Divergent Norwegian and North American HRM Regimes: Implications for Norwegian MNEs
Bibliographic record
Abstract
As Norwegian companies internationalize by establishing major business units in a variety of locations such as North America they have to confront different local human resource management (HRM) policies and practices. These differences are not arbitrary but products of different industrial relations regimes. Using a comparative data set the initial purpose of this chapter is to assess the ‘distance’ between the Norwegian and the North American HRM regimes in terms of ‘calculative’ and ‘collaborative’ HRM practices (Gooderham, Nordhaug & Ringdal, 1999). In line with measures of institutional and cultural distance our findings indicate substantial differences. Thereafter we employ interview data to investigate how these differences have an impact on the selection of HRM practices in the North American operations of a Norwegian multinational enterprise (MNE). In particular we investigate the degree to which the Norwegian MNE ‘exports’ Norwegian HRM practices and the degree to which it succumbs to local pressures to adapt to the North American context. We conclude by discussing the implications of our findings for the HRM strategies of Norwegian companies in the North American setting. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".