The Antecedents of Green Purchase Intention among Malaysian Consumers
Bibliographic record
Abstract
The objectives of this research are to identify the factors influence the green purchase intention and to determine the relationships between the factors (determinants) and green purchase intention among Malaysian consumers. A descriptive research was conducted to address the research objectives. The survey research was undertaken among the Malaysians who are members of one of the Activist Groups in which is a Non-government Organization (NGO) in Malaysia. The adopted sampling method was simple random sampling. There were 230 usable questionnaires which were analyzed with the Statistical Package for Social Science Software version 19. Five hypotheses were developed for this research and all hypotheses were tested using Pearson Correlation Analysis and Multiple Regression Analysis. The results of the study indicated that government initiative has the most significant influence on green purchase intention among Malaysian consumers. In contrast, eco-label failed to show significant relationship to green purchase intention. The finding of insignificant impact of eco-label on the green purchase intention from this study is in contrast with the finding from Nik Abdul Rashid (2009) because both studies were carried out among Malaysian consumers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".