The effect of perceived carrier-ingredient fit on purchase intention of functional food moderated by nutrition knowledge and health claim
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine how the purchase intention of functional food is influenced by the perception of carrier-ingredient fit, that is, to what extent the carrier product and functional ingredient are intuitively perceived to be matched, and how such influence is moderated by consumers’ prior nutrition knowledge and provided health claim. Design/methodology/approach – Through two phases of experimental studies on 30 hypothetical functional foods, this paper analyzed the relationship between perceived carrier-ingredient fit and purchase intention which were reported by participants with different nutrition knowledge levels and in conditions that differed in the content of health claim. Findings – Phase 1 (n=62) found that the positive influence of perceived fit on purchase intention of functional products was moderated by one’s prior nutrition knowledge; compared to those knowledgeable in food/nutrition fields, consumers with less knowledge relied more heavily on the perceived carrier-ingredient fit when making purchase decision. The results of study 2 Phase 2 (n=93) revealed that the perceived fit was more important to predict purchase intention in the condition without health claim. A further analysis revealed that health claim increased the purchase intention particularly for functional foods receiving poor perceived carrier-ingredient fit. Practical implications – For innovative functional foods, the product development and market penetration may be benefit from fine-grained segmentation and positioning strategies that are based on the understanding of interaction between intuitive perception and cognitive knowledge. Originality/value – The present work highlights consumers’ perception of the carrier-perception fit, interacting with nutrition knowledge and health claim, as a critical factor determining the acceptance of functional foods.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".