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Record W3121289443

Food Authenticity, Technology and Consumer Acceptance

2012· article· en· W3121289443 on OpenAlexaboutno aff
Jill E. Hobbs, Jillian McDonald, Jing Zhang

Bibliographic record

Venue2012 Annual Meeting, August 12-14, 2012, Seattle, Washington · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsnot available
Fundersnot available
KeywordsTraceabilityProduct (mathematics)MarketingBusinessLogitComputer scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Traceability and authenticity issues have gained increasing prominence in food markets and create both opportunities and challenges for the food industry in providing credible information to consumers. Internal molecular tagging is an emerging technology with the potential to deliver traceability and authenticity assurances. A key question for the food industry in adopting new technologies is consumer acceptance. This paper explores consumer attitudes toward traceability and authenticity and the role of information in affecting consumer acceptance of new technologies, using molecular tagging as an example. Data were gathered from an online survey conducted in Canada in December 2010. To determine whether product-specific effects exist, two versions of the survey were used, focusing on salami and on apple juice. In a discrete choice experiment respondents were presented with choice sets describing an apple juice (salami) product containing different combinations of four attributes: traceability technology (molecular tag Vs RFID), price, brand, country of origin. Of particular interest was the effect of information on consumers’ choices. Therefore, respondents were randomly assigned to one of four information treatments: positive information on molecular tagging technology or further information on the issue of food authenticity and adulteration, or a combination of both. The control group was provided with neutral information on the technology and no additional information. Results from Conditional Logit and Random Parameter Logit models reveal that initial consumer acceptance of the technology is low, however, information matters. Highlighting the problems of adulteration reduces resistance more effectively than providing positive technology information, and the effects appear to be product specific across a juice product versus a processed meat product. Other proxy signals (country of origin, brand), resonate strongly with consumers and tended to have a larger impact on willingness-to-pay.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.016
GPT teacher head0.233
Teacher spread0.216 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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