Do young consumers care about ethical consumption? Modelling Gen Z's purchase intention towards fair trade coffee
Bibliographic record
Abstract
Purpose A global shift in ethical/sustainable purchase drivers highlights Generation Z (persons aged 15–24) as an important market for producers and marketers. Although much research has touched on fair trade consumption, very little has focused on Gen Z's consumption patterns. This study provides insights into and implications of younger consumers' motivations in ethical/sustainable consumption. Design/methodology/approach This research examines Gen Z's purchase intention towards fair trade coffee with the theory of reasoned action framework. Data were collected with a convenience sample, and analyses were conducted using structural equation modelling. Findings The research found a significant influence of knowledge of fair trade towards product interest. Furthermore, general attitudes towards fair trade had a significant influence on product interest, product likeability and convenience. Lastly, product interest and subjective norms significantly influenced Gen Z's purchase intentions towards fair trade coffee. Originality/value Findings suggest that Gen Z's shift in ethical/sustainable consumption revolves around their subjective norms or peer influence circles and contributes to the notions of self-branding, identify claims and social currency. Younger generations are digital natives, and social media has created a looking glass into their actions. This digital expansion has created more opportunities for individuals to monitor the actions of others and release information in real-time. Therefore, ethical/sustainable consumption by Gen Z can be used as a communication tool among their peers to project personal values and ideological shifts and to influence others close to them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".