Who innovates with whom and why? Evidence from international collaboration in energy patenting

Milani, Suellen Oliveira

doi:10.1080/10438599.2019.1629531

Cited by 21 publications

(6 citation statements)

References 38 publications

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“…One the other hand, technology similarity exhibited a positive and long-term significant influence at a 1% level. This result is consistent with previous studies indicating that technological proximity facilitated trade and scientific research ( Milani, 2020 ). As ginseng is a medicinal plant, its medicinal value can be better understood and appreciated by members of the IRCH organization, thus facilitating its promotion, application and supervision.…”

Section: Resultssupporting

confidence: 93%

“…Religion and language were found to be positive factors influencing GGTNs, but their significance was observed only in 2010, and showed a declining trend over time. This finding implies that ginseng trade initially benefited from similar culture backgrounds, but with the advancement of economic globalization, cultural barriers in GGTNs have been progressively (Milani, 2020).…”

Section: Qap Regression Analysismentioning

confidence: 91%

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Global ginseng trade networks: structural characteristics and influencing factors

et al. 2023

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Background: Ginseng is a rare and highly valued Chinese materia medica with a rich trading history and has a wide range of application, including medicine, food, healthcare, and daily chemical production. However, the global trade of ginseng exhibits diverse features and uneven development across different countries and regions. Surprisingly, the intricate network relationship and the underlying characteristics and influencing factors of ginseng trade networks remain unexplored.Methods: This study analyzed ginseng trade data obtained from the UN-Comtrade database and used social network analysis to construct global ginseng trade networks. To elucidate the structural characteristics, we analyzed the indicators of the overall network structure and node attributes. Core-periphery analysis is used to examine the evolutionary patterns within the global ginseng trade networks. Furthermore, we apply the quadratic assignment procedure to investigate the impact and relevance of spatial proximity, cultural differences, economic indicators, population size, technological similarity, and institutional distance.Results: The findings reveal that the global ginseng trade networks exhibit typical small-world and scale-free properties, as well as a core-periphery structure. Several core countries, including China, South Korea, Germany, and the United States, exert significant control over both trade volume and trade partners. South Korea and China initially occupied central positions in the export market due to their resource endowments, their prominence has gradually diminished with the ascendancy of Germany and the United States. According to the core-periphery analysis, the ginseng trade has shown a gradual concentration within specific trade groups comprising core and semi-periphery countries, most of which are along the “Belt and Road” religion. We also found that geographic distance and GDP per capita exert negative effects on ginseng trade, while factors such as land adjacency, technology and economic gap, population size, and institution similarity play significant positive roles.Conclusion: The global ginseng trade has experienced increasing concentration and close linkage among a limited numbers of participants. It is crucial to pay close attention to the relationship between ginseng industry development and resource conservation. Strategies such as expanding trade channels, implementing trade substitution measures, and optimizing the quality and standards of ginseng products can effectively enhance trade security.

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Section: Resultssupporting

confidence: 93%

Section: Qap Regression Analysismentioning

confidence: 91%

Global ginseng trade networks: structural characteristics and influencing factors

et al. 2023

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“…The estimated coefficient of geographic proximity is significantly positive, meaning that regions seeking low-carbon technology cooperation prefer to cooperate with neighboring regions. This result agrees with the findings of Milani et al [73]. The shorter geographic distance can enhance communication and interaction between the cooperating parties, which can increase the efficiency of technical cooperation.…”

Section: Resultssupporting

confidence: 93%

Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model

2022

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China actively promotes cross-regional low-carbon technology cooperation to improve low-carbon technologies and remove technological barriers to sustainable development. In this process, a cross-regional low-carbon technology cooperation network (LCTCN) has been developed and evolved. To help China rationalize the allocation of innovation resources and promote the cross-regional exchange of low-carbon technologies, we measured the LCTCN using low-carbon technology co-patents from 2011 to 2020. We investigated changes in the network structure using social network analysis. In addition, we examined the endogenous structures and exogenous factors that influence the formation of cooperation relationships in the network using a time exponential random graph model (TERGM). We came to the following conclusions: (1) The LCTCN develops toward complexity, showing prominent characteristics of spatial imbalance, heterogeneity, and core-periphery. (2) Among the endogenous structural variables, the coefficient of geometrically weighted degree (Gwdegree) is significantly negative, suggesting that regions within LCTCN tend to form partnerships with already well-connected regions. On the other hand, a positive coefficient of geometrically weighted dyad shared partner statistic (GWDSP) suggests that regions tend to link in multiple ways to each other. (3) Among the exogenous variables, the coefficient of the digital economy is significantly positive. As a result, for every level of digital economy development in a region, the probability of establishing low-carbon technology cooperation between that region and other regions increases by 87.39%. (4) External openness and geographical proximity can also facilitate establishing partnerships. The formation of low-carbon partnerships in the network results from a combination of endogenous structures and exogenous variables.

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“…This explains why collaborations predominantly drawing on analytical knowledge is often organized at long distances. For instance, it is broadly believed that informal learning based on routine and experience is more inclined to localized knowledge exchange than knowledge sourcing outside the region, However, if a technology is more specific and has a strong scientific component, such as nanotechnology or renewable energy, its specific technological input is usually obtained from intercity partners because most rapidly developing and complex technologies are more and more based on the combination of rather distant technological fields and they need to tap in more universal, codified knowledge of national innovation systems (Bozeman et al., 2007; Milani, 2020). Given the fact that “one‐hour intercity traffic circle” strategy has been implemented for improving the high accessibility of the GPRD since 2008, as well as advances in communication and information technology in China, we assume the cost of codifying knowledge is decreasing with time.…”

Section: Theoretical Basis and Research Hypothesesmentioning

confidence: 99%

The evolutionary patterns of intercity co‐invention networks in the Greater Pearl River Delta, China: A comparative analysis based on the technological intensity of industry

Chong

Liu

2022

Growth and Change

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A substantial amount of research has examined the collaborations within technology-based industries, but how technological intensity relates to the evolution of interregional research collaboration is less well understood. This paper investigates the dynamic changes of spatial structure and determinants of intercity research collaborations in the sectoral innovation networks of high-tech industries contrasting to low and medium technology (LMT) industries in the Greater Pearl River Delta (GPRD), China.Using the sectoral patent application data over the period 2008-2016, the social network analysis results confirm a higher level of triangles in the co-invention network of high-tech industries than within LMT industries. Moreover, the empirical results through Exponential Random Graph Model (ERGM) show that, compared to LMT industries, collaboration in the co-invention network of high-tech industries is less sensitive to geographical and linguistic distance, while more subject to economic distance, regional educational level and government support with time. In addition, cognitive proximity and regional R&D intensity are both important prerequisite in the formation of these two collaborative networks.

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Who innovates with whom and why? Evidence from international collaboration in energy patenting

Cited by 21 publications

References 38 publications

Global ginseng trade networks: structural characteristics and influencing factors

Global ginseng trade networks: structural characteristics and influencing factors

Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model

The evolutionary patterns of intercity co‐invention networks in the Greater Pearl River Delta, China: A comparative analysis based on the technological intensity of industry

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