The Impact of Greenwashing on Brand Reputation, Brand Credibility, and Green Brand Equity: Evidence from the Household Appliances Market
Bibliographic record
Abstract
As customer consciousness of environmental topics increases, green marketing is quickly emerging as a crucial strategy for companies to achieve a competitive advantage. In addition, the rapid expansion of green practices has created concerns among consumers about companies that covertly capitalize on green trends and initiate a discussion about their potential effects on environmental quality. As a result, companies have skillfully used “green” phrases and labeling on any occasion to trick buyers into thinking they are purchasing more environmentally friendly products than they are. Therefore, the question arises: does the practice of greenwashing affect brand reputation, brand credibility, and green brand equity? We assess the proposed model using partial least squares structural equation modeling (Smart PLS software, version 4). Data were collected from 336 customers of green household appliances in Egypt. The results show that greenwashing has a negative effect on green brand equity, brand reputation, and brand credibility. In addition, green brand equity has a positive impact on brand reputation. Brand reputation has a positive influence on brand credibility. Finally, green brand equity has a mediation role in the relationship between greenwashing and brand reputation. The findings have discussed many initiatives intended to lessen the damaging impacts of greenwashing. Additionally, we provide several insightful avenues for the household appliance market.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".