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Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis

Author

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  • Inchae Park

    (Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea)

  • Byungun Yoon

    (Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea)

Abstract

This paper aims at proposing a quantitative methodology to identify promising research frontiers (RFs) based on bibliographic information of scientific papers and patents. To achieve this, core technological documents are identified by suggesting several indices which measure paper impact, research impact, patent novelty, impact, marketability, and the right range to evaluate technological documents and which measure the research capability of research organizations (ROs) such as a RO’s activity, productivity, market competitiveness, and publication impact. The RFs can be identified by clustering core technological documents, and promising indices of each RF which are from the perspectives of growth, impact, marketability, and science-based effect, are calculated to promising RFs. As an illustration, this paper selects the case of pattern recognition technology among various technologies in the information and communication technology sector. To validate the proposed method, emerging technologies on the hype cycle are utilized, allowing analysts to compare the results. Comparing the results derived from scientific papers and patents, the results from scientific papers are proper to suggest themes for research (R) in relatively long-term perspective, whereas the results from patents are appropriate for providing themes for development (D) in terms of relatively short-term view. This approach can assist research organizations and companies in devising a technology strategy for a future direction of research and development.

Suggested Citation

  • Inchae Park & Byungun Yoon, 2018. "Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis," Sustainability, MDPI, vol. 10(11), pages 1-32, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4055-:d:180746
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    References listed on IDEAS

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    1. Bo Jarneving, 2005. "A comparison of two bibliometric methods for mapping of the research front," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(2), pages 245-263, November.
    2. Niosi, Jorge, 1999. "Fourth-Generation R&D: From Linear Models to Flexible Innovation," Journal of Business Research, Elsevier, vol. 45(2), pages 111-117, June.
    3. Kim, Jieun & Lee, Changyong, 2017. "Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 59-76.
    4. Jarneving, Bo, 2007. "Bibliographic coupling and its application to research-front and other core documents," Journal of Informetrics, Elsevier, vol. 1(4), pages 287-307.
    5. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    6. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    7. Diana Lucio‐Arias & Loet Leydesdorff, 2009. "An indicator of research front activity: Measuring intellectual organization as uncertainty reduction in document sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(12), pages 2488-2498, December.
    8. Yuan Zhou & Xin Li & Rasmus Lema & Frauke Urban, 2016. "Comparing the knowledge bases of wind turbine firms in Asia and Europe: Patent trajectories, networks, and globalisation," Science and Public Policy, Oxford University Press, vol. 43(4), pages 476-491.
    9. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    10. Corrocher Nicoletta & Malerba Franco & Montobbio Fabio, 2003. "The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources," Economics and Quantitative Methods qf0317, Department of Economics, University of Insubria.
    11. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    12. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    13. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    14. Shino Iwami & Junichiro Mori & Ichiro Sakata & Yuya Kajikawa, 2014. "Detection method of emerging leading papers using time transition," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1515-1533, November.
    15. Woo Jin Lee & Won Kyung Lee & So Young Sohn, 2016. "Patent Network Analysis and Quadratic Assignment Procedures to Identify the Convergence of Robot Technologies," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-16, October.
    16. Woo Hyoung Lee, 2008. "How to identify emerging research fields using scientometrics: An example in the field of Information Security," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 503-525, September.
    17. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    18. Dahlin, Kristina B. & Behrens, Dean M., 2005. "When is an invention really radical?: Defining and measuring technological radicalness," Research Policy, Elsevier, vol. 34(5), pages 717-737, June.
    19. 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.
    20. Boon, Wouter & Moors, Ellen, 2008. "Exploring emerging technologies using metaphors - A study of orphan drugs and pharmacogenomics," Social Science & Medicine, Elsevier, vol. 66(9), pages 1915-1927, May.
    21. Inchae Park & Keeeun Lee & Byungun Yoon, 2015. "Exploring Promising Research Frontiers Based on Knowledge Maps in the Solar Cell Technology Field," Sustainability, MDPI, vol. 7(10), pages 1-30, October.
    22. Kristina Dahlin & Deans M. Behrens, 2005. "When is an invention really radical? Defining and measuring technological radicalness," Post-Print hal-00480416, HAL.
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    Cited by:

    1. Jinho Choi & Yong Sik Chang, 2020. "Development of a New Methodology to Identity Promising Technology Areas Using M&A Information," Sustainability, MDPI, vol. 12(14), pages 1-25, July.
    2. Jinho Choi & Nina Shin & Yong Sik Chang, 2021. "Strategic Investment Decisions for Emerging Technology Fields in the Health Care Sector Based on M&A Analysis," Sustainability, MDPI, vol. 13(7), pages 1-20, March.
    3. Lee, Keeeun & Kim, Sunhye & Yoon, Byungun, 2022. "A systematic idea generation approach for developing a new technology: Application of a socio-technical transition system," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    4. Jason Jihoon Ree & Cheolhyun Jeong & Hyunseok Park & Kwangsoo Kim, 2019. "Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology," Sustainability, MDPI, vol. 11(5), pages 1-18, March.

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