How to Regain Green Consumer Trust after Greenwashing: Experimental Evidence from China
Bibliographic record
Abstract
Greenwashing leads to consumer skepticism of all green products as well as doubts about company claims regarding sustainability. However, the understanding of how to regain green consumer trust after greenwashing is rather limited. The authors fill this gap by exploring the psychological process of green consumers following intervention strategies designed to reduce greenwashing. We collect and interpret quantitative data from two psychological experiments, the first experiment identified two types of intervention strategies that serve to counter the negative impact of greenwashing and based on our findings from the first studies, we proposed and tested the moderating effect of two factors—implicit beliefs of consumers and companies who implement intervention strategies after greenwashing. The results indicate that distrust regulation (quantifying a product’s green attributes) and trustworthiness demonstration (visualizing environmental behaviors) are effective intervention strategies that can enable consumers to re-evaluate the cost-benefit of green products, and which may serve as critical psychological factors for green consumers and contribute to the degree of trust. Validation and comparative study of the derived results show that distrust regulation, followed by trustworthiness demonstration, has the best effect on increasing green trust after intervention. If the sequence is reversed, the effect of the intervention strategy is worse than if only one strategy had been applied. The implicit beliefs of green consumers play a moderating role between intervention strategies and reconsideration of the cost-benefit of green products. The behavior of genuinely green companies and the incremental beliefs of consumers can promote the intervention effect after greenwashing. Alternatively, the behavior of greenwashing companies can easily counter these effects. These findings contribute to knowledge about which psychological factors can promote or hinder the effectiveness of an intervention.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".