Does greenwashing dim green actions? Examining how greenwashing impacts consumers’ pro-environmental intentions
Bibliographic record
Abstract
Purpose Greenwashing has become increasingly prevalent in contemporary business practices. Despite this rise, there remains a notable gap in academic research regarding how customers’ perceptions of greenwashing influence their pro-environmental intentions beyond mere green purchasing. This study aims to bridge this gap by drawing psychological inferences to understand how perceived greenwashing influences customers’ pro-environmental intentions. Design/methodology/approach This study uses a scenario-based, cross-sectional research design to explore the effects of perceived greenwashing. Data were collected from a micro-networking website, with a total of 360 Indian consumers participating in the study. The analysis was conducted using partial least squares – structural equation modeling (PLS-SEM) to assess the relationships posited in the research. Findings The analysis revealed that greenwashing significantly impacts perceived deceptiveness and consumer cynicism, both, in turn, influencing perceived ineffectiveness. This perceived ineffectiveness then negatively affects pro-environmental intentions, demonstrating how greenwashing undermines consumers’ willingness to engage in environmentally friendly behaviors. In addition, the study provides valuable insights into how different types of greenwashing affect these relationships. Practical implications The study offers critical implications for mitigating the negative impact of greenwashing on pro-environmental intentions, which are challenging to foster. Originality/value Existing literature primarily concentrates on the impacts of greenwashing on brands or customers’ switching and continuation intentions. This pioneering study introduces a unique perspective by exploring the impact of greenwashing on customers’ pro-environmental intentions in their everyday lives. Specifically, it investigates whether unethical practices by a brand can influence consumers’ lifestyles beyond the adverse effects these practices may have on the brand itself. This approach provides a deeper understanding of how greenwashing can affect consumer behavior in a broader context, offering significant insights into the intersection of corporate ethics and individual environmental actions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".