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| Identification of Disruptive Technologies and Prediction of Diffusion Trends: Conceptual Model and Empirical Analysis |
| Wang Kang, Chen Yue, Wang Yuqi, Han Meng |
| The Institute of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116024 |
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Abstract Identifying and assessing the disruptive potential and diffusion trends of a technology can provide a basis for precise decision-making regarding the allocation of scientific and technological resources and advanced layout of future industries for the country and government. This study first constructs a conceptual model for identifying disruptive technologies and predicting their diffusion trends. Subsequently, based on this model, using the 3D printing field as an example, disruptive patents are identified from among outliers and influence dimensions, and disruptive technologies are extracted. Finally, based on the identification of disruptive patents, automatic labeling and strategic coordinates are applied to technology topic diffusion paths. A new multistate automatic labeling technology topic diffusion trend prediction method is proposed to reveal the dynamic diffusion relationship between core, edge, mature, emerging, and other positional themes. An inherent logical relationship among symbiosis, matching, and the association between outlier patents and disruptive technologies is established through further research. Thus, identifying disruptive technologies from the perspective of outlier patents is feasible. The disruptive technologies in the field of 3D printing from 1955 to 2017 were mainly distributed in three directions: high-end equipment manufacturing, biopharmaceuticals, and materials. The prominent technological fields were transportation, engines/pumps/turbines, biomaterial analysis, semiconductors, and environmental technology. The predicted trend of topic diffusion in multi-state automatic labeling technology indicates that the future development potential of biomedical 3D printing technology is enormous.
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Received: 24 October 2023
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