Using a Modified Payment Card Survey to Measure Chinese Consumers’ Willingness to Pay for Fair Trade Coffee: Considering Starting Points
Bibliographic record
Abstract
Coffee consumption in China has increased rapidly in recent years. This study offers one of the few existing attempts to understand Chinese consumers’ coffee consumption behavior with a special focus on the viability of fair trade coffee in the Chinese market. A modified payment card approach was adopted to elicit consumer willingness to pay (WTP). Survey results suggest a positive attitude toward coffee and WTP for fair trade coffee. This study also explores the potential impact of starting point bias, which has been relatively well documented in the dichotomous choice literature, but less thoroughly in a payment card context. La consommation de café en Chine s’est accrue rapidement au cours des dernières années. La présente étude figure parmi les quelques tentatives déployées pour comprendre le comportement de consommation de café des consommateurs chinois et se penche sur la viabilité du cafééquitable sur le marché chinois. Nous avons utilisé une méthode modifiée de la carte de paiement pour déterminer le consentement à payer des consommateurs. Les résultats de notre étude montrent une attitude positive envers le café et un consentement à payer pour obtenir du cafééquitable. L’étude se penche également sur l’impact probable du biais de position initiale, qui est assez bien documenté dans la littérature sur le choix dichotomique, mais qui l’est moins dans le contexte de la carte de paiement.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".