The Greenconsumption Effect: How Using Green Products Improves Consumption Experience
Bibliographic record
Abstract
Abstract In many situations, consumers use green products without a deliberate choice to use or purchase the product. This research explores how using a green product (e.g., a pair of headphones made from recycled materials) influences the enjoyment of the accompanying consumption experience (e.g., listening to music), even if consumers have not deliberately chosen or purchased the product. Five experiments in actual consumption settings revealed that using a green (vs. conventional) product enhances the enjoyment of the accompanying consumption experience, referred to as the greenconsumption effect. Merely using a green product makes consumers perceive an increase in the extent to which they are valued as individuals by society, which leads to warm glow feelings, and consequently enhances the enjoyment of the accompanying consumption experience. When consumers experience low social worth, the positive effect of using green products on the accompanying consumption experience is amplified. The greenconsumption effect disappears when the negative environmental impact of the green product attribute is low. From a managerial standpoint, the current research identifies instances where brands can benefit from going green and encourages marketers, especially service providers, to promote green products that are instrumental in consumption experiences.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".