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Record W3091912047 · doi:10.1111/joss.12619

Consumers' attitudes towards <scp>3D</scp> printed foods after a positive experience: An exploratory study

2020· article· en· W3091912047 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Sensory Studies · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsAcadia University
Fundersnot available
KeywordsLikert scalePsychology3d printedScale (ratio)Exploratory researchQualitative researchQualitative propertyDisgustPoint (geometry)Applied psychologySocial psychologyMarketingMedicineComputer scienceBusinessDevelopmental psychologyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.303
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it