Consumption and Purchasing Intent of Omega-3 Enriched Food Products by Lebanese Consumers
Bibliographic record
Abstract
In the modern nutrition patterns of many populations, consumers' intake of omega-3 oils does not reach the required levels. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) of the most potent omega-3 health benefits and commonly referred to as marine fatty acids, are not sufficiently consumed. This has led to numerous studies and attempts to produce foods enriched with omega-3 fatty acids. While applying fortification techniques, the most important challenge faced is to prevent the degradation of these fatty acids since both EPA and DHA are prone to oxidation. This study aims to predict the intention of Lebanese consumers towards the purchase of omega-3 enriched foods by applying the Theory of Planned Behavior Model. An online self-administered questionnaire was designed and conducted among Lebanese population. Results were collected throughout the month of August, 2021. Descriptive statistics, correlation and regression analyses were carried out using IBM SPSS Statistics 25. One hundred and eight (108) responses were received, 88% of which showed positive results on the intention of purchase of omega-3 enriched foods. Multiple linear regression analysis explained 30.4% of the variance in intention (p<0.001), and attitude and behavioral beliefs were the significant determinants of intention. One limitation though is faced within the Lebanese consumers is their economic status; but in general, respondents show willingness to spend money and time to purchase such fortified food provided that promoters should emphasize on the multiple health benefits of such products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".