Research on Technological Divisive Faultlines in Cliques: Evidence From China’s Biomedical Industry Innovation Network

Zhao, Yan; Zheng, Li; Li, Luying; Qi, Niannian

doi:10.1109/access.2022.3200753

Cited by 2 publications

(5 citation statements)

References 56 publications

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“…Third, we check and update the firms' business registration name, the established time of the above firms participating in the alliance through the official website of Qichacha (https://www.qcc.com/) and so on. Fourth, we judge whether a firm belongs to the new energy industries alliance by two criteria as follows (Zhao et al , 2022): At least a firm in the alliance belongs to the new energy industries (i.e. wind energy industries, solar energy industries, biomass energy industries, geothermal energy industries, wave energy industries, ocean current energy industries and tidal energy industries). The alliance is engaged in new energy industries. …”

Section: Methodsmentioning

confidence: 99%

“…On the one hand, some scholars put forward or summarized their concept definitions according to social network theory and existing research. For example, Zhao et al (2022) thought that ED can measure the closeness of connections among members in a network. Burt (1992) put forward that a structural hole is a relationship of nonredundancy between two contacts and pointed out that it can be measured by effective size, efficiency, constraint and hierarchy.…”

Section: Ego-network Structuresmentioning

confidence: 99%

Section: Sample and Datamentioning

confidence: 99%

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How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Zhao

et al. 2022

JKM

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Purpose Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network. Design/methodology/approach Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network. Findings This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant. Research limitations/implications This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI. Originality/value First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

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Section: Methodsmentioning

confidence: 99%

Section: Ego-network Structuresmentioning

confidence: 99%

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How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Zhao

et al. 2022

JKM

Self Cite

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“…Building successful technological capabilities to have a niche in the market has been gripped by incumbents and startups [21]. Previous research [22][23] has shown that most citations flow between a few firms in the network, and these significant firms are likely to drive activity in the market. The previous research formulated the multivariate analysis model to develop the organization innovation relationship, briefed in Table I.…”

Section: Related Workmentioning

confidence: 99%

“…Closeness centrality is the measure that scores each node based on their "closeness" to all other nodes in the network. It stands for the convenience and ease of connections between the focused node and rest of the nodes in the network [22]. Closeness centrality is determined by calculating the average inverse distance (distance) between a node and every other node in a network.…”

Section: ) Closeness Centralitymentioning

confidence: 99%

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

Bhatt

2023

IEEE Access

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In the current era of inevitable technological progress, organizations strive to achieve technological competence. One way to measure the adaptability of firms to huge technological shifts is through various parameters, including patenting activities. This study presents a method for identifying the significance of firms in an innovation network using patent citation analysis and centrality measures. Specifically, the study employs k-means clustering to classify firms into similar clusters based on Networkbased centrality measures such as betweenness, closeness, and eigenvector centrality. The study then develops a cluster relational network by establishing a cluster adjacency network and identifying firm positions within and between clusters. By examining the relation between clusters, the cluster network identifies the significance of firms. The study identifies four positions, namely, leader, follower, knowledge inertia, and significantly emerging, that align with the status of firms in patenting innovation capability. The method is implemented using blockchain technology as a case study. The novelty of the study lies in the structured approach to identifying firm significance by adding another layer of adjacency network to existing patent citation analysis techniques.

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Research on Technological Divisive Faultlines in Cliques: Evidence From China’s Biomedical Industry Innovation Network

Cited by 2 publications

References 56 publications

How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

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