An analysis of the spillover effects based on patents and inter-industrial transactions for an emerging blockchain technology

Hiroshi Someda (), Takanori Akagi and Yuya Kajikawa
Additional contact information
Hiroshi Someda: Tokyo Institute of Technology
Takanori Akagi: Tokyo Institute of Technology
Yuya Kajikawa: Tokyo Institute of Technology

Scientometrics, 2022, vol. 127, issue 8, No 1, 4299-4314

Abstract: Abstract A simple and robust approach to predict the spillover effects of emerging technologies enables proper formulation of investment strategies. In this study, we propose the method in order to detect industry sectors impacted by the spillover effect of emerging technologies in their early stage. The method integrates patent analysis with input–output analysis to model knowledge spillover among industrial sectors and has the following three steps. The first is an analysis of technological features in industry sectors. Using the IPC group of patents, we characterized each industrial sector by technological features. The second is an analysis of technological features in a given emerging technology. The third is a similarity analysis of the technological features between emerging technology and industry sectors. In this paper, we conducted a case study on blockchain technology. We demonstrated the effectiveness of the proposed method by comparing the results with the existing reports. We found that the predictive performance became the highest when we used an industrial sector-normalized matrix in patent analysis and producer’s price table in input–output analysis. This method is expected to be used for the early detection of spillover effects of emerging technologies.

Keywords: Patent analysis; Technology spillover; Blockchain impact; Input–output table; Concordance table (search for similar items in EconPapers)
JEL-codes: O32 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-022-04457-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04457-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-022-04457-9

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().