Identifying disruptive technologies by integrating multi-source data

Xiwen Liu (), Xuezhao Wang, Lucheng Lyu and Yanpeng Wang
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Xiwen Liu: Chinese Academy of Sciences
Xuezhao Wang: Chinese Academy of Sciences
Lucheng Lyu: Chinese Academy of Sciences
Yanpeng Wang: Chinese Academy of Sciences

Scientometrics, 2022, vol. 127, issue 9, No 13, 5325-5351

Abstract: Abstract Identifying disruptive technologies has important value for the decision-making in technology layout and investment. The identification methods of disruptive technologies based on data mining have attracted much attention recently, but most of the existing studies use single data for the identification, that may cause bias. Therefore, this paper uses multi-source data which represent the “science-technology-industry-market” chain to identify disruptive technologies. In addition, this paper improves the two steps, generating candidate technology list and evaluating disruptive potential, in the general process of identifying disruptive technologies separately and develops two new methods. One method is to obtain the list of potential disruptive technologies from experts and then evaluate the technology disruptive potential by using a multi-dimensional index system. The case study of this method is carried out in life science field, and four types of data (papers, patents, data of start-ups and public opinion) are used to evaluate thepotential disruptive technologies. Another method is to generate the list of potential disruptive technologies by mining multi-source data and then evaluate the technology disruptive potential by experts. The case study of this method is carried out in energy technology filed and life science, and three types of data (papers, patents and projects) are used for mining to generate the candidate technologies list. The effectiveness of the two methods using multi-source data is verified by comparing the results with the list of technologies given by experts in advance.

Keywords: Disruptive technology; Multi-source data mining; Life science; Energy field; Technology forecasting (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11192-022-04283-z

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