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Record W4226078324 · doi:10.3389/fsufs.2022.853692

Consumers' Expectations on Transparency of Sustainable Food Chains

2022· article· en· W4226078324 on OpenAlexaff
Renata Pozelli Sabio, Eduardo Eugênio Spers

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité de MontréalProvidence Health Care
Fundersnot available
KeywordsTransparency (behavior)BusinessPerceptionMarketingProduct (mathematics)Sustainable consumptionSustainabilitySustainable agricultureConsumption (sociology)EconomicsProduction (economics)MicroeconomicsPsychologyComputer science

Abstract

fetched live from OpenAlex

The search for food products from sustainable chains has increased in the past years, motivated by consumers' interest in reducing the negative environmental, economic, and health impacts of their food choices. However, it is not yet clear whether transparency expectations of sustainable food chains influence in consumers' perception of this food products. The literature shows that there are gaps in the growth of sustainable product consumption is the transparency of production and the provision of more information to consumers. In this paper, we aimed to better understand what is the role of transparency expectations and how they influence consumers' decision to consume sustainable food products. Based on scales already validated in the literature, a theoretical model with nine hypotheses was proposed. A questionnaire was structured and empirically tested through a survey with 136 consumers of food from alternative networks. Six hypotheses were validated. Three segments of consumers target were identified from an exploratory factor analysis and cluster. Based on the results some marketing actions were suggested for the participants of alternative food networks. Other studies may validate the model proposed here.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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.

Study designQualitative
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

Citations19
Published2022
Admission routes1
Has abstractyes

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