MétaCan
Menu
Back to cohort
Record W4409409319 · doi:10.28924/2291-8639-23-2025-86

Eco-Conscious Consumers and Influencer Marketing: Understanding the Path to Green Purchases through the Theory of Planned Behavior

2025· article· en· W4409409319 on OpenAlexvenueno aff
Nilna Muna, Ni Wayan Eka Mitariani, Ni Luh Wayan Sayang Telagawathi

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsInfluencer marketingTheory of planned behaviorMarketingMathematicsAdvertisingPath (computing)Green marketingBusinessMarketing managementRelationship marketingEconomicsComputer scienceControl (management)Management

Abstract

fetched live from OpenAlex

This study aims to investigate the direct and indirect impacts of influencer marketing on Balinese consumers' propensity to purchase eco-friendly products and services. It specifically explores the mediating roles of information credibility and positive emotions within this framework. This quantitative research design collected data from 246 Balinese consumers through questionnaire and analyzed it using SEM AMOS. A structural study methodology is adopted. To test the model and hypotheses, the authors used the analysis moment structure structural equation modeling AMOS 23. This study reveals that while influencer marketing may not directly lead consumers to purchase eco-friendly products, it wields a powerful indirect influence. The findings highlight that information credibility and positive emotions are crucial mediators in this relationship. This study offers valuable insights into influencer marketing’s impact on eco-friendly purchases in Bali, but further research is needed for broader applicability. This research guide marketers in using influencer marketing to promote sustainable consumption, higlihgting information credibility and positive emotions as key to enhancing purchase intention.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.334
Teacher spread0.312 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Analysis and ApplicationsSame topicDigital Marketing and Social MediaFrench-language works237,207