Organizing for innovation: A contingency view on innovative team configuration
Bibliographic record
Abstract
Abstract Research Summary While innovation has increasingly become a collaborative effort, there is little consensus in research about what types of team configurations might be the most useful for creating breakthrough innovations. Do teams need to include inventors with knowledge breadth for recombination or do they need inventors with knowledge depth for identifying anomalies? Do teams need overlapping knowledge to integrate insights from diverse areas or does this redundancy hamper innovation by creating inefficiencies? In this article, we offer evidence that the answers to these questions may depend on the characteristics of the technologies. Focusing on the degree of modularity and the breadth of application in patent data, we identify empirical patterns suggesting that differing team configurations are associated with different technological domains. Managerial Summary While innovation has increasingly become a collaborative effort, there is little guidance for managers about how you can construct teams to create novel breakthroughs. Who should be on the team? Some have suggested that inventors should have broad knowledge in order to facilitate the recombination of ideas, which is at the heart of creativity. Others suggest that only deep knowledge in an area can lead to novel solutions. How much diversity in backgrounds is useful? Some find that inventors need to have common knowledge in order to integrate their insights. Others worry that this redundancy will lead to inefficiencies that slow down innovation. In this article, we resolve these conflicting recommendations by showing that the team you pick depends on the type of technology.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".