Developing radical innovation capabilities: Exploring the effects of training employees for creativity and innovation
Bibliographic record
Abstract
The resilience of organizations is increasingly dependent on their ability to develop radical innovation capabilities. While the literature documents numerous cases of organizations that already have radical innovation capabilities, the question of organizational devices that can be used to stimulate the emergence of such capabilities remains poorly addressed. Specifically, training for innovation and creativity has been proposed as a means to foster innovation capabilities; however, there has been little empirical evidence concerning the long‐term impacts of such training. To fill this gap, this article aims to document and evaluate the efforts of the research institute of a major Canadian energy company to provide training for innovation and creativity to initiate a radical innovation capability. We rely on a longitudinal study over the span of 18 months, where we observed 128 h of training and conducted 70 semi‐structured interviews with a sample of 40 researchers. We found that training for creativity and innovation has the potential to develop individual creative skills for exploration, to catalyze and federate collective action through common methods and a shared sense of what innovation entails, and to help create a common language and vocabulary between the different groups or divisions of an organization to talk about exploration.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".