Bibliographic record
Abstract
This paper builds on the principles and insights from improvisational theater to unpack the nature of collective improvisation and to consider what it takes to do it well and to innovate. Furthermore, we discuss the role of training in enhancing the incidence and effectiveness of improvisation. We propose that two common misconceptions about improvisation have hindered managers’ understanding of how to develop the improvisational skill. First, the spontaneous facet of improvisation tends to be overemphasized, and second, there is a general assumption that improvisation always leads to positive performance. Our goal is to clear up the conceptual confusion about improvisation by laying out the various aspects of preparation that are required for effective improvisation. In our theoretical model, we delineate how the improvisational theater principles of “practice,” “collaboration,” “agree, accept, and add,” “be present in the moment,” and “draw on reincorporation and ready-mades” can be used to understand what it takes to improvise well in work teams and to create a context favoring these efforts. Our findings support a contingent view of the impact of improvisation on innovative performance. Improvisation is not inherently good or bad; however, improvisation has a positive effect on team innovation when combined with team and contextual moderating factors. We also provide initial evidence suggesting that the improvisational skill can be learned by organizational members through training. Our results shed light on the opportunities provided by training in improvisation and on the challenges of creating behavioral change going beyond the individual to the team and, ultimately, to the organization.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".