Understanding the packaging colour on consumer perception of plant‐based hamburgers: A preliminary study
Bibliographic record
Abstract
Abstract Plant‐based hamburgers (PBH) are a recent alternative for consumers who want to reduce meat consumption. The marketing of this type of food has increased in the last few years, being an exciting niche in the food industry. In parallel, food marketing uses packaging to attract consumers, being packaging colour a key to improving consumer perceptions and increasing purchasing intentions. However, no study has explored the ideal packaging colour for PBH. Hence, this research aimed to understand the packaging colour on consumer perception of PBH. A study with 727 volunteers was carried out, and the data were obtained through an online form and analysed using an electronic spreadsheet. Firstly, the diet types and purchasing intention of BPH were analysed. In sequence, it was investigated the perceptions against different colours used in the package of PBH. Neutral (white and black), cool (green, blue and lilac), and warm (red, yellow and magenta) colours were used in the PHB package and data derived from consumers' perceptions were analysed using the word association methodology. Results from volunteers showed that people are interested in buying PBH, including people having a carnivore or flexitarian diet type (89% of volunteers). Important attributes related to PBH were induced in the volunteers using food packaging with green, yellow and white colours. These colours can attract attention and induce emotions related to nature, health, nutritional value and quality in packages of PBH.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".