Platform Venture Capital Investments and the Introduction and Withdrawal of Complementary Products
Bibliographic record
Abstract
Many platform providers are involved in corporate venturing. Increasingly, platform providers stake out minority equity investments in complementors from their own platform ecosystem as an act of ecosystem governance, a practice that we refer to as platform venture capital (PVC). We study how other complementors respond to such investments, as it pertains to their decisions to introduce and withdraw complementary products. Building upon the triadic exchange structure and contingent adoption nature that are characteristic for platforms, we advance theoretical arguments that position PVC as a powerful proxy for customer demand in the platform ecosystem. We explore the implications of PVC for complementary product introduction and withdrawal by assessing the consequences of 24 PVC investments in the context of Salesforce’s platform ecosystem. Consistent with our theoretical arguments, we show that complementors tend to view PVC as a signal of opportunity rather than as a potential threat, such that they are more likely to introduce and less likely to withdraw complementary products in product categories affected by PVC. We also show that these effects are weaker for complementors with greater platform ecosystem experience and multihoming complementors that are better positioned to access information on the preference of platform customers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".