Collaboration Between Technology Entrepreneurs and Large Corporations: Key Design and Management Issue
Bibliographic record
Abstract
The 1990's witnessed an explosive growth in strategic alliance activity. Today, strategic alliances are a central element in the growth strategies of technology entrepreneurs and emerging technology companies. In many instances, these alliances involve a large corporate partner. This phenomenon has been accelerated by the explosion in corporate venturing activity in recent years. On a conceptual level, alliances between small entrepreneurial firms and large corporations can provide significant benefits to both parties. A small technology company can leverage the research, manufacturing, marketing and financial resources of the large partner while the latter can tap into the innovative capacity of the smaller partner. On a practical level, however these alliances pose some significant design and management challenges. This article examines these challenges and outlines actions that the technology entrepreneur can take to respond to them.
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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 | 0.073 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".