The effect of greenwashing on online consumer engagement: A comparative study in France, Germany, Turkey, and the United Kingdom
Bibliographic record
Abstract
Abstract The effect of the Volkswagen emission crisis in 2015, the biggest greenwashing event in recent years, on the online consumer engagement of Facebook brand pages was investigated in France, Germany, Turkey, and the United Kingdom. These countries have been selected for the reason that Volkswagen sales and diesel‐engine cars and the use of social media are quite common. For 6 years in these four countries, the likes, comments, and shares made by consumers on the Facebook brand page of Volkswagen have been examined. The monthly dataset covers January 2012 to December 2017. The obtained data were analyzed with autoregressive–moving average models. Despite a globally positive approach to green products, countries' attitudes toward greenwashing have been significantly different. The findings showed that online consumer engagement was negative in the United Kingdom and Turkey and in a positive direction in Germany, whereas there was no change in France in the fourth quarter of 2015.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".