New or recycled products: how much are consumers willing to pay?
Bibliographic record
Abstract
Purpose This paper aims to consider the price premium that consumers state they are willing to pay for products with reused or recycled content. It also aims to address the effect of the impact of product category on consumers' willingness to pay premium prices. Design/approach/methodology Willingness to pay was studied for seven different product categories ( n =49). Findings Perceived functional risk is an important determinant of the price that consumers are willing to pay for products that have recycled or reused content. It was also found that consumers will switch from a recycled product to a new product within a smaller range of price for products with high functional risk. Research limitations/implications The study is exploratory, while it serves its purpose by raising initial questions and finding that this is a complex area that is worth studying. Additional work is clearly required to consider the wide range of potentially relevant variables and a sampling plan that ensures an understanding of the generalisability of findings across the population within a region and across regions. Practical implications A technique for understanding consumer willingness‐to‐pay (WTP) is provided and insights into differences are offered between products in terms of WTP for greener products. Practitioners can use this technique to determine the price range and indirectly the profitability of a version of their product based on recycled or reused content. Originality/value An understanding of WTP for products with recycled or reused content is developed. This is important as legislation in many countries aims at diverting disposed product from waste dumps to consumers.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".