FDI and cities: network dynamics in cleantech innovation
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the relationship between foreign direct investment (FDI) and cities and how the relationship between multinational enterprise (MNEs) and local firms facilitates regional cleantech innovation. Design/methodology/approach Using a combination of social network analysis, regression analysis and interview analysis, the authors map and analyze a cleantech cluster to investigate the relationship between MNEs and local firms and the resulting effects on cleantech innovation. Findings The findings of the paper indicate that FDI plays a crucial role in cities and their local clusters by acting as a broker between a diverse set of actors: firms, institutions, universities, financial and other intermediaries. Additionally, connectedness to MNEs improves local firms’ innovation. Research limitations/implications This study is not free of limitations, mainly, because of the aspects that the analysis is based on one city and one cleantech hub. Further research could verify whether the findings of this paper hold in other cities and industries. Practical implications The findings, elucidating the connection between MNEs and local firms, as well as MNEs being important brokers in the local system, and the resulting impact, will help policymakers to take appropriate actions and support the local cleantech innovation. It is important to not only attract high-quality FDI into local clusters, but also to create and support collaborations between foreign firms and local actors, because colocation does not automatically leads to positive spillovers and a lot depends on how MNEs are integrated into the local milieu. Social implications The present paper argues that FDI plays an important role in local cleantech innovation and it is important to integrate foreign firms in local social networks. Originality/value The authors analyze FDI patterns in an emerging industry at the city and local cluster level using a unique database containing the information on relationships between MNEs and local firms, as well as interview data.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".