Division Headquarters Go Abroad â A Step in the Internationalization of the Multinational Corporation
Bibliographic record
Abstract
In 1986, SKF, the Swedish ball-bearing company was reorganized into a multi-divisional structure (M-form) and a speciality bearing division was formed. At the same time the US ball-bearing company MRC with a strong position in some speciality bearing lines was acquired. The headquarters (HQ) of the speciality bearing division were placed in the USA with the executive manager of the former North American regional HQ as division executive. Also in 1986, Alfa Laval, the old Swedish multinational corporation (MNC) with roots in separator technology, was divided into 12 divisions. The German subsidiary Bran+Luebbe GmbH became a division of its own headed by the former subsidiary executive with HQ in Germany. Further, in the 1980s the packaging division of SCA, one of the leading Swedish pulp and paper corporations, expanded rapidly in the European market through a number of foreign acquisitions. In 1989, division HQ were moved from Sundsvall in Sweden, where corporate HQ were situated, to Brussels in the new centre of their European market. The top management was unsatisfied with earlier attempts of managing at a distance. Thus, when foreign operations increased and became a larger part of the division’s total operations it was decided to move division HQ abroad.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.081 | 0.031 |
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".