Purchase Intention of Organic Food Products among Generation Y in Malaysia: A Quantitative Study
Bibliographic record
Abstract
Farming of crops, cattle, and fish are all a part of the food industry, which is essential to the expansion of a country's economy. The food business classifies the food manufacturing process as including both conventional or traditional food and organic food. Producing organic food is growing in popularity as consumers all over the world become more aware of the advantages of leading a healthier lifestyle. The organic food industry in the US is currently the biggest and has the highest sales when compared to other food categories. Similarly, the Malaysian government has supported and encouraged the production of organic food since the late 1980s. Malaysia's research into organic food products is still in its early stages when compared to those of other countries. The Malaysian organic food business is actually understudied right now, with concentration narrowed to a few states and segments. The purpose of this study is to determine Generation Y's intentions to buy organic food products throughout all fourteen states in Malaysia. The relationship between financial and non-financial elements and Generation Y's purchase aspirations is also examined in this study. This study is organised based on the Theory of Reasoned Action (TRA). This study is mostly positivist and employs a quantitative research design. 390 participants from Malaysian public and private universities were chosen for the study using judgemental and snowball sampling techniques. The data were analysed using multiple regression. The results of the study shows that both financial and non-financial factors have a significant influence on people's intentions to purchase organic food. The results even more strongly support the TRA. For businesses, marketers, and food producers, the research findings are helpful since they expand the body of knowledge on purchase intention. The stakeholders would then be able to target, draw in, and fulfil the needs of Generation Y consumers by using efficient marketing strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".