Nudging Toward Sustainable Food Consumption at University Canteens: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: This systematic literature review and meta-analysis investigated the effectiveness of the nudging approach toward sustainable food consumption in the university canteen context. METHODS: The systematic literature search was carried out in 5 databases, Web of Science, PubMed, Scopus, ProQuest, and the Royal Library, identifying 14 eligible studies and selecting 9 articles containing adequate information for meta-analysis. The nudging strategies were classified using the typology of interventions in the proximal physical microenvironments framework that resulted in 5 different intervention types: availability, position, size, presentation, and information that belonged to either intervention class-altering properties or placement. RESULTS: The study identified presentation, availability, and information as the most promising nudge intervention for achieving sustainable food consumption at the university canteen or similar settings. Nudging by altering the properties had a small effect size (d = 0.16), and nudging by altering placement showed a medium effect size (d = 0.21). DISCUSSION: Nudging interventions implemented after understanding consumers' current behavior showed positive effectiveness toward sustainable food consumption rather than implementing random nudges. CONCLUSIONS AND IMPLICATIONS: It is important that future studies aim to achieve sustainable food consumption by understanding canteen user food preferences and food choice motives before designing a nudging strategy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".