Work Systems in Heavy Engineering: The Role of National Culture and National Institutions in Multinational Corporations
Bibliographic record
Abstract
This paper is based on an Anglo-German research project of two research groups in both countries. It is based on data collected by qualitative research in the three largest multinational corporations (MNCs) in the lift and escalator industry. The headquarters (HQs) of the three corporations are based in the United States, Finland and Germany, respectively, and all three MNCs each have subsidiaries in Germany and Britain. Our main objects of analysis were change processes in the work systems of these three MNCs. We chose the lift and escalator industry as an example because it has been characterized by strong concentration processes during the last 10 years. Most of these corporations have grown by acquisition and there are strong tendencies in the market towards standardized, globally uniform products. National cultures and institutions, first of all play a role on the HQ level. Important areas were the standardization of products and production technology, the design of management systems and location and relocation decisions for R&D and manufacturing. Second, MNCs take differences in national cultures into account and deliberately "use" them in allocating resources and investment within the multinational group. National cultures and institutions massively shape the very formulation of manufacturing strategies within the multinational groups, as well as the R&D strategies--a particular important field in an industry still relying heavily on small-batch and unit production. National cultures also play a significant role in implementing the global strategies of MNCs in different host countries. Our data reveal striking differences on this level.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".