Food Technology Neophobia Scales in cross‐national context: Consumers’ acceptance of food technologies between Chinese and New Zealand
Bibliographic record
Abstract
An increasing number of novel food technologies have been developed to meet consumers' growing desire for safe and high-quality foods. However, consumers can be cautious of novel food technologies, and their acceptance cannot be guaranteed. Food Technology Neophobia Scales (FTNS) have been proven to be an effective tool to predict consumers' behavior toward novel food technologies in a range of individual countries, but not for cross-national contexts. To fill the gap, this study designed a survey involving 604 Chinese and 614 New Zealand respondents, investigating the influence of consumers' food technology neophobia (FTN) on their acceptance of food technologies. Chinese respondents' FTNS score (50.62) was lower than that of New Zealand respondents (55.02), which was in line with the finding that Chinese respondents' acceptance of all tested food technologies was higher than New Zealand respondents (0.34-0.86 in a Likert-7 scale). Chinese respondents' acceptance was determined by their perception of benefits, whereas New Zealand respondents were influenced by their perception of both benefits and risks. The findings conclude that FTNS is a valid tool to reflect consumers' acceptance of novel food technologies in cross-national contexts, although the influence of FTN varies among consumers from different countries. PRACTICAL APPLICATION: China has the largest food market, and New Zealand is a leading food exporter. Understanding their consumers' acceptance of and attitudes toward food technologies will help food companies implement appropriate strategies in developing and using novel technologies. Because FTNS first was constructed in 2008, it has been applied in Australia, Italy, Canada, Finland, Korea, China, Chile, Brazil, and Uganda; the findings of this study will allow these individual studies on FTNS to better connect, help food companies predict consumer acceptance of food technologies in the global market, and help them identify early adopters of novel food technologies in new food markets.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".