Developing Perceived Greenwash Index and Its Effect on Green Brand Equity: A Research on Gas Station Companies in Turkey
Bibliographic record
Abstract
Today, companies are searching for the ways to be perceived as more sensitive to the environment in order to enhance their green brand equity, because of consumers’ increasing environmental concern. Companies have reacted to increasing environmental consciousness of consumers by introducing and developing eco-friendly products. However, there are still consumers being suspicious about the environmental performance of companies and their products. Greenwash or disclosure of deceptive green claims decreases the popularity of the real green product and decreases the effectiveness of green marketing.This study proposed four constructs -greenwashing, green perceived risk, green confusion and green trust- as the predictors of the green brand equity of gas station companies. The study offers a negative relationship between greenwash perception and green brand equity. Besides, the effects of green confusion, green perceived risk and green trust on green brand equity are tested. The study also develops perceived greenwash index, so that it reveals a direct effect of greenwash on green brand equity. The empirical analysis was carried out based on the data obtained from 400 customers of the gas station companies, which are located in Ankara, the capital city of Turkey. The survey result was analyzed by using Partial Least Squares (PLS-PM) analysis method. The results reveal that consumer’s greenwash perception has a positive effect on green confusion and green perceived risk, whereas green confusion and green perceived risk have negative effects on green trust. Expectedly, green trust has a positive effect on green brand equity. The result also indicates that consumer’s greenwash perception negatively and directly affects green brand equity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".