FROM EXPERIENCE: Disruptive Innovation and the Need for Disruptive Intellectual Asset Strategy
Bibliographic record
Abstract
Disruption has become a popular business term, yet it is often used so loosely as to convey almost nothing of substance. Here a largely neglected factor is addressed: the role of intellectual assets in securing opportunities for or averting threats from disruptive innovations. While the literature explains why the decision-making systems in large established companies cause difficulty in responding effectively to disruptive innovation the generation of intellectual assets (e.g., patents, publications, trademarks) typically is not subject to the same cultural and structural barriers. Though it may be difficult to convince a business to invest millions in pursuit of a speculative disruptive innovation, it is much easier for a small team to gain support in pursuing low-cost intellectual assets in the name of mitigating potential threats. A two-pronged approach is proposed that builds on the authors' experience at Kimberly-Clark Corporation in dealing with disruptive threats and opportunities. The approach calls for generation of intellectual assets, often using small proactive teams, to (1) protect an existing business by reducing competitive risks from disruptive innovation, including the risk of new products with disruptive potential and the risk of associated competitive patents that might limit one's response; and (2) prepare for future new and disruptive business opportunities that could be protected or strengthened by the intellectual assets generated. Kimberly-Clark's growing experience with this approach suggests that it may be a valuable component of one's strategy for innovation and protection of the business.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".