The effects of customer perceived value and perceived innovation on green products
Bibliographic record
Abstract
Customer behavior of environmentally friendly products becomes marketing attention by implementing a green marketing strategy to improve customer’ green trust. The study concentrates on the correlation of eco-label attribute, perceived innovation, perceived quality, and green trust of a customer. The study was conducted in 2020 with a survey of supermarket’s customers who were familiar with green products. There were 200 customers who were selected randomly; data from a customer was taken by questionnaire. Then, data from the questionnaire was processed by using SEM approach through SmartPLS. The research finding determined that the implementation of an eco-label attribute may influence on customer’ green trust directly through customer perceived quality. Furthermore, it was determined that customer perceived quality could play the mediation role between eco-label attributes and green trust. Besides, it has known that the value of innovation of green products could not be affected by eco-label attributes and it could not affect customer’s green trust. The study provides a recommendation from the model of green customer behavior with mediation focuses on customer perceived quality. The finding can provide important information for marketers and producers who use the environmental issue, and it is implemented to green marketing strategy.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".