Asymmetries in Firm-Level Globalization: The Case of Swiss Multinational Enterprises
Bibliographic record
Abstract
Abstract This paper addresses the regional and global strategies of multinational enterprises (MNEs), with an application to the largest Swiss companies. We extend Rugman and Verbeke’s (2004) classic approach to measure MNE globalization by adopting a multidimensional lens, whereby we focus on four distinct parameters that evaluate respectively: market success across geographic space (proxied by sales); investments as a response to foreign business opportunities (proxied by assets); human capital (as proxied by the employees’ geographic distribution); and knowledge capital (as measured by patented innovations). We observe substantial discrepancies in globalization levels according to the parameter used. According to this study, the largest segment of companies (42.1%) remains home-regional in terms of sales. Bi-regional firms constitute the second largest category, comprising 28.9% of the sample. Only 21.1% of the companies can be classified as global in terms of sales distribution. Upstream activities such as knowledge capital seem to be more home-region oriented than downstream activities. One critical conclusion of this study is that not a single large Swiss MNE can be considered global in terms of knowledge capital creation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".