Corporate venture capital and interfirm rivalry: A competitive dynamics perspective
Bibliographic record
Abstract
Abstract Research Summary This study views corporate venture capital (CVC) investment as a form of inter‐firm rivalry. Adopting a competitive dynamics perspective, we argue that when a focal corporate investor invests in an entrepreneurial venture, that investment sends important competitive signals to its rivals, thereby increasing their likelihood of initiating a matching response. We theorize how three factors characterizing such investment—the amount of funding, industry relatedness between the corporate investor and the entrepreneurial venture, and the reputation of the corporate investor—can influence rivals' awareness of competitive threat, their motivation to respond, and therefore their likelihood of launching a matching counterattack. Our results demonstrate substantial support for our theoretical model. Managerial Summary This study views CVC investment as a form of competitive interaction, arguing that when a corporate investor participates in an investment round, it sends a competitive signal to its rival, motivating the latter to respond by also investing in CVC. Because of this counteraction, the competitive advantages of firms' CVC strategies may be temporary as rivals catch up and nullify the benefits of a CVC initiative. Thus, when planning strategy, CVC managers need to take potential rival counteractions into account and carefully assess the competitive implications of their CVC strategy, perhaps by avoiding harmful counteractions through initiatives more subtle in execution and orientation, and thus “under the radar” of rivals.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".