Role of Environmentalism in the Development of Green Purchase Intentions: A Moderating Role of Green Product Knowledge
Bibliographic record
Abstract
Environmentalism has become not only a significant policy issue but also a crucial topic in marketing research. Environmental consciousness is now considered as a fundamental requirement to prevent further damage to the environment. Efforts have been made to improve the consumers understanding about the environmental consequences of their consumption patterns. This study develops a conceptual model of green purchase behaviour to determine the effects of environmentalism, advertisement message credibility and green perceived value on the consumers’ green purchase intentions with green product knowledge as a moderator. The convenience sampling technique with cross-sectional research design was used to select the sample. Data were collected using survey, and a total of 368 valid questionnaires were used for further analysis. The results from the structural analysis using Smart-PLS depict that environmentalism significantly influences the ad message credibility and green perceived value, and these constructs significantly affect the green purchase intentions. Furthermore, the green product knowledge moderates the relationship between environmentalism, ad message credibility and green perceived value. Overall, the findings of this study extend the theoretical basis of green purchase intentions by empirically testing the proposed model and provides the limitations and further research directions.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".