Design‐Led Innovation: A Framework for the Design of Enterprise Innovation Systems
Bibliographic record
Abstract
Innovation is not business as usual. Many enterprises struggle to build the systems necessary to consistently deliver new and improved sources of value to customers and stakeholders. Through a thematic analysis, expert interviews, systems mapping, and a case study with the $100B Ontario Municipal Employees Retirement System (OMERS), this paper presents a framework for the design of enterprise innovation systems, called the innovation systems design cycle (ISDC). To apply the ISDC, innovators iteratively plan, build, check, and refine innovation systems. The ISDC framework is detailed with new models exploring innovation system mapping and implementation, modes to assess and compare an innovation system’s development, and configurations to support rapid, adaptable design. Together they support innovators of all experience levels in applying the ISDC to design more resource‐efficient innovation systems with a greater capacity to shape an innovation ecosystem and avoid enterprise disruption. The ISDC can be used to build or enhance an innovation system, benchmark performance, frame best practices, quantify value creation potential, demystify innovation systems design, and democratize innovation across not‐for‐profits and other organizations towards a more just, democratic, and sustainable collective future.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".