Effect of Green Consumption Perception Degree on Relationship Model of Green Consumption Behavior
Bibliographic record
Abstract
Consumption behavior significantly influences environment; thus, in order to avoid the harm of consumption behavior on environment, consumers must pay attention to green consumption behavior in order to contribute themselves. This study aims to probe into difference of groups of different green consumption perception regarding relationship model of green consumption perceived benefit, perceived risk, subjective norm, perceived control, perceived value, behavior intention and actual behavior. After retrieving 626 valid questionnaires, the researcher divided consumers into groups of medium and high green consumption perception. By comparison, the researcher realized that groups of different green consumption perception degrees have significant difference on effect of two relationship paths. The effect of green consumption subjective norm of group of high green consumption perception on behavior intention and actual behavior is significantly higher than group of medium green consumption perception. In addition, green consumption perceived risk of group of high green consumption perception significantly and negatively influences perceived value. Perceived control significantly and positively influences behavior intention. However, group of medium green consumption perception does not have significant effect on the two paths. On the contrary, green consumption perceived risk of group of medium green consumption perception significantly and negatively influences behavior intention. Group of high green consumption perception does not have significant effect on the path.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".