Bibliographic record
Abstract
Purpose Functional foods, also known controversially as “phoods,” are perceived by many as the food industry's response to consumers' increasing desire to make healthier eating choices. The objective of the present study is to determine the influence of the production technology used to make functional foods on the perceived health value of functional foods. Design/methodology/approach To meet the objectives of the study, the paper employs an exploratory study with six conditions. The two factors addressed were the added nutrient (lycopene and beta‐carotene) and the degree of production technology (low, medium, and high). Lycopene and beta‐carotene were both added to two functional foods with different health features, which in this study were orange juice and apple pie. The use of this latter factor supposed that the level “low” implied a product which was improved by adding a food that naturally contained a nutrient, the level “medium” implied that the nutrient was added in the laboratory, and the level “high” refers to an ingredient whose genetic code had been modified in order to introduce the gene producing the nutrient. In order to reduce the effect of the order of presentation of the technology levels, the sequence of levels was randomized. Findings The results show that perceived health benefits and intention to purchase are not so much influenced by what we pose as graduated stages of production technologies as by a perceived dichotomy between natural and artificial foods. The results also show the extensive mediating effect of perceived risks and benefits on the relationship between experimental conditions, perceived health benefits, and intent to purchase. The results also reveal that pre‐purchase intentions of functional foods are more noteworthy for orange juice, which has a usefulness valence, than for apple pie, which has a less healthy epicurean valence. Originality/value This study has various strengths, including a novel intervention that addressed a timely topic for which few data are currently available. The sale of functional foods is a complex practice. This exploratory study took a few steps toward understanding how health benefits of functional foods are perceived and how these perceptions can be better understood by food manufacturers and consumers in today's society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.143 | 0.057 |
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".