Why Consumers Don't see the Benefits of Genetically Modified Foods, and what Marketers can do about It
Bibliographic record
Abstract
Evidence from four studies suggests that the moral opposition toward genetically modified (GM) foods impedes the perception of their benefits, and critically, marketers can circumvent this moral opposition by employing subtle cues to position these products as being “man-made.” Specifically, if consumers view the GM food as man-made, and if they understand why it was created, moral opposition to the product diminishes, and the GM food's perceived benefits increase, which subsequently increases purchase intentions for the product. This effect is replicated in the field (in both controlled and naturalistic settings), in a laboratory experiment, and with an online consumer panel. The results suggest that marketers can help consumers better consider all information when assessing the merits of GM foods by using packaging and promotion strategies to cue consumers to view the GM food for what it is (i.e., a man-made object created with intent). The findings have implications for the recent GM food labeling debate.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".