The Determinants of Consumer Confidence in Credence Attributes:Trust in the Food System and in Brands
Bibliographic record
Abstract
Given the credence nature of food quality and food safety attributes, consumers rely on abstract systems of regulation as well as quality signals such as brands to make informed choices. Motivated by the need to further investigate what influences consumer confidence in credence attributes, this paper develops a conceptual framework in which trust in the food system (i.e. government, farmers, manufacturers, and retailers) and brand trust are posited to influence public confidence in credence attributes. The proposition is tested using Structural Equation Modeling techniques based on survey data from a sample of Canadian consumers of fresh chicken meat and of packaged green salad. Survey results indicate that while both trust in the food system and brand trust are positively associated with consumer confidence in credence attributes, the influence of system trust on public confidence is more pronounced than the effect of trust in individual food brands. The effect of brand trust also appears to vary across product categories. The paper offers insights into the use of SEM to model the complexity underlying the determinants and outcomes of trust within food networks.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".