Bibliographic record
Abstract
Many executives see innovation as an unmanageable process, riddled with risks. The research we conducted with the Industrial Research Institute, interviewing over 200 vice-presidents of research and development and chief technical officers in many sectors around the world, yields a more nuanced view. Innovation becomes manageable once managers move away from normative prescriptions that view the process as uniform and recognise that different rules and practices apply to different circumstances. Our argument is that clusters of interdependent firms contributing to the building of a set of interacting products and services tend to self-organise themselves into distinct and relatively persistent "games of innovation". Such games operate at a meso level of analysis, grouping together many complementary agents, such as competitors, suppliers, public regulators, universities, innovation-support agencies, and venture capitalists. Six games of innovation, each with a distinct set of rules for innovating, have been identified around value-creation exchanges between buyers and sellers. Three games focus on market creation: "patent-driven discovery", "systems integration" and "platform orchestration". Market maintenance games are "cost-based competition", "systems extension and engineering" and "customised mass production". The perspective proposed in this paper recognises that heterogeneous innovation patterns and strategies can coexist within a single industrial sector and that the same game can be played in many sectors. Specific conditions call for distinct rules and practices. Customer expectations, for example, are central in some games but almost irrelevant in others. Rules for managing innovation are neither generic best practices that can be applied universally nor narrow industry recipes. They are game- and role-specific ways to create and capture market value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".