift toward plant-based diets. Then, we will use these features to create eating simulation food labels, and conduct experiments to test whether such labels increase desire by assessing eating simulations, desire, salivation, and purchase intentions, compared to control labels that don't include eating simulation features.

Finally, Work Package 3 will examine whether eating simulation labels affect actual liking, and if they increase purchase intentions measured after consumption only if the product lives up to the expectations created by the label. Here, consumers will taste a plant-based food that is presented with an eating simulation label or a control label, and will report liking and purchase intentions. Half of consumers receive a tasty version of the product, and half of consumers receive an adapted, bland version. Assessing desire before and after tasting the food will allow us to examine the interplay between simulations and experiences, which will be informative for the psychology of desire more generally.

Together, these studies will establish a detailed understanding of the role of simulations in desire for food, and show how simulation-inducing language can be used to boost desire. This will help to make the urgent the dietary shift needed to sustain the planet.

Animal-based foods have a significant, negative impact on the environment. Compared to plant-based protein sources, the production of beef and other red meat requires 20 times more land and emits 20 times more greenhouse gas emissions per unit of edible protein. To sustain the planet, we need "huge and immediate changes" in our diet, including a 90% reduction in beef consumption. This can only be achieved if consumers make more plant-based food choices. To facilitate this, food businesses need to use language about plant-based foods that effectively creates desire. Indeed, recent research has shown that food industry consider language the most feasible and effective tool to promote sustainable food choices. Thus, our main goal is to communicate our research findings on this issue to stakeholders food retail, food service, and food manufacturing.

Our Impact activities are a collaboration with the Better Buying Lab (BBL), a unit of the World Resources Institute, a world-leading sustainability think-tank (www.wri.org). Together, we have developed a detailed strategy to support stakeholders in designing food labels that make plant-based choices consistent with the short-term motivation to consume attractive food. This will create increased consumer demand for sustainable foods, which will in turn motivate food businesses to keep developing new plant-based foods. Thus, making research findings easy for industry to understand and act upon is a key strategy to enable change both at the consumer and at the industry level.
In addition, however, it is important that consumers become aware of the major role that reducing meat consumption can play in curbing climate change. This will encourage them to make plant-based choices independent of desire-inducing labelling. Indeed, consumers underestimate the environmental impact of meat consumption (Hartmann & Siegrist, 2017) and the impact that their personal choices for meat reduction can have (Macdiarmid, Douglas, & Campbell, 2016). Therefore, our impact strategy also involves knowledge exchange events to reach consumers.

Impact Tools
1) We will create a Shiny App to share our food features database. A Shiny App is an interactive web page that is built in R, the same programming environment that we use for our statistical analyses. Building a Shiny App is therefore a natural way of providing industry users access to our food features database. This will allow users to easily search for simulation features associated with, for example, specific ingredients, types of dishes, or eating situations. We will create the Shiny App at the end of Work Package 1, when all food features, their valence data, and their associations with attractiveness ratings of foods are available, and update it throughout the project.
2) We will communicate our research findings and publicise the Shiny App to key industry users through the BBLs carefully curated approach to share scientific insights with the right audiences. The BBL team have extensive experience in communicating research findings to commercial food organisations, marketing and advertising, and taking insights from behavioural science to commercial settings. They will reach current BBL members as well as further organisations in the commercial food sector through targeted publications, speaking opportunities at industry conferences and meetings, and blogposts that have a readership of ca. 100,000 per month.
3) We will engage with consumers through interactive knowledge exchange events at the Glasgow Science Festival, the ESRC Festival of Social Science, and at the Glasgow Science Centre. These activities will help to increase awareness of the climate impact of food choices, provide information on sustainable food choices, and support dietary change through behavioural tips for making convenient, plant-based choices in different eating situations. Details of the planned events can be found in our Pathways to Impact statement