Green Energy Product: The Role of Green Marketing Mix and Green Brand Image on Consumer Decision-Making
Bibliographic record
Abstract
This study investigates the influence of the green marketing mix on green brand image and consumer purchase decisions, with a specific focus on Pertamina Green Energy Station (GES) products.Emphasis is placed on determining the influence of green brand image on consumer decision-making.A quantitative approach was employed to assess the extent to which the elements of the green marketing mix and green brand image shape perceptions of purchase intentions.It was found that the green marketing mix exerts a positive and statistically significant effect on both green brand image and purchase decisions.Moreover, a green brand image was shown to significantly enhance purchase decisions, functioning as a mediating variable in the relationship between the green marketing mix and consumer behavior.These findings suggest that a coherent and strategically aligned green marketing mix not only elevates the perceived credibility of environmentally friendly brands but also increases consumer propensity to purchase green energy products.By integrating environmental responsibility into all aspects of marketing strategy, companies such as Pertamina can strengthen their brand equity and foster consumer trust.The study contributes to a deeper understanding of the mechanisms through which sustainable marketing practices can shape brand image and drive environmentally conscious purchasing behavior, thereby offering valuable implications for practitioners and policymakers aiming to promote the adoption of green energy solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".