Exploring Consumer Behavior towards Social Impact Apps for Food Waste Reduction
Bibliographic record
Abstract
Food waste is a significant global challenge, with social and environmental implications that demand innovative solutions. Apps like Too Good To Go offer a technological approach to mitigating food waste by connecting consumers with surplus food from local businesses at discounted prices. This study examines consumer behavior toward such apps, focusing on their attitudes, motivations, and barriers to adoption. Using a survey distributed to current users, potential users, and non-users, data were collected on demographics, usage patterns, perceptions, and challenges. Descriptive analysis, behavioral segmentation, and statistical testing revealed several main motivators—such as cost savings, environmental awareness, and convenience—that drive engagement with these apps. We also identified generally positive attitudes toward the technology’s potential to reduce food waste, though notable barriers persist, including skepticism about surplus food quality and app usability. Building on these insights, our results show that consumers prioritize substantial discounts of 40% or more and clear indication of food freshness when deciding to adopt and consistently use food waste reduction apps. Additionally, low interest in features associated with loyalty programs and wide variety of dietary options, allow us to save on app development costs and shorten time-to-market. Our findings also allow us to conduct a more targeted marketing campaign, focusing on motivational drivers like convenience, instead of a more generic message.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".