Product Reuse in Innovative Industries
Bibliographic record
Abstract
Most models of product reuse do not consider the fact that firms might be required to innovate their products over time in order to continue to appeal to the tastes of customers. We consider how the rate of this required innovation, which might be fast or slow depending on the product, affects reuse decisions. We consider two types of reuse—remanufacturing to original specifications, and upgrading used items by replacing components that have experienced innovation since the item was originally produced. We find that optimal reuse decreases with the rate of innovation, implying that models that ignore innovation overestimate the optimal amount of reuse that a company should pursue. Furthermore, we show that reuse can be encouraged in two ways—the intuitive approach of increasing end‐of‐life costs, and the less intuitive approach of raising the cost to make items reusable. We also examine the environmental impact of reuse, measured in terms of virgin material usage, finding that reuse can actually increase total virgin material usage in some cases. In an extension, we show how the results and insights change when the rate of innovation is uncertain.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".