Analyzing the relationship between consumer trust, awareness, brand preference, and purchase intention in green marketing
Bibliographic record
Abstract
As part of their corporate social responsibility (CSR) and sustainable products, many big players in the industry have now reduced plastic through the design, manufacturing and packaging of products and their ultimate disposal. This paper investigates the direct and indirect relationships between awareness, trust, and brand preference on purchase intention in green marketing. Based on a review of the literature, a series of hypotheses are derived and tested using regression analysis. The research employs an online survey-based method to test a theoretically grounded set of proposed hypotheses. The data were collected from 348 young adults living in Jakarta, Indonesia. The results show that green awareness does not influence purchase intention directly. On the other hand, the indirect effect of green awareness through green brand preference on purchase intention was greater compared with the indirect effect through green trust. Therefore, this study draws attention to the importance of green brand preference and green trust on purchase intention. Given that the consumption of organic products has the potential to elicit awareness, trust, and preference of eco-friendly consumers, these findings have significant management implications for corporate managers when considering the production of organic commodities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".