Consumer willingness to pay for traceable food products: a scoping review
Bibliographic record
Abstract
Purpose Traceability is an increasingly important tool for reducing food safety risks and managing supply logistics. Given the costs of implementing and maintaining traceability systems, it is crucial to understand consumer willingness to pay (WTP) for traceable products. Design/methodology/approach The authors conducted a scoping review to collate the existing literature on consumer WTP for traceability in food products to determine the nature of the evidence base and to identify research gaps. Findings A total of 77 articles were included in the review. The number of studies published per year generally increased over the review period, and China and the United States were the most common countries in which studies were conducted (43.6 and 14.1% of total studies, respectively). All but one of the studies investigated at least one factor that might influence consumer WTP for traceability, the most common of which was socio-demographic characteristics (72.7%). Three-quarters of studies used hypothetical methods to elicit WTP values (75.3%), whereas one-quarter used non-hypothetical methods (24.7%). Most studies included some measure of preference heterogeneity (83.1%). Research limitations/implications There is some potential for systematic bias in the evidence due to the predominance of studies from only a few countries and the possible presence of hypothetical bias. These potential biases could be corrected through future research. Originality/value To the authors’ knowledge, no previous study systematically and comprehensively identifies and summarizes the evidence base on consumer WTP for traceable food products.
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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 | 0.023 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".