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Record W3115571949 · doi:10.3390/jrfm13120330

Consumer Behaviour towards Organic Products: The Moderating Role of Environmental Concern

2020· article· en· W3115571949 on OpenAlexvenueno aff
Silvia Cachero‐Martínez

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetBusinessContext (archaeology)SustainabilityMarketingOrganic productConsumption (sociology)Order (exchange)PsychologySample (material)Consumer behaviourModerationAdvertisingAgricultureSocial psychologyGeographySociology

Abstract

fetched live from OpenAlex

The pandemic caused by COVID-19 has changed the mindset of many consumers. They are increasingly aware of the risks of not caring for the planet. Before the pandemic, there was a perceived increase in collective environmental concern and sustainability, but COVID-19 has further accelerated this process and motivated more people to assume this responsibility. Thus, the health crisis could trigger the consumption of organic foods, which are foods produced through environmentally friendly agricultural methods and that have not been artificially altered. It is essential for retailers to know how these consumers of organic foods behave in order to try to modify their strategies. In this context, the objective of this research is to analyze the relationship between attitude, satisfaction, trust, purchase and word-of-mouth (WOM) intentions towards organic products. The results of a survey administered a survey to a sample of 195 consumers show that trust is influenced by satisfaction and attitude. In relation to the behavioural variables, satisfaction is the variable that has the greatest influence on purchase intentions and WOM intentions. In addition, a moderating effect of environmental concern is observed on the proposed relationships.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.483

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 head0.007
GPT teacher head0.182
Teacher spread0.175 · 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 designObservational
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

Citations102
Published2020
Admission routes1
Has abstractyes

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