Headquarters‐subsidiary knowledge strategies at the cluster level
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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".