How Salieri Beats Mozart: Socio-Political Process of Bridging Creativity and Innovation
Bibliographic record
Abstract
Ideas successfully generated within firms do not always result in implementation. Drawing upon a rich body of research on creativity and innovation, this study delves into the socio-political process of intrafirm innovation to propose how ideas evolve into innovation. We argue that idea creators can leverage their social ties with top management team (TMT) members to efficiently capture managerial attention, garner political support, and streamline the decision-making process. Thus, even when possessing comparable attributes to other ideas, the ideas of creators who have close social connections with politically powerful TMT members are more likely to be selected and implemented within their firms. Moreover, we highlight that the uncertainty regarding the potential returns of ideas is a boundary condition of the socio-political process of intrafirm innovation. Specifically, we argue that the socio-political process becomes more pronounced when implementing exploratory ideas with uncertain returns. In contrast, it is less evident when creators build their ideas upon star creators to mitigate the associated uncertainty. We find supportive evidence from the intrafirm collaboration network for patenting activities. This study provides valuable insights into why some creative ideas get stuck while others are implemented.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".