Economies of Scale: The Rationale Behind the Multinationality-Performance Enigma
Bibliographic record
Abstract
Abstract In a widely acclaimed contribution to Management International Review, Hennart (2007) challenged one of the mainstream theories of International Business, the S-curve relationship between multinationality and performance, by arguing that there is no positive impact on performance aside from the scale enhancing effect resulting from increasing multinationality. We examine his arguments by analyzing 3876 firms from Canada, Germany, Japan, the UK and the US over the period from 2002 to 2016. We find that the empirical evidence for a direct positive impact of multinationality on performance is not convincing. However, increasing multinationality leads to a significantly higher firm performance via the economies of scale-channel. Multinationality seems to be more important as a means to increase scale for firms from small home markets compared to firms from large domestic markets. Intangible assets appear to amplify the impact of scale on performance much more than the impact of multinationality on performance. In the end, it's size that matters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".