The Innovation Cascade: A Five-Level Framework for Building Enterprise Innovation Systems
Bibliographic record
Abstract
This Major Research Paper (MRP) describes a framework for creating more innovative, lower-cost enterprise innovation systems (EISs). \n \nThrough a literature review, I have identified and described ten driving forces behind the performance of EISs: innovation ecosystems, innovation strategy, enterprise architecture, innovation inputs, the innovation process, portfolio management, innovation working practices, innovation accounting, innovation culture, and innovation tools. \n \nDrawing from the literature, I have gathered and analyzed 250 innovation approaches, such as horizon scanning or value proposition design, to describe the five overarching areas involved in creating EISs: ecosystem, strategy, architecture, people, and infrastructure. Through eleven practitioner interviews and system mapping, I have shaped the five areas into a prototype framework, which I call the Innovation Cascade. The Innovation Cascade provides EIS builders with a process for creating or improving an EIS by framing missing areas or highlighting tensions between the five areas of an EIS. \n \nTo test the Innovation Cascade, I conducted a case study with the Ontario Municipal Employees Retirement System (OMERS). In the case study, I mapped OMERS’s EIS to the Innovation Cascade, designed an EIS research function for OMERS, and offered ten recommendations for improving OMERS’s EIS. Through the case study, I determined the Innovation Cascade is effective for building or enhancing EISs and propose next steps for further refining it. \n \nFinally, I have suggested three other models to augment the Innovation Cascade. First, five modes that EISs can exhibit: informal, linear, distributed, embedded and emergent. Second, patterns or predictable configurations each area can exhibit. Third, five steps that match each area of the Innovation Cascade with appropriate tools and actions. Together, the three models and the Innovation Cascade offer a framework for EIS builders to design, improve, maintain and understand EISs, as well as communicate EISs to stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".