Ethical chocolate consumption by millennials in the Netherlands
Bibliographic record
Abstract
The process of producing chocolate is an unsustainable practice. The cocoa industry contains several challenges related to living income, human rights and environmental protection. The root cause of these impacts is poverty of cocoa farmers. Ethically produced cocoa ensures the wellbeing of people and the environment within the production process. The Netherlands is one of the largest players in the cocoa industry, and has been making efforts to create a more sustainable cocoa practice. Around one quarter of the Dutch population consists of millennials, a group of people born between 1980-2000. This generation has a huge purchasing power and influence on the market, and characterises itself by their awareness regarding ethical issues, the environment and value to multiculturalism. The aim of this study is to explore the motivations of millennials living in the Netherlands to consume ethically produced chocolate. A survey was conducted on 189 millennials living in the Netherlands, of which 175 are fairtrade chocolate consumers. The motivations were divided into six categories: guilt, empathy, narcissism, self-actualisation, happiness, and future intention. The categories consisted of statements, which were answered on a five point Likert scale. The data were analysed via the partial least squares method (PLS). The results show a strong positive association between happiness and future intention. Guilt has a positive influence on empathy, and empathy impacts self-actualisation and happiness positively. Empathy has a negative association with future intention. Narcissism has a positive influence on self-actualisation, which in turn elicits happiness and future intention. The study shows a strong path from self-actualisation to happiness to future intention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".