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dc.contributor.authorPanzone, Luca
dc.contributor.authorSniehotta, Falko
dc.contributor.authorComber, Rob
dc.contributor.authorLemke, Fred
dc.date.accessioned2020-09-14T03:48:55Z
dc.date.available2020-09-14T03:48:55Z
dc.date.issued2020en_US
dc.identifier.issn0195-6663
dc.identifier.doi10.1016/j.appet.2020.104794
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6543
dc.description.abstractFood consumption decisions require consumers to evaluate the characteristics of products. However, the literature has given limited attention to how consumers determine the impact of food on health (e.g., kilocalories) and on the environment (e.g., carbon footprint). In this exercise, 1511 consumers categorised 43 food products as healthy/unhealthy and good/bad for the environment, and estimated their kilocalories and carbon footprint, which were known to the investigator. The task was performed either with no stimuli (a control group), under time pressure only, with traffic-light labels only, or both. Results show that traffic-light labels: 1) operate through improvements in knowledge, rather than facilitating information processing under pressure; 2) improve the ability to rank products by both kilocalories and carbon footprint, rather than the ability to use the metric; 3) reduce the threshold used to categorise products as unhealthy/bad for the environment, whilst raising the threshold used to classify products as good for the environment (but not healthy). Notably, traffic-light increase accuracy by reducing the response compression of the metric scale. The benefits of labels are particularly evident for carbon footprint. Overall, these results indicate that consumers struggle to estimate numerical information, and labels are crucial to ensure consumers make sustainable decisions, particularly for unfamiliar metrics like carbon footprint.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectNumerical Assessmentsen_US
dc.subjectSustainable Dietsen_US
dc.subjectCarbon Footprinten_US
dc.subjectKilocaloriesen_US
dc.subjectThreshold Analysisen_US
dc.subjectMulti-level Modellingen_US
dc.titleThe effect of traffic-light labels and time pressure on estimating kilocalories and carbon footprint of fooden_US
dc.identifier.journalAppetiteen_US
dc.source.volume155en_US
dc.source.issueDecemberen_US
dc.contributor.departmentSchool of Natural and Environmental Science, Newcastle University, UKen_US
dc.contributor.departmentFaculty of Behavioural, Management and Social sciences, University of Twente, The Netherlandsen_US
dc.contributor.departmentNIHR Policy Research Unit Behavioural Science, Newcastle University, UKen_US
dc.contributor.departmentDepartment of Human Centred Technology, KTH Royal Institute of Technology, Stockholm, Swedenen_US
vlerick.knowledgedomainMarketing & Salesen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentMKTen_US
dc.identifier.vperid186039en_US


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