Why Are Some Subsidiaries of Multinationals the Source of Novel Practices while Others Are Not? National, Corporate and Functional Influences
Bibliographic record
Abstract
It has frequently been argued that multinational companies are moving towards network forms whereby subsidiaries share different practices with the rest of the company. This paper presents large‐scale empirical evidence concerning the extent to which subsidiaries input novel practices into the rest of the multinational. We investigate this in the field of human resources through analysis of a unique international data set in four host countries – C anada, I reland, S pain and the UK – and address the question of how we can explain variation between subsidiaries in terms of whether they initiate the diffusion of practices to other subsidiaries. The data support the argument that multiple, rather than single, factor explanations are required to more effectively understand the factors promoting or retarding the diffusion of human resource practices within multinational companies. It emerges that national, corporate and functional contexts all matter. More specifically, actors at subsidiary level who seek to initiate diffusion appear to be differentially placed according to their national context, their place within corporate structures and the extent to which the human resource function is internationally networked.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".