Backing outsiders: selection strategies for discontinuous innovation
Bibliographic record
Abstract
A key challenge in managing innovation is to explicitly identify ways to improve an organization's performance with regard to discontinuous innovation. However, discontinuous innovation does not fit the existing ‘frame of reference’ and hence requires a reframing of the traditional ways of innovating within the organization. More specifically, previous research shows that practices that work well in the context of incremental innovation do not work in the context of discontinuous innovation. Thus, the aim of this paper is to explore innovation practices that enable organizations to select innovation projects, which are ‘outside the box’ of its prior experience, i.e. are discontinuous in nature. Building on the experience of more than 150 firms across 12 countries, we have identified nine innovation practices for the selection of discontinuous innovation; these can be grouped into three clusters: enable, engage and experience. In sum, we identify that an organization needs to acknowledge that its choice to engage in discontinuous innovation will have consequences for the innovation practices chosen to select which discontinuous projects to carry forward.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".