Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020

Jeeeun Kim () and Sungjoo Lee ()
Additional contact information
Jeeeun Kim: Seoul National University
Sungjoo Lee: Ajou University

Scientometrics, 2017, vol. 111, issue 1, No 4, 47-65

Abstract: Abstract Having a new technology opportunity is a significant variable that can lead to dominance in a competitive market. In that context, accurately understanding the state of development of technology convergence and forecasting promising technology convergence can determine the success of a firm. However, previous studies have mainly focused on examining the convergence paths taken in the past or the current state of convergence rather than projecting the future trends of convergence. In addition, few studies have dealt with multi-technology convergence by taking a pairwise-analysis approach. Therefore, this research aimed to propose a forecasting methodology for multi-technology convergence, which is more realistic than pairwise convergence, based on a patent-citation analysis, a dependency-structure matrix, and a neural-network analysis. The suggested methodology enables both researchers and practitioners in the convergence field to plan their technology development by forecasting the technology combination that will occur in the future.

Keywords: Technology convergence; Forecasting; Patent-citation analysis; Neural-network analysis; Dependency-structure matrix; 68 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-017-2275-4 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:111:y:2017:i:1:d:10.1007_s11192-017-2275-4

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

DOI: 10.1007/s11192-017-2275-4

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 ().