Knowledge transfer in knowledge-intensive organizations: the crucial role of improvisation in transferring and protecting knowledge
Bibliographic record
Abstract
Purpose This paper aims to answer the question: how do knowledge workers’ improvisation processes promote both knowledge transfer and protection in knowledge-intensive organizations (KIOs)? A model is proposed identifying how effective improvisation can strengthen the effect of four specific knowledge transfer mechanisms – an experimental culture, minimal structures, the practice of storytelling and shared mental models – on knowledge transfer inside the organization and knowledge protection outside of it. Design/methodology/approach The paper builds on a knowledge translation perspective to position improvisation as intrinsically intertwined with knowledge transfer and knowledge protection. Findings Improvisation is proposed as the moderating factor enhancing the positive impact of an experimental culture, minimal structures, storytelling practice and shared mental models on knowledge transfer and knowledge protection. Practical implications The paper argues against a “plug-and-play” approach to knowledge transfer that seeks to replicate knowledge without considering how people relate to the routines and the context and highlights to leaders of KIOs the importance of developing awareness, understanding and motivation to improvise to internalize new knowledge being transferred and to create imitation barriers. Originality/value The paper proposes that KIOs’ success in transferring and protecting knowledge emerges not directly from formal knowledge transfer mechanisms but from knowledge workers’ improvisation processes.
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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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".