Chinese consumers' preferences for imported beef products
Bibliographic record
Abstract
ABSTRACT China's rapidly growing food market offers opportunities for foreign beef producers, thanks to its sizable population and increasing income levels. This study examined Chinese consumers' preferences and willingness to pay (WTP) for credence attributes of beef from the domestic market and other large exporting countries. Three beef cuts were considered: steak, brisket, and tongue. Data were collected from an online survey incorporating choice experiments (CEs) of 2016 consumers from China in 2021. Each respondent was presented with three beef alternatives that differed in price, country of origin, food safety, and production certifications, and also included a “no purchase” option. Chinese consumers' beef selections in the CEs were analyzed using a mixed logit model in WTP space. Results indicate that the type of cut does not influence Chinese consumers' evaluation of country of origin and credence attributes. Moreover, results show that Chinese consumers strongly prefer and are willing to pay more for domestic beef than imported beef. Beef from New Zealand had the highest WTP value among all the exporting countries, followed by Argentina, Australia, Canada, Uruguay, Brazil, and the United States. Also, enhanced food safety, Organic, and Green Food certifications had positive WTP values. The findings of this study offer evidence that Chinese consumers prefer safe and quality‐assured beef products. This information can be used by beef producers targeting the Chinese market to design production and marketing strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".