Green awareness through environmental knowledge and perceived quality
Bibliographic record
Abstract
Green awareness is worth researching to determine the customer consumption pattern of environment-friendly products. Several research models are showing the importance of green awareness of customer behavior. This paper studies the role of information in marketing decisions related to customer green awareness. Based on the phenomenon of green awareness, this research work aims to study the role of customer green awareness built through eco-label, environmental knowledge, and perceived quality. This experimental research is conducted on 200 supermarket customers who had experience with green products. The data is collected through a questionnaire and analyzed using the Structural Equation Model approach. SmartPLS is conducted to test the research hypotheses. The findings show that there was a relationship between the eco-label credibility of environment-friendly products on the customers' increased environmental knowledge and perceived quality of the products. Besides, both environmental knowledge and perceived quality are identified to play an essential role in controlling green awareness. Eco-label in product attributes is found to be capable of changing the positive side of green awareness. These findings describe a model in developing green awareness through environmental knowledge and perceived quality with the support of environment-friendly product eco-label. The model also can predict customer green awareness and support the green marketing strategy. Therefore, further research works on green customer behavior are welcome, as green customer behavior must impact on the implementation of green marketing strategies. Also, we may predict the customer behavior of environment-friendly products, and implement better business strategies.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".