Platforms without borders? The international strategies of digital platform firms
Bibliographic record
Abstract
Research Summary : Digitalization has enabled firms with so‐called platform business models to emerge in many sectors of the economy. By facilitating transactions between different groups of users (e.g., buyers and sellers), platform firms are disrupting industries around the world. However, little is known about the international strategies of platform firms, as research has mostly examined platforms in single‐country contexts. We address this gap by integrating insights from platform research in strategy and economics—specifically the notion of network externalities—with internalization theory. We extend the existing typology of network externalities by distinguishing between within‐country and cross‐country network externalities. We derive testable propositions regarding the foreign entry modes of platform firms, their international strategic posture (multidomestic vs. globally integrated), as well as foreign market selection criteria and market exit. Managerial Summary : Many companies in the digital economy operate platform business models, which create value by connecting different groups of users, such as buyers and sellers. We examine how network externalities—the notion that a platform becomes more valuable to each user as more users join—influence the international expansion of these firms. We show that it is important to consider the geographic scope of network externalities, that is, whether network externalities operate across national borders or whether platform firms have to create separate user networks in each country. The distinction between within‐country and cross‐country network externalities affects key internationalization decisions, such as how to enter foreign markets, whether to pursue multidomestic or global strategies, how to select foreign markets, and when to exit from a foreign market.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".