Consumer Purchase Intentions Regarding Fair-Trade Coffee and the Role of Virtual Reality: an Exploratory Study
Bibliographic record
Abstract
Fair-trade is an alternative approach to trading that has a goal of sustainable development and creating a better opportunity for producers in third world countries. Fair-trade coffee represents the largest category under this umbrella. Globally, Generation Z’s consumption and adoption of ethical/sustainable products creates new challenges and opportunities for producers and marketers. Virtual reality has seen to educate, market, and create value with its media richness, presence, interactive, and immersive qualities. As a result, VR has positioned itself to be a very strong communication tool for social scientists and marketers to add value, communicate effective messages, and impact consumer behaviour. This research examines consumer purchase intentions regarding Fair-trade coffee through the lenses of the Theory of Reasoned Action and the exploratory effect of virtual reality in the context of Generation Z. A first analysis will allow me to outline, present, and test a model regarding Fair-trade coffee consumption in the scope of the Theory of Reasoned Action. Additionally, a second analysis will leverage VR in an exploratory manner to see if this has an effect on the dimensions outlined in the model. The dimensions presented to impact Fair-trade coffee purchase intentions are personal values, knowledge of Fair-trade, general attitudes towards purchasing Fair-trade coffee, and subjective norms. By analyzing data from 314 respondents, this study found that competence, knowledge of Fair-trade, skepticism, and concern are significant predictors of product interest and likeability. Knowledge of Fair-trade and Skepticism are significant predictors of price acceptability. Furthermore, subjective norms are a significant predictor of purchase intentions. Lastly, virtual reality was found to have an inconclusive effect on the dimensions outlined in the conceptual model.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".