The Impact of Consumer Attitudes Towards Chocolate Products with Sustainability Labels on Buying Process
Bibliographic record
Abstract
The consumer attitudes towards different products have a significant impact on the decision whether to purchase the product or not. Over the last 20 years a lot of initiatives have started communicating sustainability-related information about food products to consumers. The sustainability labels (among the prominent ones are the Fair Trade labels) are increasingly appearing on chocolate products. The main objective of this labelling is to inform the consumer in a way that can promote sustainable consumption. This paper evaluates consumer attitudes towards chocolate products with sustainability labels and analyses the determinants of their willingness to purchase these products. Data were collected by means of an online survey implemented among students (aged between 19 and 35, total sample size of 72 respondents) in Slovenia. The majority of the respondents expressed positive attitudes towards sustainability issues. On the other hand, a quarter of respondents have never heard from chocolate products with sustainability labels. Statistical analysis revealed that respondents from urban areas, aged between 27 and 35, with higher income, healthy lifestyle and positive attitudes towards sustainability issues, are more willing to buy sustainability-labelled chocolate products. However, the results of the survey also indicate that sustainability labels at that time do not play a major role in consumers’ choices when deciding to buy a chocolate product.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".