Internal Knowledge Development and External Knowledge Access in Venture Capital Investment Performance
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 We examine the performance effects of two knowledge‐driven strategies – internal knowledge development and external knowledge access through inter‐firm relationships – in the context of venture capital investing. Using longitudinal data on the investments, syndication, and performance of 200 US‐based venture capital firms, we find that investing in industries in which a firm has more knowledge and investing with more or familiar external partners enhances investment performance. In addition, we reveal important interactions between the two strategies, such that access to external knowledge is particularly beneficial when the investment exposes gaps in the firm's own expertise. Thus, access to external knowledge is more effective when an incongruity exists between what the firm knows and what it intends to do. We discuss the study's implications for organizational knowledge and learning, strategic alliance, and venture capital literature.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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