Digital transformation among Chinese listed firms deepens cross-regional innovation ties and raises the quality and number of collaborative patents, with faster gains after a critical mass of digital patenting is reached. Network evolution and stronger inter-city relationships appear to mediate these effects, though the evidence is correlational rather than causally identified.
How Does Digital Transformation Affect Cross-Regional Collaborative Innovation: Evidence from A-Share Listed Firms
Using 2011–2021 patent and digital-transformation data for Chinese A-share firms, the paper finds that digital transformation strengthens cross-regional collaborative innovation networks—boosting both the quantity and quality of joint innovation—and that effects accelerate once digital patenting passes a threshold, partly via greater relational embeddedness among cities.
This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy.
Summary
Main Finding
Digital transformation among A‑share listed firms in China (2011–2021) strengthens cross‑regional collaborative innovation: it increases both the quantity and quality of collaborative patents, accelerates benefits once digital patenting passes a threshold, and does so partly by reshaping city‑level innovation networks (creating small‑world features, a quadrilateral → multicenter spatial structure) and by enhancing relational embeddedness between cities.
Key Points
- Dataset: firm‑level digital transformation indicators and patent data for A‑share listed companies on the Shanghai and Shenzhen exchanges, 2011–2021.
- Network structure: the cross‑regional cooperative innovation network exhibits pronounced small‑world properties (high clustering and short path lengths).
- Spatial evolution: China’s urban collaborative innovation network displays a four‑corner (“quadrilateral”) spatial pattern that has evolved toward a multicenter (polycentric) structure over the sample period.
- Effects of digital transformation:
- Positive impact on the quality and quantity of cross‑regional collaborative innovation.
- Mechanism: digital transformation strengthens relational embeddedness (stronger, repeated intercity ties), improving collaborative outcomes.
- Nonlinearity: once the volume of digital patent applications exceeds a certain threshold, the marginal positive effect on collaborative innovation quality increases (an accelerating effect).
- Contribution: provides empirical evidence from a major emerging economy on how digital transition alters geographic innovation dynamics.
Data & Methods
- Data: digital transformation measures and patenting records for publicly listed (A‑share) firms on Shanghai and Shenzhen stock exchanges, covering 2011–2021.
- Network analysis: characterization of the cooperative innovation network (small‑world metrics, clustering, path length) and mapping of spatial evolution (identification of quadrilateral and multicenter patterns).
- Econometric analysis: tests of the impact of firm/city digital transformation on cross‑regional collaborative innovation outcomes, including mediation analysis for relational embeddedness and threshold (nonlinear) analysis to identify accelerating effects once digital patenting passes a critical level.
- Robustness: multiple specifications and network/space‑aware analyses (spatio‑temporal perspective) to isolate effects across regions and over time.
Implications for AI Economics
- Digital investments have increasing returns for cross‑regional innovation when they push digital patenting past a critical mass — models of AI R&D should allow for nonlinear (threshold) effects of digitization on innovation output and spillovers.
- Network topology matters: small‑world and multicenter structures facilitate rapid knowledge diffusion; policies that encourage short‑path connections and clustering (e.g., platforms, interoperable standards, targeted infrastructure) can amplify cross‑regional AI collaboration.
- Relational embeddedness is a key channel: incentives for repeated interactions (joint projects, mobility programs, data‑sharing agreements) strengthen ties that raise collaborative AI patent quality and diffusion.
- Regional policy design: fostering polycentric innovation systems (supporting multiple hubs rather than a single megacity) can broaden the geography of AI innovation and help avoid overconcentration.
- Firm strategy: firms and research institutions should target digital capability building and patenting intensity as complementary to network positioning (alliances, hubs) to unlock higher‑quality collaborative innovation.
- Generalizability & research agenda: results from China provide a testbed for theories of digitization → innovation networks in emerging economies; future work should (a) quantify which digital technologies matter most for AI collaboration, (b) examine firm‑level heterogeneity (size, sector), and (c) study policy levers that lower the threshold for beneficial digitalization across regions.
Assessment
Paper Typecorrelational
Evidence Strengthmedium — Uses decade-long panel data from A-share listed firms and patent records and applies network metrics and threshold analyses, providing consistent correlational evidence across multiple specifications; however, causal claims are weakened by likely endogeneity (reverse causation, omitted variables), measurement limits (digital transformation proxies, patents as innovation quality), and lack of clear exogenous variation or instrumental strategy.
Methods Rigormedium — The study combines network analysis with longitudinal firm/patent data and explores mechanisms (relational embeddedness, threshold effects), indicating thoughtful empirical work; but rigor is limited by reliance on observational associations, potential sample selection (listed firms only), and no explicit quasi-experimental identification to address confounding.
SamplePanel of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2011–2021, using firm-level digital transformation indicators and patent data (including digital patent counts), aggregated to construct city-level and cross-regional collaborative innovation networks based on joint patenting/co-applications.
Themesinnovation adoption
IdentificationObservational panel analysis linking firm- and city-level measures of digital transformation to patent-based measures of cross-regional collaborative innovation, complemented by network analysis (small-world metrics, spatial evolution) and threshold/heterogeneity tests; identification appears to rely on covariate adjustment and fixed effects rather than exogenous variation or instrumental/quasi-experimental designs.
GeneralizabilityChina-specific institutional and market context (policy, firm structure) may not generalize to other countries, Sample restricted to publicly listed firms — excludes SMEs and informal-sector innovators, Uses patent co-application as proxy for collaborative innovation, which omits non-patented knowledge exchange and may bias toward formalized R&D, Digital transformation measured via firm disclosures/patent tags — measurement and sectoral heterogeneity may limit transferability, Findings from 2011–2021 may not reflect post-2021 advances (e.g., rapid AI tool diffusion) or different technological regimes
Claims (7)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| The cooperative innovation network exhibits pronounced small-world characteristics. Innovation Output | positive | high | presence of small-world characteristics in the cooperative innovation network |
0.3
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| China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern over time. Innovation Output | positive | high | spatio-temporal structure of urban collaborative innovation network (quadrilateral → multicenter evolution) |
0.3
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| Advancement of digital transformation positively contributes to both the quality and the quantity of cross-regional cooperative innovation. Output Quality | positive | high | quality and quantity (counts) of cross-regional cooperative innovation |
0.3
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| Digital transformation enhances the relational embeddedness among cities, and this enhanced relational embeddedness facilitates improved outcomes in collaborative innovation (mediating mechanism). Innovation Output | positive | medium | relational embeddedness among cities and its mediating effect on collaborative innovation outcomes |
0.18
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| When the volume of digital patent applications surpasses a certain threshold, the positive effect of digital transformation on the quality of cross-regional collaborative innovation accelerates (nonlinear threshold effect). Output Quality | positive | high | quality of cross-regional collaborative innovation (and its change above a patent-volume threshold) |
0.3
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| These results provide empirical evidence from a major emerging economy (China) that can offer insights to inform policies and strategies in other regions undergoing digital transition. Governance And Regulation | positive | high | policy relevance / generalizability of findings to other regions |
0.05
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| Mechanisms identified — network structure evolution and increased relational embeddedness — contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Innovation Output | positive | high | role of network structure evolution and relational embeddedness as mechanisms linking digital transformation to cross-regional innovation dynamics |
0.3
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