Consumer Trust in Chicken Brands: A Structural Equation Model
Bibliographic record
Abstract
The issues of what drives consumers to trust the food industry in general and food brands specifically, and the outcomes of consumer trust, are increasingly of interest to food industry stakeholders and to policy makers. The extant knowledge of trust in food brands, and in particular the empirical investigation of brand trust, is relatively underresearched in food economics. Research examining institutional trust has been carried out in sociology, marketing, and political science, while a limited number of studies to date have investigated the degree of consumer trust in food. This paper extends previous research on consumer trust in the context of food by developing a conceptual framework based on insights from the trust literature that explores the drivers and outcomes of consumer confidence in food quality and food safety. The conceptual framework is tested with a Structural Equation Model using survey data from a sample of Canadian consumers of fresh chicken. Model results indicate that both trust in the food industry (i.e., food companies and food retailers) and brand trust bolster consumer confidence in credence attributes. Furthermore, personal traits, such as risk aversion and ethically motivated behavior, moderate the relationship between brand trust and consumer confidence in credence qualities.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".