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Record W2157499060 · doi:10.5539/ijps.v4n1p22

Determinants of Sustainable Food Consumption: A Meta-Analysis Using a Traditional and a Structura Equation Modelling Approach

2012· article· en· W2157499060 on OpenAlexvenueno aff
Yan Han, Håvard Hansen

Bibliographic record

VenueInternational Journal of Psychological Studies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of planned behaviorPsychologyNorm (philosophy)Social psychologySustainable consumptionVariance (accounting)Scope (computer science)Consumption (sociology)Structural equation modelingFood consumptionControl (management)SustainabilityComputer scienceStatisticsSociologyPolitical scienceArtificial intelligenceMathematicsEconomicsSocial science

Abstract

fetched live from OpenAlex

Based on a database of 16 empirical studies, this MASEM study aims to provide an overview of existing antecedents of sustainable food consumption within an integrative framework based on the TPB. Among the antecedents, Personal Norm, Attitude and Subjective Norm displayed strongest effects on Intention, followed by Beliefs, Perceived Behavioral Control and Ethical Concern, which were also within the scope of medium to large. As for the correlations of Behavior, Personal Norm, Attitude and Subjective Norm showed strongest effects, and the effects of Intention, Beliefs and Perceived Behavioral Control were also within the scope of medium to large. Results of the MASEM study indicate that both TPB and extended TPB models have statistically acceptable power in explaining the intention and behavior of sustainable food consumption, while a slight increase was made to the amount of explained variance of intention by adding Personal Norm to TPB. The results of our meta-analyses give readers an understanding of the magnitude and significance of relationships between antecedents and intention in the sustainable food consumption domain.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.351
GPT teacher head0.376
Teacher spread0.024 · 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

Citations78
Published2012
Admission routes1
Has abstractyes

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