Bibliographic record
Abstract
Innovation Reinvented: Six Games to Drive Growth Roger Miller and Marcel Cote (Toronto, Ontario: Rotman- UTP Publishing, 2012) In Innovation Reinvented, Roger Miller and Marcel Cote reinvent the understanding of innovation processes by recognizing that innovation is a fundamental characteristic of, and driven by, maturing business models. Although this idea is in conflict with the classic paradigm of innovation, it is well supported by data gathered in a global survey of innovative businesses, conducted with the support of the Industrial Research Institute (IRI). (1) Analyzing these firms' approaches to innovation, Miller and Cote found that the classic model was of little use in understanding actual innovation patterns. What mattered most were customer interactions, marketing, and change management. These factors drive distinct patterns of innovation, characterized as competitive games. Managing RD the first introduces the six games of innovation, the second gives a detailed description of each of the games, and the third describes how the games may be applied to RD other attributes vary by the kind of innovation: * Autonomous products. These are innovations characterized by new, standalone products, such as a new blockbuster drug, a solar panel, or a Cirque du Soleil performance. These innovations are often science- or technology-based, and brand identity and patents are usually used to fend off emulators. Marketing is critical. * Platforms. In this instance, the innovation is a new system, like Microsoft Office, smart phones, or televisions. Success in this field depends upon vision, network effects, and coalition building. Venture capitalists are often key to funding early work. * Closed systems. Innovations falling into these games are characterized as system breakthroughs. Examples are integrated accounting software or electric car batteries. These industries, which are based on collaboration between an inventor and a demanding client, are characterized by an intense, high-risk development environment. …
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".