Consumers' attitudes towards <scp>3D</scp> printed foods after a positive experience: An exploratory study
Bibliographic record
Abstract
Abstract 3D food printing has far‐reaching potential in the food industry; however, consumer attitudes towards 3D food printing need to be evaluated. The present study investigated consumers' attitudes towards 3D food printing after consuming a cookie that was labeled as 3D printed. The participants (n = 133) first evaluated two cookies (conventional and “3D printed”) using hedonic scales and a check‐all‐that‐apply question. The participants were then asked to answer survey questions (7‐point Likert scale) and open‐ended comments about 3D printing. The results of the survey questions indicated that after consuming the “3D printed” cookie, the participants were willing to eat 3D printed foods and felt they were sustainable. The open‐ended comments highlighted some barriers to consumers' acceptance, including disgust, safety and unacceptability of 3D printed meat products. The findings illustrate that participants are less fearful of novel technologies if they have a positive experience with a food item produced by that technology. Practical applications A sensory trial (hedonic scales and check‐all‐that‐apply) was used as a priming step before asking participants about their attitudes towards 3D food printing. The participants' opinions were identified using quantitative (7‐point Likert scales) and qualitative (open‐ended comments) questions. The open‐ended comments allowed the participants to build on the responses they expressed with the Likert scales and identified other attitudes that were not included in the quantitative questions. Studies on consumer attitudes should include both quantitative and qualitative questions. The results indicated that if consumers have a positive experience with a 3D printed product (evaluated using hedonic scales), they have positive attitudes towards 3D printing. Additionally, the study highlighted for 3D food printing to be accepted by consumers; their concerns about safety and the unacceptability of 3D printed meat products need to be addressed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".