Innovation signals: leveraging machine learning to separate noise from news
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DOI: 10.1007/s11192-023-04672-y
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- O. Mryglod & Yu. Holovatch & R. Kenna & B. Berche, 2016. "Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1151-1166, March.
- Giada Di Stefano & Alfonso Gambardella & Gianmario Verona, 2012. "Technology Push and Demand Pull Perspectives in Innovation Studies: Current Findings and Future Research Directions," Post-Print hal-00696607, HAL.
- Gordon, Adam Vigdor & Ramic, Mirza & Rohrbeck, René & Spaniol, Matthew J., 2020. "50 Years of corporate and organizational foresight: Looking back and going forward," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
- Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
- Ahmad Barirani & Bruno Agard & Catherine Beaudry, 2013. "Discovering and assessing fields of expertise in nanomedicine: a patent co-citation network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1111-1136, March.
- Douglas Henrique Milanez & Leandro Innocentini Lopes Faria & Roniberto Morato Amaral & Daniel Rodrigo Leiva & José Angelo Rodrigues Gregolin, 2014. "Patents in nanotechnology: an analysis using macro-indicators and forecasting curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1097-1112, November.
- Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
- Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
- Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
- Di Stefano, Giada & Gambardella, Alfonso & Verona, Gianmario, 2012. "Technology push and demand pull perspectives in innovation studies: Current findings and future research directions," Research Policy, Elsevier, vol. 41(8), pages 1283-1295.
- Rohrbeck, René & Kum, Menes Etingue, 2018. "Corporate foresight and its impact on firm performance: A longitudinal analysis," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 105-116.
- Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
- D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
- Oleg Ena & Nadezhda Mikova & Ozcan Saritas & Anna Sokolova, 2016. "A methodology for technology trend monitoring: the case of semantic technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1013-1041, September.
- Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
- Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
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More about this item
Keywords
Weak signals; Strong signals; Corporate foresight; Innovation management; Machine learning; Artificial intelligence; Trend scouting; Technology scouting; Startup scouting;All these keywords.
JEL classification:
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
- C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
- M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
- O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
- O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
- O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
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