Examining the Factors Influencing Consumers’ Purchasing Intention for Genetically Modified Agricultural Food
Bibliographic record
Abstract
The application of genetic engineering to food is becoming popular worldwide, especially in UK and USA. However, consumers in Asia remain unsure of the risks and benefits of genetically modified food. To measure the purchase intentions of Taiwanese and Vietnamese consumers using an integrated framework of the attitude model and the behavioral intention model, quantitative research was conducted in which the questionnaires were distributed to respondents living in Taiwan and in Vietnam. The results showed that the dependent variable of consumer attitude was positively impacted by three in-dependent variables including perceived risk to health, perceived benefit to health, and perceived benefit to the environment. The other construct showed no significant effect on consumers’ attitudes in the two countries. The consistency result in both countries between subjective norm and attitude revealed a positive impact on the purchase intention in descending order, while the variable of perceived behavioral control did not contribute to affecting purchase intention significantly. Finally, limitations and suggestions for future studies are also proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".