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| Identification of Opportunities for Technological Fusion: Comprehensive Consideration of Relational and Structural Embedding |
| Zhao Youlin1,2, Gu Chenya1, Wang Jiajie2,3, Wang Yuemei1, Shi Yanqing4, Feng Li5 |
1.Business School, Hohai University, Nanjing 211100
2.School of Information Management, Nanjing University, Nanjing 210023
3.Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023
4.College of Information Management, Nanjing Agricultural University, Nanjing 210095
5.Hohai University Library, Nanjing 211100 |
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Abstract With the rapid development of technology and deepening of disciplinary integration, the technological innovations increasingly rely on the deep integration of interdisciplinary knowledge. In the existing research on identification of opportunities for technology integration, fields with high levels of technology integration have not been extensively investigated, and the adverse effects of weak relationships on knowledge fusion innovation in this field have not been considered. There is also a lack of consideration on the role of nodes in different network locations in technology fusion innovation. In this regard, this article introduces the theory of network embeddedness to construct a model that identifies opportunities for technology integration from fields with high levels of technology integration. The relationship embedding in the network embeddedness theory focuses on the strong and weak characteristics of relationships, while the structural embedding emphasizes the positional characteristics of nodes. First, the model introduced in this paper constructs a technology fusion path based on relationship embedding, dividing citation relationships into four quadrants with different strong and weak characteristics, combining them pairwise, and constructing knowledge networks separately. The knowledge network with a high technology fusion potential is used as an existing technology fusion path. Second, based on this technology fusion path and integration opportunity of structural embedding recognition technology, knowledge combinations that have not yet been connected and cross domain are identified through the medium of structural holes and central position nodes, and their fusion value is calculated to screen out technology fusion opportunities. An empirical evidence in the field of hyperspectral imaging with diverse fusion features shows that this model has a higher recognition accuracy and better depth and breadth of recognition content, which helps grasp the direction of future technological fusion.
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Received: 01 November 2024
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