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Record W4404902775 · doi:10.6007/ijarbss/v14-i11/23515

A Systematic Literature Review of Consumer Attitude towards Sustainable Fashion

2024· article· en· W4404902775 on OpenAlexaff
Shujie Wang, Siti Hasnah Hassan

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsSystematic reviewAestheticsBusinessPsychologyMarketingArtPolitical scienceMEDLINE

Abstract

fetched live from OpenAlex

With the environmental pollution caused by the development of the fashion industry, consumers' attitudes toward sustainable fashion consumption have also attracted attention. Consumer attitudes are not only related to individual sustainable consumption choices but also directly affect the development of the sustainable fashion market. Therefore, understanding and promoting consumers' attitudes has become an important topic for sustainable marketing. This study aims to systematically review and analyze consumer purchasing attitudes in sustainable fashion, including its concepts, antecedent variables, and outcome influence. By studying the existing research results, we can better understand how consumer attitudes are formed and affect their consumption outcomes, thus providing theoretical guidance and practical suggestions for sustainable fashion marketing. This study used a literature review approach to increase the understanding of consumer attitudes in the field of sustainable fashion. The main findings include the multidimensional influencing factors of consumer attitudes and the effect of attitudes on purchase intention. Future research can explore consumer attitudes in the context of sustainable fashion from other perspectives, including cultural differences, interdisciplinary approaches, and emerging technologies.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.397
Teacher spread0.345 · 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 designSystematic review
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
Published2024
Admission routes1
Has abstractyes

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