UNDERSTANDING CONSUMER ACCEPTANCE OF GENETICALLY MODIFIED FOODS IN CANADA: AN EXPLORATION OF THE INFLUENCE OF CULTURE ON CONSUMER PLANNED BEHAVIORS
Bibliographic record
Abstract
Genetically modified (GM) food is playing an increasingly important role in the global food supply chain but is still a controversial topic with consumers. This study aims to better understand consumer acceptance of GM foods and the influences of culture in Canada. More specifically, this paper investigates antecedents to consumer attitudes with respect to GM foods and how individualism and uncertainty avoidance might moderate the relationships between perceptions of risks and benefits, subjective norms, and purchase intentions. \nThe theoretical framework of this study is based on the Theory of Planned Behavior and Hofstede’s cultural dimensions theory. Specifically, attitude, subjective norm, and perceived behavioral control are proposed as three significant predictors of consumers’ purchase intention of GM foods. In addition, perceived personal benefits are hypothesized to have a stronger influence on attitude among consumers with a more individualist culture compared to consumers with a more collectivistic culture. In contrast, subjective norm is predicted to have stronger influence on purchase intention among consumers with more collectivistic culture. Moreover, perceived risks are hypothesized to have a stronger influence on attitude among consumers with higher scores on uncertainty avoidance.\nThis study employed a questionnaire-based consumer survey to collect quantitative information. The results indicate that consumer attitudes are influenced by perceived personal, social, and industry benefits, and risks. Further, consumers with high uncertainty avoidance place heavier emphasis on the risk factors. The integrated framework and findings of this study provide useful knowledge for both researchers and food marketers to better understand the influence of cultural values in shaping consumers’ attitude and purchase intention. The results have potential implications for Canadian food and agricultural companies with respect to creating more effective strategies to communicate with consumers from diverse cultural backgrounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".