Impact of corporate social responsibility claims on consumer food choice
Bibliographic record
Abstract
Purpose The study seeks to assess the impact of two different corporate social responsibility (CSR) claims, relating to social and environmental dimensions, on consumers' wine choice across international markets. It analyses how point of purchase CSR claims compete with other food claims and their awareness, penetration and consumers' trust are examined. Design/methodology/approach A discrete choice experiment with a visual shelf simulation was used to elicit consumer preferences and to estimate marginal willingness to pay for CSR and other food claims across the UK, France, Germany, the US East Coast, the US Midwest, and Anglophone and Francophone Canada. Findings CSR claims relating to social and environmental responsibility have a similar awareness, penetration and consumer trust, but differ in their impact on consumer choice, where environmental corporate responsibility claims benefit from a higher marginal willingness to pay. Consumer valuation of CSR claims significantly differs across international markets, but is consistently lower than for organic claims. Originality/value This is the first cross‐national study that analyses the impact of CSR claims on consumer food choice relative to other food claims using large representative consumer samples. The strength of the paper also pertains to the utilisation of innovative choice experiments covering a large range of choice relevant product attributes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".