Using R&D portfolio management to deal with dynamic risk
Bibliographic record
Abstract
We develop a theoretical framework for understanding why firms adopt specific approaches for the management of innovation project portfolios. Our theory focuses on a key contingency factor for innovation, namely the dynamics of competitive environments. We use four dimensions to characterize the patterns of environmental dynamics: velocity, turbulence, growth and instability. The paper then proposes the concept of dynamic risk as a determinant of portfolio management processes. Dynamic risk results from second‐order learning by a firm confronted with a specific dynamic pattern in its environment. This learning concerns the likely nature of threats and the required updating of cognitive frameworks in such environments. Attempts to deal with dynamic risk enable various actors inside the firm to understand what kind of dynamic capabilities are needed in their innovation portfolio management processes. As a result of this diffuse learning, firms tend to favor certain common characteristics in their concrete portfolio management activities. To advance the theorizing of these characteristics, the paper also proposes four dimensions of portfolio management: structure, commitment, emergence and integration. Based on arguments inspired by the dynamic capability and related literatures, we advance a series of hypotheses, that relate environmental dynamics dimensions and portfolio management dimensions. These hypotheses are tested based on a survey of 795 firms in a variety of sectors and on four continents, using original scales and structural equation modeling methods. The results show, among other findings, that high‐velocity environments favor structured as well as integrated portfolio management approaches, while high‐growth environments favor approaches that are structured but commit significant resources to each project as well. Turbulent environments favor approaches that are emergent, but also, contrary to our expectations, have high resource commitment levels. Finally, firms in unstable environments have a marginal preference for emergent approaches. Results could help advance the dynamic contingency theoretical perspective on dynamic capabilities, as well as improve the practice of innovation portfolio management.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".