The Effect of Competition on R&D Portfolio Investments
Bibliographic record
Abstract
Although project portfolio management has been an active research area over the past 50 years, budget allocation models that consider competition are sparse. Faced with the competition, firms contemplating budget allocation for their project portfolio cannot limit their attention to the returns from their projects' target markets, as is the case for monopoly firms, but must also anticipate the competitive effects on these returns. Assuming firms allocate their budgets between projects offering incremental innovation targeting a mature market and projects offering radical innovation targeting an emerging market, we show that while the monopoly firm bases its budget allocation decision solely on the marginal returns of the markets, competing firms—as they take into account their counterparts' investment decisions—need to also consider the projects' average returns from their respective markets. This drives competing firms into incrementalism: faced with competition, firms invest larger portions of their budgets into projects targeting mature markets. This effect is amplified as the number of competing firms increases and firms allocate an even greater share of their budget into projects targeting a mature market. We further demonstrate the effects that changes to firms' individual budgets, as well as to market characteristics, have on firms' budget allocation decision.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".