Bibliographic record
Abstract
How does a firm decide which products to introduce when faced with the cannibalization of profits from its current offerings? In a simple spatial model of imperfectly substitutable products, we show that when the cost of product development is fixed, the firm must take into account the impact on both the lowered profits of its current offerings and the profits from the new product. As a result, there is a gap between the firm's offerings. The substitutability of the products, the cost of product development and the magnitude of demand all impact this distance. When the cost of product introduction increases with the distance between the old and new products, the firm will offer those products that are sufficiently far away from its current offerings, but will not offer a new product if it is \too far away from its current offerings. Hence, the range of its possible offerings is smaller. The fixed cost of product development, the substitutability of the products and the magnitude of demand help to determine the gap between the products and the increased cost of product development due to fit limits the range of product offerings. We investigate the impact of external competition and show that firms will introduce new products further from their current offerings when there is a threat of competition. When the firm cannot prevent a spin-out, it is more likely to introduce a new product, and to discard a new product even
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".