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CO2e (best) avoided? How people experience CO2e avoided on the Too Good To Go app

Abstract:
Carbon Dioxide Equivalent (CO2e) avoided is increasingly communicated to individuals through digital media. Too Good To Go – a food waste app – presents users with a personalised CO2e avoided figure. Each time they collect food from a supermarket, café or restaurant their number increases. How do users experience CO2e avoided on the app? We explore this question through a longitudinal research project with 10 households in Oxfordshire, involving three interviews, five self-report surveys and a 28-day trial of the app. In general, participants did not see the figure as particularly valuable – best demonstrated in how little it influenced their usage of the app. This was largely due to issues of truthfulness. Each time a participant travelled and collected a bag, the CO2e avoided data point would increase by a fixed amount. This was at odds with the considerable variation in user experiences. Instead of CO2e avoided, participants put forward alternative impact data that was less individualised and more relatable. We use the concept of data experiences, as developed by Hoeyer et al. (2024), to think through the findings. In doing so, we provide empirical support to the utility of the four-part concept and put forward an additional cross-cutting theme of intensity. The paper also highlights how organisations cannot assume that certain metrics will influence peoples’ opinions and behaviours. To have an impact, their audiences’ data experiences need to be properly understood.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1177/20539517251367695

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Role:
Author
ORCID:
0000-0003-3228-6245
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Role:
Author
ORCID:
0000-0001-9414-7844
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-8226-4621
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Role:
Author
ORCID:
0000-0003-1020-2426


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Funder identifier:
https://doi.org/10.13039/501100000857
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Funder identifier:
https://doi.org/10.13039/501100000781


Publisher:
SAGE Publications
Journal:
Big Data and Society More from this journal
Volume:
12
Issue:
4
Article number:
20539517251367695
Publication date:
2025-12-02
DOI:
EISSN:
2053-9517
ISSN:
2053-9517


Language:
English
Keywords:
UUID:
uuid_60dff290-4079-41ce-976d-9845a321e595
Source identifiers:
3529261
Deposit date:
2025-12-03
ARK identifier:
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