Headquarters‐subsidiary knowledge strategies at the cluster level
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Research Summary This article examines how multinational enterprises (MNEs) leverage knowledge across clusters. Based on the geographical sources and the contextuality of knowledge, we construct a typology of four MNE knowledge strategies across space: replicating, scouting, connecting, and integrating, and take into consideration their spatial, industrial, and leadership contexts. A fuzzy‐set qualitative comparative analysis of 49 pairs of headquarters‐subsidiary linkages between Canada and China suggests that replicating strategies occur in cluster‐to‐non‐cluster contexts or in fields with a knowledge gap between the two countries, whereas scouting strategies are typical in non‐cluster‐to‐cluster investments. Connecting and integrating strategies are focused on cluster‐to‐cluster contexts. We also find that while connecting occurs in fields where knowledge is locally bounded, integrating takes place in nonlocally bounded contexts. Finally, scouting and integrating strategies are associated with home nationals as subsidiary leaders. Managerial Summary How do multinational enterprises (MNEs) transfer knowledge over space between clusters and between other locations? To explore this question, we construct a typology of four MNE knowledge strategies (replicating, scouting, connecting, and integrating) and examine the spatial, industrial, and leadership conditions of each. By examining 49 headquarter‐subsidiary linkages between Canada and China through detailed interviews, we find that replicating strategies occur in cluster‐to‐non‐cluster contexts or industries with a knowledge gap between the two countries, whereas scouting strategies are typical in non‐cluster‐to‐cluster investments. Connecting and integrating strategies are focused on cluster‐to‐cluster contexts. We also find that while connecting occurs in fields where knowledge is locally bounded, integrating dominates where this is not the case. Finally, scouting and integrating strategies are associated with home nationals as subsidiary leaders.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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 it