Real‐option valuation of research and development investments
Bibliographic record
Abstract
Purpose Aims to promote an integrated performance measurement system. Design/methodology/approach The literature on R&D performance measurement identifies the need for an integrated performance measurement system for strategy implementation. Develops a theoretical framework for R&D performance measures, incorporating real options to define strategic net present value, which values the plan to make R&D investments. Findings Real options techniques can be used to value managers' options to shelter investments from adverse effects and exploit upside potential. The shift in valuation paradigms from a naïve net present value model to active risk management implicit in real options requires performance measures that reflect real option value and defines strategic value created (SVC), which is based on residual income concepts. Since residual income is known to be superior to ROI in motivating goal congruence, infers that SVC has similar advantages. Originality/value Illustrates how SVC would be used as a performance measure for a new drug in the commercialization stage, considers several relevant questions and discusses how SVC could be used in a firm's balanced scorecard.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".