The Impact of Green Marketing and Perceived Innovation on Purchase Intention for Green Products
Bibliographic record
Abstract
With the rise of eco-awareness and innovation in recent years, companies have constantly sought to be the firstto introduce new green-concept products to the market to gain a larger market share. However, it is unclearwhether consumer awareness of green marketing and innovation will increase purchase intention. This issuerequires an in-depth discussion. This study uses energy-saving lamps and environmental cleanser as examples,using a literature review and empirical research to explore the correlations between consumer awareness of greenmarketing, perceived innovation, perceived quality, perceived price, perceived risk, perceived value, andpurchase intention. Further, an overall relationship model is established.An analysis of 320 effective questionnaires about energy-saving lamp and 310 effective questionnaires about anenvironmental cleanser resulted in three main findings: (1) Consumers’ green marketing awareness of bothenergy-saving lamp and an environmental cleanser mainly influences their perceived quality and perceived value,which in turn influence purchase intention. (2) Consumers’ perceived innovation of energy-saving lamp mainlyinfluences their perceived quality, perceived price, and perceived value, while consumers’ perceived innovationof an environmental cleanser mainly influences their perceived quality and perceived value, all of which in turninfluence purchase intention. (3) The results for the two products indicate that the impact of consumers’ greenmarketing awareness on purchase intention is greater than the impact of perceived innovation. Through SEManalysis, this study establishes a valid relationship model for green products and identifies the main influencepaths. In addition, measurement variables and a scale were established, which provide academics and industrywith critical research tools and concepts that should be of academic and practical value.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".