Cultural Dimensions and Individual’s Attitude Toward Cultured Meat
Bibliographic record
Abstract
The present study investigates how consumers make decisions regarding food, particularly in the context of novel foods where there are abundant uncertainties and unknown factors, focusing on the concept of cultured meat. With the growing global population and the associated concerns regarding resource scarcity and greenhouse gas emissions from the livestock sector, cultured meat offers a promising solution. However, consumer acceptance and the high production cost remain significant obstacles for the time being. To address these challenges, the study proposes a conceptual framework that combines multi-attribute theory and cultural dimensions to examine consumers' attitudes and preferences towards cultured meat based on their cultural orientation. By understanding the cultural influence on consumer behavior, this study aims to provide insights into marketing strategies for cultured meat. The study utilizes a mixed methodology, combining quantitative and qualitative approaches, to gather data from the English-speaking Canadian population. The findings reveal that individuals with a collectivistic mindset, a long-term orientation, and high uncertainty avoidance are more likely to have positive attitudes and higher willingness to try and purchase cultured meat. Furthermore, younger participants exhibit a higher liking for cultured meat compared to older participants. These results emphasize the importance of considering psychographic factors, demographic characteristics, and pricing strategies in promoting cultured meat as a sustainable alternative. Overall, this study contributes to the development of a more sustainable food system by examining the intersection of consumer behavior, cultural orientation, and sustainable food choices.
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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 | 0.001 | 0.001 |
| 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".