Eco-Conscious Consumers and Influencer Marketing: Understanding the Path to Green Purchases through the Theory of Planned Behavior
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".