sh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • harvard-anglia-ruskin-university
  • apa-old-doi-prefix.csl
  • sodertorns-hogskola-harvard.csl
  • sodertorns-hogskola-oxford.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research
Hanken School of Economics, Finland.
Hanken School of Economics, Finland; University of St. Gallen, Switzerland.
Luleå University of Technology, Sweden.
Södertörn University, School of Social Sciences, Business Studies.ORCID iD: 0000-0003-2125-6155
Show others and affiliations
2021 (English)In: Technological forecasting & social change, ISSN 0040-1625, E-ISSN 1873-5509, Vol. 165, article id 120524Article in journal (Refereed) Published
Abstract [en]

Recent advances in AI algorithms and computational power have led to opportunities for new methods and tools. Particularly when it comes to detecting the current status of inter-industry technologies, the new tools can be of great assistance. This is important because the research focus has been on how firms generate value through managing their business models. However, further attention needs to be given to the external technological opportunities that also contribute to value creation in firms. We applied unsupervised machine learning techniques, particularly DBSCAN, in an attempt to generate a macro-level technological map. Our results show that AI and machine learning tools can indeed be used for these purposes, and DBSCAN is a potential algorithm. Further research is needed to improve the maps and to use the generated data to study related phenomena including entrepreneurship.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 165, article id 120524
Keywords [en]
AI, DBSCAN, Innovation diffusion, Tracking technology, Unsupervised machine learning, Business models, Computational power, Current status, Research focus, Technological opportunity, Value creation, Machine learning
National Category
Business Administration Computer and Information Sciences
Identifiers
URN: urn:nbn:se:sh:diva-43540DOI: 10.1016/j.techfore.2020.120524ISI: 000618756500015Scopus ID: 2-s2.0-85098940035OAI: oai:DiVA.org:sh-43540DiVA, id: diva2:1518105
Available from: 2021-01-15 Created: 2021-01-15 Last updated: 2022-10-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Oghazi, Pejvak

Search in DiVA

By author/editor
Oghazi, Pejvak
By organisation
Business Studies
In the same journal
Technological forecasting & social change
Business AdministrationComputer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 116 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • harvard-anglia-ruskin-university
  • apa-old-doi-prefix.csl
  • sodertorns-hogskola-harvard.csl
  • sodertorns-hogskola-oxford.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf