Could Perceived Risks Explain the âGreen Gapâ in Green Product Consumption?
Bibliographic record
Abstract
Although green consumption is increasingly popular in the academic literature, practice is still far from commonplace among consumers. Few studies have been conducted to explain consumer reluctance to adopt green products (GPs), particularly with regard to the roles of the various risks consumers perceive in their purchases. However, perceived risks towards GPs could be one of the explanations for the âgreen gapâ â the difference between pro-environmental attitudes and green purchase behaviour. We used a means-end chain (MEC) approach to explore the links that consumers establish between the attributes of green cleaning products, their consequences, and their perceived risks. Findings indicate that consumers perceive greater risk with respect to the functional, financial, and temporal aspects of GPs than to their physical and psychosocial aspects. Social desirability appears to be a strong personal value attached to the purchase of GPs. We also identified positive (pleasant fragrance, natural ingredients, recyclable packaging, lack of health risks, protection of the environment, enhancement of personal and social image) and negative motivations (limited distribution, weaker concentration, less attractive label, higher cost, longer and more complex purchasing process, product ineffectiveness) associated with the purchase of green cleaning products.
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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 | 0.006 | 0.024 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".