To Collaborate or Not: Product Upgrading Strategy in a Competitive Duopoly Market
Bibliographic record
Abstract
Releasing an upgraded version of a product is a common tactic that firms use to maintain competitiveness. However, in light of the significant research and development investment required to upgrade products, a new industry trend has emerged in which incumbents collaborate with market entrants to innovate products. At the same time, such collaboration may lead customers to think that the upgraded products are more similar than those innovated without collaboration. Consequently, we consider the following tradeoff: collaboration allows firms to share the innovation investment, but leads to less differentiated products. Specifically, we consider a two-period model where the incumbent is a monopoly during the first period. This incumbent and a market entrant decide whether or not to collaborate to innovate their products, and the two firms sell their products during the second period. We find that both firms can benefit from a higher innovation cost. Moreover, the market entrant can be better off when the products become less differentiated due to collaboration, or when customers are impatient to buy in the second period. Finally, we find customers can be worse off when there is a lower innovation cost or more differentiated products.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".