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Record W4405160556 · doi:10.1016/j.clrc.2024.100241

Insights from consumers' exposure to environmental nutrition information on a dashboard for improving sustainable healthy food choices

2024· article· en· W4405160556 on OpenAlexfundno aff
Prince Agyemang, Ebenezer Miezah Kwofie, Jamie Baum, Dongyi Wang

Bibliographic record

VenueCleaner and Responsible Consumption · 2024
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersMcGill UniversityDanone Institute North America
KeywordsDashboardHealthy foodFood choiceBusinessSustainable agricultureEnvironmental healthSustainabilityMarketingMedicineFood scienceComputer scienceData scienceEcology

Abstract

fetched live from OpenAlex

Over the last decade, there has been a growing demand for tools to support sustainable healthy lifestyles, including food choices. Through a survey, this study examined the influence of environmental nutrition information conveyed with aids such as nudges and traffic light labels through a Dashboard for Improving Sustainable Healthy (DISH v1.0) food choices on consumer purchase intentions. DISH is an application that enables end-users to envisage and compare the potential impacts of their choices before purchasing. In the early stage of the technological development of DISH, the environmental nutrition information of two fast-food menus, plant-based and animal-based burgers, was tested among 112 respondents from a university campus. The results suggested that with an environmental nutrition score, less cognitive processing was required to make sustainable healthy choices. Among the 90.2% of respondents with a predisposed purchase intention for animal-based burgers, 56.9% reported a purchasing intent for plant-based burgers. More than 83% attributed their decision to the environmental nutrition information provided on DISH. 64.3% of respondents rated DISH as 4 stars or 5 stars, suggesting the perceived usefulness of the application. A statistical investigation of the results indicated that features of the DISH application, nudges, and awareness considerably influenced sustainable choices (sig<0.001). The results support digital innovations as essential drivers for reinforcing environmental nutrition messages and stimulating subtle dietary changes. These preliminary results have served as a precursor for ongoing studies on other university campuses and corporate institutions testing the long-term impact of DISH v2.0 in stimulating dietary change. • DISH maps out selected meals' health, nutritional, and environmental impacts. • Consumer exposure to environmental nutrition information influenced purchase intentions. • 56.9% of respondents predisposed to animal-based burgers changed their purchase intentions. • 83% of respondents attributed features of DISH to influence their purchase intention. • 64.3% of respondents rated DISH 4-star and 5-star.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.264
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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