Explaining consumer willingness to pay for country‐of‐origin labeling with ethnocentrism, country image, and product image: Examples from China's beef market
Bibliographic record
Abstract
Abstract Chinese beef imports have been increasing in recent years. At the same time, Chinese public sentiment toward foreign countries, including those who export beef to China, has been changing. Therefore, this research uses a discrete choice experiment (DCE) to investigate the role of ethnocentrism, country image, and product image on consumer willingness to pay for country‐of‐origin labeled beef. Results indicate, on average, Chinese consumers prefer domestic beef most, and value beef from Australia and the US somewhat similarly. Their willingness to pay varies based on their perceived image of the country that the beef originates from and based on their perception of the safety of the beef, that is, product image. The more ethnocentric consumers are, the more they prefer domestic beef and discount foreign beef. Importantly, the effects of ethnocentrism, country image, and product image are stronger on the willingness to pay for domestic beef than for imported beef. More generally, findings indicate that controlling for ethnocentrism, country image, and product image contributes to understanding consumer willingness to pay for products originating from foreign countries. Overall, findings suggest that stronger ethnocentric tendencies lead to lower willingness to pay for imported beef (with some exceptions), and positive country image and product image increase the willingness to pay for imported beef. Thus, it is important to consider these constructs when estimating consumer willingness to pay for imported products, especially for countries where public sentiment toward exporting countries may be prone to change in a dynamic global environment.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".