Search and Integration in External Venturing: An Inductive Examination of Corporate Venture Capital Units
Bibliographic record
Abstract
Research summary How do external venturing units effectively achieve external knowledge search and integration of their initiatives with mainstream organizational units? We investigate this largely unexplored question through an inductive study of 17 corporate venture capital units. We document a set of five novel practices that influence the efficacy of a unit's external search and internal integration and identify how these practices complement a broader set of practices used by all units. We highlight the entrepreneurial nature of managing an external venturing unit, often to overcome unfavorable corporate contexts, a perspective that prior research has largely overlooked. Our findings provide unique insights into why some corporate investors are better at learning from external start‐ups than others. Managerial summary External venturing involves strategic partnerships by established firms with entrepreneurial ventures. Top management usually tasks autonomous units with searching for willing and potentially valuable partners. These units must integrate their activities with the operations of parent firms to elicit cooperation from important business units. To understand how external venturing units implement search and integration in combination, we study corporate venture capital ( CVC ) units, which form external partnerships through minority investments in start‐ups. While all units adopted fundamental processes that are well established in the venture capital community, certain processes that are idiosyncratic to corporate investing helped units demonstrate superior performance in their strategic missions. These processes often required CVC unit managers to be entrepreneurial and politically savvy in building connections with relevant personnel in parent firms. Copyright © 2015 Strategic Management Society
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".