Branding, Ingredients and Nutrition Information: Consumer Liking of a Healthier Snack
Bibliographic record
Abstract
Taste appeal, sustainable ingredients and valid health claims are challenges for successful marketing of healthier food products. This study was designed to compare the effects of branding, ingredients and nutrition information on consumer liking towards a prototype of the Nothing Else healthier snack bar with the top three brands of New Zealand snack bars, and another product with a good nutrient profiling score. Sixty-four consumers were recruited to evaluate the five snack bars. Participants initially blind-rated on visual analogue scales their liking scores in relation to colour, taste, flavour, texture and overall liking. Packaging for the products was then presented alongside each of the five products and participants rated their liking scores for a second time. Participants also ranked the five products from 1 to 5 for healthiness, taste, naturalness, and purchase intent if prices were the same. In both blind and informed tests, the Nothing Else bar was the least liked snack bar among all the tested samples. However, after the packaging for the products was presented, overall liking of the Nothing Else bar increased by 14% (p = 0.023), while overall liking for the four commercial products were unchanged. While the most popular commercial bar was ranked the highest for taste and purchase intent, the Nothing Else bar was ranked the highest for the healthiness and naturalness. Our findings confirmed that the branding and health related nutrition information could improve consumer liking and brand perception particularly if backed by marketing.
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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 | 0.003 |
| 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".