A Review on Green Purchase Behaviour Trend of Malaysian Consumers
Bibliographic record
Abstract
The emergence of environmental problems and the increased awareness towards green purchase behaviour have received many responses by the stakeholders’ worldwide like from the government bodies, researchers, businesses, consumers and so on. Government’s bodies, for example, have responded by developing and introducing their own environmentally-linked policies to be implemented in their countries, which are intended to conserve and preserve the environment. Researchers, on the other hand, are continuously conducting extensive studies and publishing their findings on the issues to inform the public, while businesses that promote the selling of green products (or environmentally friendly products) in the marketplace have been increasing in number. Segments of green consumers have been observed to emerge and grow in size worldwide including Malaysia. This may be due to the increased number of green products introduced to consumers in the marketplace. Moreover, scholars from Malaysia also argued that, this trend is experiencing tremendous growth. Although there are responses from these stakeholders, especially consumers, who have had a positive impact on the environment, the trend of the green purchase behaviour by Malaysian consumers remains unobserved. Therefore, the authors aim to answer the questions concerning whether a trend can be observed in the green purchase behaviour of Malaysian consumers. The ability to observe the green purchase behaviour trend is useful, particularly for marketers and businesses that are selling or intending to sell green products within the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".