Firm Matching in the Market for Technology: Business Stealing and Business Creation*
Bibliographic record
Abstract
The combination of existing technologies is necessary for innovation. An important aspect of technology exchange between firms is that it generates both business creation and, possibly, business stealing. These countervailing effects generate potentially misaligned interests between technology adopters and providers. We propose an empirical framework for studying the prevalence of business creation and business stealing in technology transfers from the effect of technological overlap and product market overlap. A fundamental identification challenge is that product market overlap could conflate business creation effects. We propose two new asymmetric measures that allow us to separately identify the business stealing and business creation effects of product market overlap. We estimate the model on a new dataset that tracks interactions in the market for technology across a broad range of exchange modes between publicly held US companies. We obtain two main findings. First, product market overlap has a negative effect on matching patterns that is suggestive of business stealing. Second, technological proximity has a positive effect on matching patterns that is consistent with business creation. We use our results to assess the relevance of IP rights in deterring undesirable technology adoptions and discuss the suitability of alternative strategies of technology exchange.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".