E-commerce Widget for Nutrition and Sustainability

E-commerce Widget for Nutrition and Sustainability


In this project, we are investigating whether an e-commerce widget can be a promising approach to nudge people towards healthier and more sustainable food choices.

Obesity is one of the major global health challenges of the 21st century, as it threatens individual health and poses a great burden on health systems and society in general. Among the main risk factors for obesity are dietary aspects, such as poor food choices or unhealthy dietary patterns. Additionally, food consumption is a major contributor to individual greenhouse gas emissions, substantially influencing one’s ecological foodprint.

Even though many people have the aim to eat healthier, lose weight, and contribute to reducing greenhouse gas emissions with their food choices, very few succeed in doing so. Often individuals do not know how to interpret the information provided to them by food producers and retailers. Additionally, they are tempted by myopic food choices, immediately fulfilling their cravings without taking into account their negative long-term consequences.

We aim to assess whether digital interventions have the potential to improve individual food choices regarding their healthiness and sustainability. In particular, we focus on the effects of nutrition- (i.e. Nutri-Score) and sustainability-oriented (i.e. Beelong) food labels provided through web-based browser extensions that integrate into existing online grocery shopping websites. These labels directly visualize the environmental and/or health impact of food products to the consumer. In addition, the application developed tracks the consumers’ engagement with the website, leading to additional insights regarding the effects of food labels in online shopping environments.

Our experimental approach is based on randomized control trials in lab environments but also aims for field studies to get a more detailed view of real consumer behavior.

For this project, we join forces with an interdisciplinary team from the Auto-ID labs at ETH Zurich and at the University of St. Gallen.

Prof. Dr. Verena Tiefenbeck
Prof. Dr. Simon Mayer (St. Gallen)
Klaus Fuchs (Zurich)
Leonard Michels
Jie Lian (St. Gallen)