Eco-Labeling Perspectives amongst Malaysian Consumers
Bibliographic record
Abstract
This study explores the Malaysian consumers’ trust of an eco-label and the influence it has in their choice for the corresponding environment friendly product. Taking into consideration the infancy stage of the Malaysia green marketing initiative, traditional approach to evaluating local consumer receptiveness to the eco-label might not be suitable. This paper approaches the introduction of eco-label with two perspectives in mind. Firstly, while earlier studies from the western scholars use eco-label as a part of the augmented product, this study introduces eco-label as a separate moderating variable. Secondly, the choice of employees working in ISO14001 certified organizations as the population explore a potentially conducive place to initiate a systematic effort in developing a green consumer community. The result is very encouraging. This study has shown that, with some exposure to environmental related experiences Malaysian consumer would indeed react positively to the eco-label. In fact, for situation that requires them to consider environmental aspects of a product that they wish to purchase, the eco-label will definitely be the crucial factor that will push them to make the right purchase choice. Key words: Eco-label; Environmental attitude; Knowledge of Environmental Issues; Green Products; Environmental Management System
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".