Effects of R&D Investments and Market Signals on International Acquisitions: Evidence from IPO Firms
Bibliographic record
Abstract
We investigate how intangible assets in the form of R&D influence firms’ hazards of engaging in international acquisitions. On the one hand, previous research has noted that the tacit and redeployable nature of R&D investments may prompt firms to expand their operations overseas and create value from international acquisitions. On the other hand, it is difficult for other firms to evaluate the quality and prospects of an acquirer’s intangible resources, thereby hampering its ability to finance and execute international M&A deals. In the context of international acquisitions undertaken by firms just completing their initial public offerings (IPOs), we argue and find that the IPO firm’s engagement in post-IPO international acquisition activity is generally negatively related to its R&D intensity. This effect contrasts previous arguments on the internalization advantages possessed by R&D-intensive firms. We also argue that firms able to convey their resources and prospects through such signals as previous international alliances and foreign sales can mitigate information problems presented by their intangibles, and thus carry out and benefit from cross-border acquisitions. We therefore identify an unexamined tradeoff that R&D investments present in the international M&A context and discuss how international signals can facilitate cross-border transactions subject to various market frictions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".