Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology

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Exploring Innovation Ecosystem with Multi-Layered Heterogeneous Networks of Global 5G Communication Technology


4.3.1. Hub Nodes in Strong Connections

As show in Table 5, in the scientific sub-ecosystem, innovators’ collaboration degrees decline from macro to micro levels: d M e a n G C S > d M e a n G O S > d M e a n G I S . Within the technological sub-ecosystem, organizational-level collaboration is the lowest: d M e a n G C T > d M e a n G I T > d M e a n G O T . Standard deviation in G I S is the smallest, indicating relatively similar cooperation levels among individuals in the scientific sub-ecosystem.

Cooperation degrees and their standard deviations are lower in the scientific sub-ecosystem compared to the technological sub-ecosystem at macro and micro levels: d M e a n G C S < d M e a n G C T , d M e a n G I S < d M e a n G I T , d S . D . G C S < d S . D . G C T , and d S . D . G I S < d S . D . G I T . This suggests that scientific innovation exhibits less pronounced cooperation than technological innovation at the national and individual levels, with minimal variation among countries or individuals.

Cooperation degrees and their standard deviations are lower in scientific sub-ecosystem compared to technological sub-ecosystem at macro and micro levels: d M e a n G C S < d M e a n G C T , d M e a n G I S < d M e a n G I T , d S . D . G C S < d S . D . G C T and d S . D . G I S < d S . D . G I T . This suggests that scientific innovation exhibits less pronounced cooperation than technological innovation at the national and individual levels, with minimal variation among countries or individuals.

Conversely, at the meso level, the degree of cooperation and its standard deviation in the scientific sub-ecosystem surpass those in the technological sub-ecosystem: d M e a n G O S > d M e a n G O T and d S . D . G O S > d S . D . G O T . This indicates that, within organizational settings, scientific innovation exhibits higher levels of cooperation and dispersion compared to technological innovation.

From the perspective of knowledge elements, scientific innovation features fewer knowledge combinations than technological innovation, d M e a n G K S < d M e a n G K T . Moreover, the differences in knowledge combinations within scientific innovation are less pronounced than those within technological innovation, as d S . D . G k S < d S . D . G K T .

The degree distribution of each network is shown in Figure 5. After fitting the power law [85], the minimum coefficient Alpha value and the distance from the empirical data are summarized in Table 6, which reflects the degree of integration of organizational cooperation, individual cooperation, and knowledge, except for national-level cooperation with larger distance values.
Extensive collaboration among central nodes facilitates the exchange of information and resources. Figure 6 shows the comparison and geographical affiliation of the five most influential nodes in each network from macro to micro.

China and the United States (US) exhibit remarkable collaboration in both scientific ( G C S ) and technological ( G C T ) innovation. United Kingdom, France, and Gerseveral hold significant influence in scientific innovation cooperation, while the EP and WO, as country unions, along with China and the US, play crucial roles in technological innovation. These countries contribute to national-level knowledge innovation, exchange, and protection.

In G O S and G O T , Huawei and Nokia are influential players in both sub-ecosystems. Ericsson, Beijing University of Posts and Telecommunications (BUPT), and Southeast University focus on scientific cooperation, while Oppo, Samsung, and Sony demonstrate stronger collaboration in the technological sub-ecosystem. Among the top-20 influential organizations, universities and research institutes dominate in scientific innovation (75%), while enterprises dominate in technological innovation (100%). Chinese organizations, both enterprises and universities, have a significant presence in both scientific and technological innovations. European companies from Finland and Sweden excel in scientific innovation, while Samsung from Korea and Sony from Japan hold substantial influence in technological innovation.

In G I S , influential scientists in 5G telecommunications, such as Zhiguo Ding and H. Vincent Poor, provide references for other researchers. There are multiple scientists named Yangyang in G I S , which may be due to shared names or scientists changing workplaces. With comprehensive personal information, this method can accurately identify high-impact inventors in specific technical fields.

In G K S and G K T , hub nodes share similarities and differences. Both networks emphasize digital signal transmission (H04L) and wireless communication (H04W). In G K S , scientists show interest in the Internet of Things, MIMO, and machine learning, while in G K T , inventors focus on improving communication system frequency characteristics (H04B) through network entry. These similarities and differences highlight the distinct collaboration emphasis and knowledge connections within each sub-ecosystem.

4.3.2. Bridges of Weak Connections: Bridging Nodes

As show in Table 7, in the scientific sub-ecosystem, average bridging capacity and standard deviation of innovators decrease from macro to micro levels: B C M e a n G C S > B C M e a n G O S > B C M e a n G I S and B C S . D . G C S > B C S . D . G O S > B C S . D . G I S . In technological sub-ecosystems, national innovators have the highest average bridging capacity, followed by individuals, and organizational have the lowest: B C M e a n G C T > B C M e a n G I T > B C M e a n G O T and B C S . D . G C T > B C S . D . G I T > B C S . D . G O T . National actors have the highest average bridging capacity with notable variation, and individuals and organizations have the lowest in scientific and technological innovation, respectively.

At both macro and micro levels, the average bridging capacity and variability of actors in scientific ecosystem are lower compared to technological ecosystem, with B C M e a n G C S < B C M e a n G C T , B C M e a n G I S < B C M e a n G I T , B C S . D . G C S < B C S . D . G C T and B C S . D . G I S < B C S . D . G I T , but at the micro level, it is the opposite, with B C M e a n G O S > B C M e a n G O T and B C S . D . G O S > B C S . D . G O T . Additionally, most organizations in the technological sub-ecosystem lack bridging capability, with B C M e a d i a n G O T = 0 .

For knowledge elements, bridging capability in scientific innovation is lower compared to the technological field, with B C M e a n G K S < B C M e a n G K T .

As show in Figure 7, in G C S , the US and China serve as hub and bridging countries, while Latvia and Kenya connect communities despite not being the prominent hubs. In G C T , strong bridging capabilities are observed in country alliances (WO and EP), as well as in the US, China, and Switzerland. China and the US play crucial roles in facilitating cross-community collaborations in both scientific and technological sub-ecosystems.

In G O S , 355 bridging organizations, including Nokia, University of Basque Country, Yonsei University, and Jinan University, are consisted of 69 enterprises and 286 universities and research institutes, playing bridging roles. In G O T , there are 54 prominent bridging organizations, such as the State Grid Corporation of China, Samsung, Huawei, AT&T, and Purdue Research Foundation, with 46 enterprises and eight universities and research institutes. Enterprises take on bridging role.

In G I S , there are 382 bridging scientists, including Thar Baker, Ronan Farrell, and others. In G I T , there are 48 bridging inventors, including inventors like Liu J, Li C, and others.

Table 8 summarizes the number and percentage of bridging nodes. G C S has a higher proportion of bridging countries compared to G C T (6.11% vs. 5.41%). G O S has more bridging organizations compared to G O T (355 vs. 54), but a lower proportion of them (10.04% vs. 16.17%). There is also a greater willingness among organizations in technological collaborations to undertake a bridging role. Additionally, the number of bridging scientists is nearly eight times higher than bridging inventors (382 vs. 48), and their proportion is also three times greater than bridging inventors (4.92% vs. 1.51%). Stronger presence of as bridging roles in scientific innovation cooperation compared to inventors in technological innovation cooperation.
In G K S and G K T , number of bridging knowledge elements is small. Only seven keywords and 10 IPC Codes serve as bridging nodes, representing 0.13% and 0.45%, as show in Figure 8.

Furthermore, bridging knowledge elements in scientific innovation focus on scientific issues and extensibility of 5G technology, while in technological innovation, the emphasis is on practical implementation of firmware, equipment, and devices related to 5G technology.


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