Measuring cognitive proximity using semantic analysis: A case study of China's ICT industry

Yawen Qin, Xiaozhen Qin, Haohui Chen, Xun Li () and Wei Lang
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Yawen Qin: School of Geography and Planning, Sun Yat-Sen University
Xiaozhen Qin: School of Geography and Planning, Sun Yat-Sen University
Haohui Chen: Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Xun Li: School of Geography and Planning, Sun Yat-Sen University
Wei Lang: School of Geography and Planning, Sun Yat-Sen University

Scientometrics, 2021, vol. 126, issue 7, No 28, 6059-6084

Abstract: Abstract Quantification of knowledge technologies has long posed a challenge to the measurement of cognitive proximity. This paper proposes a method to measure cognitive proximity by mining patent description text with the LDA topic model. With the patent-topic distribution got from the LDA topic model, the cognitive proximity is measured between enterprises or within cities, which could make up for the shortage of existing measurement methods limited by the rigid IPC, industry classification system, or non-standard interview data. Our empirical studies on the ICT industry indicate that the 20 topics obtained through the topic model have a good correspondence with the technologies involved in this industry's leading products and services. And we dig out the knowledge and technology information in the patent text to depict the technology landscape, including mining the changes of technology topics over time, the difference of distribution in various cities, and the development trend of the urban innovation network. This method's effectiveness is also proved in the model that compares different measurement methods when revealing the relationship between cognitive proximity and patent productivity. Last, researchers can use this approach to delve deeper into urban innovation issues, and policymakers can use it to figure out further innovation.

Keywords: Cognitive proximity; Innovation; Patent text; Topic model; Technical landscape; ICT industry; 68U15; 68R10; 62J02 (search for similar items in EconPapers)
JEL-codes: O32 O33 R11 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11192-021-04021-x

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