The nature of ICT in technology convergence: A knowledge-based network analysis

Abstract

This study aims to understand the nature of information and communication technology in technology convergence. We form a knowledge network by applying social network theories to Korean patent data collected from the European Patent Organization. A knowledge network consists of nodes representing technology sectors identified by their International Patent Classification codes and edges that link International Patent Classification codes when they appear concurrently in a patent. We test the proposed hypotheses using four indices (degree centrality, E-I index, entropy index, and clustering coefficient). The results show that information and communication technology is easily attached but tends to converge with similar technology and has the greatest influence on technology convergence over other technologies. This study is expected to help practitioners and policymakers understand the structure and interaction mechanisms of technology from a systematic perspective and improve national-level technology policies.

China’s non-ferrous metal recycling technology convergence and driving factors: A quadratic assignment procedure analysis based on patent collaboration-based network structural hole

“…This study collects the non-ferrous metal resource recycling registrations in China based on the 52 types of primary IPC, involved in the non-ferrous metal resource recycling, where the value of two IPCs is the number of associated patents. Previous studies have only focused on the measurement of technology convergence in 3D printing [20], textile [35], and ICT [55] fields. In contrast, this study analyzed the Chinese non-ferrous metal resource recycling technology convergence, using normalized degree, closeness, and betweenness social network analysis indicators based on the construction of IPC cooccurrence matrix.…”

“…The degree of technology convergence is judged based on the similarity of technologies between different technology domains, and the higher technology similarity between two technology nodes represents the greater degree of convergence. The degree of technological similarity is measured by the Jaccard index of two nodes in the network [55]. The Jaccard index can transform the correlation matrix into a correlation coefficient matrix that expresses the strength of association and similarity between the departments; it is calculated as follows:…”

The role of design engineers: Evidence from intra-firm knowledge and collaboration networks

“…In addition to the collaboration networks, we developed knowledge networks for each firm period based on Cooperative Patent Classification (CPC) codes (see Fig 1(A) and 1(C) for a visual representation of our knowledge network construction) [19,52]. An inventor's knowledge elements were defined as all 4-digit CPC codes related to their patents, with each CPC PLOS ONE code represented as a node and a tie indicating the co-occurrence of CPC codes within a patent application [19]. It is important to note that designers are excluded from the knowledge network analysis as design patents do not have CPC codes.…”

“…A utility patent is granted to individuals who "invent or discover any new and useful process, machine, manufacture, or composition of matter", while a design patent is granted to those who "invent any new, original, and ornamental design for an article of manufacture" [16]. Subsequently, we constructed two types of networks for each firm period: a social network comprising inventors and a knowledge network comprising knowledge elements [17][18][19]. We theorized and empirically evaluated how engineers and design engineers differ in degree and betweenness centralities within a firm's knowledge and collaboration networks.…”

The Impact of Technology Convergence on the Sustainable Innovation of China’s Modern Manufacturing Enterprises: The Mediating Role of the Knowledge Base

“…The focus then shifts to attempting technology convergence with groups of technology units that demonstrate a substantial flow density of technological knowledge; essentially, those with a pronounced clustering coefficient [38]. Variations in local clustering coefficients within technology convergence networks signal the velocity of technological knowledge flow, serving as benchmarks for assessing fusion opportunities and potential added value [42]. A network-wide escalation in clustering coefficients signifies enhanced information exchange capabilities across a network, fostering improved innovation performance [37].…”