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Record W3010173671 · doi:10.35409/ijbmer.2020.3136

CONSUMER PURCHASE INTENTIONS FOR CERTIFIED FARM-RAISED ATLANTIC SALMON

2020· article· en· W3010173671 on OpenAlexaffabout
Dipika Majumder, Morteza Haghiri

Bibliographic record

VenueInternational journal of business management and economic review · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Supply Chain Traceability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCertificationBusinessAdvertisingMarketingFisheryAgricultural scienceCommerceEconomicsEnvironmental scienceBiologyManagement

Abstract

fetched live from OpenAlex

The occurrences of food safety incidents like polychlorinated biphenyls in farmed Atlantic salmon in Canada heightened public awareness causing significant reduction in the consumption of the product. This has induced policymakers and stakeholders to implement traceability systems as part of enhancing consumers' trust and safety in the industry. This study provides information on consumers' awareness about traceability systems of farm-raised Atlantic salmon and their willingness to pay for traceable product in the province of Newfoundland and Labrador, Canada. In this study, we used a logistic regression model to assess consumers' preferences for farm-raised Atlantic salmon. To estimate the parameters of the model, a telephone survey was carried out in fall 2018 over 200 consumers in the province. The results of the study showed that age of the respondents, education level, household size, and household consumptions were significant determinants of the Newfoundlanders and Labradoreans' willingness-to-pay a premium price for the farm-raised traceable salmon. Moreover, a shortage of public knowledge about the traceability systems was also observed in the empirical evidence. To increase the consumers' knowledge about the value of traceability system and its aspects, provincial authorities and private food companies need to take further initiatives. Providing detail labeling could be one of the suitable ways of communicating traceability to consumers. Besides, comprehensive monitoring by the competent authorities is also required to guarantee the truthfulness of traceable information and to reveal the food safety problems for enhancing the degree of consumer confidence in traceability systems. .

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.264
Teacher spread0.209 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations1
Published2020
Admission routes2
Has abstractyes

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