Managing knowledge, creativity and innovation
Bibliographic record
Abstract
The place and role of managing creativity in organizations appears as a growing concern amongst scholars as well as practitioners. The aim of this chapter is to situate and analyze how managing creativity should fit into the organizational framework orchestrated by the interactions between the management of knowledge and the management of innovation. In this contribution, we question the traditional view that places creativity at the preliminary stage of the innovation process. Following pioneering works on the management of creativity, we suggest in the following that managing creativity is equivalent to managing ideas, and argue that the main theoretical obstacle is that at the present stage ideas are mostly “black boxes” in innovation theories. In an effort to “open this black box”, we come to the suggestion that a major change of perspective is needed in management: instead of viewing the management of ideas as an initial stage of the innovation process, we propose an integrated framework where the processes of ideation and innovation are not sequential but coupled, and where these strategic interactions are mediated by knowledge-management processes. Such a change of perspective suggests drastic impacts on the ways to manage organizations, which are discussed in the conclusion of this chapter.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".