Locations, city connectivity and innovation zones in China: a dynamic perspective of knowledge community
Bibliographic record
Abstract
Purpose This study aims to examine two innovation zones in China, including the Suzhou Industrial Park and Tianjin Eco-city, to gain a comprehensive understanding of city locations attributes and its relationship to inward foreign direct investment (FDI) from multinational enterprises (MNEs) in innovation zones embedded in nonhub cities in China. Design/methodology/approach This research incorporates two site visits and in-depth interviews with 39 personnel working with innovation zones. Thematic analysis is used to analyze interview data and documents. Findings The results highlight that cities can use innovation zones as a strategy to build high scale knowledge community precincts to connect MNEs and other global actors. As an important institutional feature of city locations, innovation zones increase within-city connectivity and connect cities in global networks resulting in cross-city connectivity to attract FDI from MNEs. From a dynamic knowledge community perspective, this research also compares active and passive approaches toward building knowledge communities and identifies several elements of knowledge communities within innovation zones in China. Research limitations/implications The research results could be further explored in other institutional and economic contexts, to understand the interplay of city locations, FDI and innovation zones, and the dynamics of building knowledge communities. Practical implications This research has several implications for policymakers and administrators who work with municipal economic development and the development and enhancement of innovation zones. It offers recommendations for MNEs to consider where to make foreign investments and the advantages innovation zones may offer to support FDI. Originality/value This research contributes to the literature related to economic development and how nonhub cities can attract FDI and join global networks. It offers empirical insights drawn from two successful innovation zones located in nonhub cities in China.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.000 |
| 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".