Digital Greenwashing in the Age of Sustainability Marketing: A Meta-Analysis of Consumer Perception, Detection, and Backlash
Bibliographic record
Abstract
Over the past years, the rise of sustainability-driven marketing in the digital sphere has transformed the way in which brands communicate about environmental responsibility, a turn of events that has also heightened concerns about greenwashing (i.e., false claims that inflate or invent sustainable practices). This research critically analyses the changing nature of greenwashing in digital times by incorporating the knowledge of 24 peer-reviewed studies carried out over the last 6 years (2020–2025). In addition, the research bases its scholarship within marketing, psychology and environmental communication literature to critically examine the perception, recognition and reaction of consumers to greenwashing in online environments, including social media platforms, e-commerce, and corporate websites. Research shows that consumers are becoming more aware of deceptive sustainability claims. However, their ability to detect them remains inconsistent due to cognitive biases, trust in different platforms, and the complexity of digital marketing tactics. Importantly, the research shows that consumer reactions to greenwashing detection have become more severe because people lose trust in brands and sometimes publicly shame them and boycott their products. The research reveals that these backlash patterns are most intense among younger, digitally literate consumers who are also most active in industries with significant environmental impact, such as food and fashion. The findings from the research, therefore, highlight a significant disconnect between consumer intent (in terms of supporting sustainable brands) and their ability to detect authentic sustainability. In light of this, this paper presents a conceptual framework to assess consumer reactions to greenwashing while proposing policy solutions to improve transparency in communicating digital sustainability. The results demonstrate that reinforced digital literacy training, in combination with stronger regulatory control, is necessary to empower and protect consumers in an environment perpetuated with increasingly greenwashed digital information.
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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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".