Building External Corporate Venturing Capability*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT How firms build new capabilities to adapt to changing environments is at the core of strategic management. However, research has addressed this question only recently. In this paper, I propose a model that describes how firms develop a capability to create and develop ventures through corporate venture capital, alliances, and acquisitions. The model is based on two longitudinal case studies of large corporations operating in the information and communication technology sector in Europe. At the core of this model are learning processes that enable the firm to build up an external corporate venturing capability, by utilizing learning strategies both within and outside venturing relationships. To build this new capability, firms engage in acquisitive learning. Critical to deepening the capability acquired is adaptation of all knowledge to the firm specific context through experiential learning mechanisms. I also discuss the important role that initial conditions and knowledge management practices play in determining the direction and effectiveness of specific learning processes that lead to an external corporate venturing capability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it