Understanding Consumers’ Attitudes Toward Fruits and Vegetable Attributes: A Multi-Method Approach
Bibliographic record
Abstract
Background: Results from previous work indicated that when consumers make purchasing decisions, they pay more attention to freshness, taste and hygiene attributes of fruits and vegetables than price and nutritional value, when these attributes are considered individually. Methods: To shed light on the underlying factors that shape the pattern of reported preferences, researchers used five doubly censored Tobit models to analyze data generated from a fuzzy pairwise comparison model (FPC) to explain the pattern of reported preferences. In the model, nutritive value, hygiene, taste, price and freshness were separately regressed on a number of demographic and personal characteristics variables. For this study, a random sample was drawn proportionate to population size by county in Georgia, North Carolina and South Carolina. Data were collected from 412 respondents. Results: Higher levels of education and income did not affect how consumers rate the nutritional value of fruits and vegetables. This relative lack of difference among consumers as classified in the model, along with results that showed consumers giving a higher preference rating to hygiene, taste and price offer support for the notion that the nutritional value attribute plays a subsidiary role in consumers purchasing decisions. Conclusion: The multi-method approach used in this study provides information on the demographic characteristics of consumers that influence attitudes and behaviors toward fruit and vegetable attributes. Nutrition educators and marketers will be able to use this knowledge about consumers’ attitudes and behaviors to customize programs that more accurately address consumers’ preferences.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".