IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p4055-d180746.html
   My bibliography  Save this article

Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/4055/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/11/4055/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    5. 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.
    6. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Kristina Dahlin & Deans M. Behrens, 2005. "When is an invention really radical? Defining and measuring technological radicalness," Post-Print hal-00480416, HAL.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Yuan Zhou & Heng Lin & Yufei Liu & Wei Ding, 2019. "A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 167-185, July.
    4. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    5. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
    6. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    7. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    8. 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.
    9. Suominen, Arho & Peng, Haoshu & Ranaei, Samira, 2019. "Examining the dynamics of an emerging research network using the case of triboelectric nanogenerators," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 820-830.
    10. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    11. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    12. Wooseok Jang & Yongtae Park & Hyeonju Seol, 2021. "Identifying emerging technologies using expert opinions on the future: A topic modeling and fuzzy clustering approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6505-6532, August.
    13. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    14. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.
    15. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    16. Li, Munan & Porter, Alan L. & Suominen, Arho, 2018. "Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 285-296.
    17. Park, Inchae & Triulzi, Giorgio & Magee, Christopher L., 2022. "Tracing the emergence of new technology: A comparative analysis of five technological domains," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    18. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    19. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    20. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4055-:d:180746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.