Eliciting Consumer Preferences for Certified Animal‐Friendly Foods: Can Elements of the Theory of Planned Behavior Improve Choice Experiment Analysis?
Bibliographic record
Abstract
ABSTRACT Models used in neoclassical economics assume human behavior to be purely rational. On the other hand, models adopted in social and behavioral psychology are founded on the “black box” of human cognition. In view of these observations, this paper aims at bridging this gap by introducing psychological constructs in the well‐established microeconomic framework of choice behavior based on random utility theory. In particular, it combines constructs developed employing Ajzen's theory of planned behavior with Lancaster's theory of consumer demand for product characteristics to explain stated preferences over certified animal‐friendly foods (AFF). To reach this objective, a Web survey was administered in the largest five EU‐25 countries: France, Germany, Italy, Spain, and the United Kingdom. Findings identify some salient cross‐cultural differences between northern and southern Europe and suggest that psychological constructs developed using the Ajzen model are useful in explaining heterogeneity of preferences. Implications for policymakers and marketers involved with certified AFF are discussed.
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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 | 0.036 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".