The role of university‐industry research centers in embedding foreign subsidiaries: Insights from automotive research and development in Ontario
Bibliographic record
Abstract
Abstract Regionally embedded foreign subsidiaries have an advantage over disembedded ones. The ability to draw on local knowledge networks to create differentiating capabilities can enhance the subsidiary's influence within the multinational enterprise. A subsidiary's power is shaped by its control over external resources or skills the parent company depends on to develop new products. These local relationships are then used to bargain for further mandates, sometimes in competition with sister subsidiaries. One way in which foreign subsidiaries can deepen their connections with the local context is by collaborating in their innovative efforts with university‐based collaborative research centers (CRCs). I argue that CRCs are not a panacea when it comes to embedding the subsidiaries of foreign multinational enterprises (MNEs). A review of the literature helps identify the subsidiary‐parent relationship and industry‐academia conflicts of interest as obstacles to the development of productive relationships between subsidiaries and academia. I draw on evidence from three Ontario CRCs, with different scientific focus and stakeholders and analyze how these factors shape their behavior. The study shows that long‐term CRCs‐subsidiary relationships can be challenging to establish. This is because subsidiaries remain tightly controlled by their parent companies and cannot get access to the resources necessary to pursue ambitious innovation projects. It is also challenging for subsidiaries to form long‐term relationships with CRC researchers, who prioritize other goals such as training skilled personnel and continuing their research agenda. CRCs where 1) subsidiaries have a strong bargaining position within the MNE, and 2) academic expertise is aligned with industry needs have a greater chance to contribute to future MNE embeddedness.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".