The role of corporate incubators as invigorators of innovation capabilities in parent companies
Bibliographic record
Abstract
Companies need to rethink their innovation strategies in an increasingly disruptive business environment. The long-term success of large established companies depends not only on their ability to leverage their current capabilities and improve efficiency but also on taking risks and exploring unknown areas. To meet this challenge, established companies are increasingly relying on corporate incubators to fuel innovation and growth with entrepreneurial mindset. Drawing on Zollo and Winter's (Organization Science_13:339-351, 2002) deliberate learning model in conjunction with Christensen's [Christensen, C.M., Anthony S.D., and Roth E.A, Seeing What’s Next? Using the Theories of Innovation to Predict Industry Change, 2004] resource-processes-values (RPV) theory, this paper attempts to answer the question “how can the entrepreneurial mindset fostered in corporate incubators drive the innovation capabilities in parent companies?” The study of four corporate incubators set up by companies from different industries reveals several factors that enable the entrepreneurial spirit fostered by corporate incubators to boost the innovation capability in their parent companies. These factors comprise the recruitment of employees with entrepreneurial potential, investments in knowledge articulation and codification, and a leadership that legitimizes the incubator as a means for the company to develop new ideas and provide support to entrepreneurs inside 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".