The effect of environmental concern on conscious green consumption of post-millennials: the moderating role of greenwashing perceptions
Bibliographic record
Abstract
Purpose This study aims to demonstrate how greenwashing perceptions shape the effect of environmental concern on post-millennials purchasing behavior. Design/methodology/approach Based on 174 responses gathered through a street survey method from 5 different universities in Turkey, data are analyzed using the statistical package for social sciences software (SPSS 16.0). Principal component analysis is performed to assess the differentiation in factors. Multiple regression analysis is used to examine the effects of the items on the post-millennials purchasing and recommendation behavior. Findings The main findings revealed that the environmental concern trait of post-millennials triggers their green purchasing behavior. When the concern on green products is high, the awareness of perceiving that “if the product is actually green or pretending to be green” is high. When the post-millennials take the greenwashing perception into account, their environmental concern has lower effects on their green behavior. The moderating role of greenwashing between environmental concern and green purchasing is apparent. Greenwashing perception decreases the effects of environmental concern on green behavior. Originality/value The research raises the concept of greenwashing perception that moderates the relationship between environmental concern and post-millennials purchasing behavior. This study also demonstrates that greenwashing awareness has a critical role in creating a purchasing behavior of post-millennials that have environmental concerns.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".