Managing the New Product Development Project Portfolio: A Review of the Literature and Empirical Evidence
Bibliographic record
Abstract
Literature on project portfolio management (PPM) has been escalating as interest has intensified. The surge of interest has been attributed to the increased importance of technological innovation and the recognition that successful innovation depends upon effective selection and management of the new product development (NPD) project portfolio. PPM processes are responsible for the alignment of projects with the innovation strategy, maintaining a balance of project types, and ensuring that the project portfolio fits with resource capability so that the organization can gain the maximum value from the investment in NPD. This is the first comprehensive review of the literature on NPD PPM to be published and reveals a wide range of considerations from a variety of sources across several disciplines. The growing importance of NPD PPM is highlighted, and interest in PPM is shown to have stimulated a field of research that is beginning to offer empirical findings to help clarify the relationships between PPM methods and NPD outcomes. Findings reported in the empirical literature are compared with the common beliefs and assertions presented in other published sources. The empirical findings show support for some assertions, and challenge others, while some proposed relationships remain untested.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".