Could Perceived Risks Explain the âGreen Gapâ in Green Product Consumption?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.022 |
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 it