IDEAS home Printed from https://ideas.repec.org/a/eee/telpol/v40y2016i10p956-970.html
   My bibliography  Save this article

Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations

Author

Listed:
  • Noh, Heeyong
  • Song, Young-Keun
  • Lee, Sungjoo

Abstract

In recent years, the volume of mobile traffic has increased at an unprecedented rate and the mobile paradigm has changed. These dynamics have driven the next-generation telecommunications technologies, and the existing fourth-generation technology is reaching maturity. The pre-acquisition of promising future technologies enables firms to achieve and sustain their business growth; thus, numerous organizations in the telecommunications sector have made a huge amount of effort to develop fifth-generation (5G) technologies. Although understanding these emerging and promising 5G technologies is essential, they still remain poorly investigated. To fill this research gap, we first define the characteristics of promising technologies in the telecommunications sector, then develop a framework for identifying them based on patents. Specifically, we design three patent indices for deriving the core patents published by leading organizations in the sector. We then apply bibliographic coupling and text mining to the patents and identify their major innovation trends. We identify 21 technology fields as promising areas emphasized by the leading organizations. Theoretically, this study is one of the few attempts to examine various approaches to identify promising technologies and to suggest the most appropriate one considering the research purpose as well as the characteristics of telecommunications sector. In practice, this study can provide information about patent activities of key incumbent actors and thus offer some insights into recent technological developments towards 5G.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:telpol:v:40:y:2016:i:10:p:956-970
    DOI: 10.1016/j.telpol.2016.04.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308596116300295
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.telpol.2016.04.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ernst, Holger & Omland, Nils, 2011. "The Patent Asset Index - A new approach to benchmark patent portfolios," World Patent Information, Elsevier, vol. 33(1), pages 34-41, March.
    2. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    3. Kano, Sadahiko, 2000. "Technical innovations, standardization and regional comparison -- a case study in mobile communications," Telecommunications Policy, Elsevier, vol. 24(4), pages 305-321, May.
    4. Marc Rysman & Timothy Simcoe, 2008. "Patents and the Performance of Voluntary Standard-Setting Organizations," Management Science, INFORMS, vol. 54(11), pages 1920-1934, November.
    5. Sue Bowden & David Clayton & Alvaro Pereira, 2012. "Extending broadcast technology in the British Colonies during the 1950s," European Review of Economic History, European Historical Economics Society, vol. 16(1), pages 23-50, February.
    6. Bekkers, Rudi & Duysters, Geert & Verspagen, Bart, 2002. "Intellectual property rights, strategic technology agreements and market structure: The case of GSM," Research Policy, Elsevier, vol. 31(7), pages 1141-1161, September.
    7. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
    8. Cagnin, Cristiano & Havas, Attila & Saritas, Ozcan, 2013. "Future-oriented technology analysis: Its potential to address disruptive transformations," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 379-385.
    9. Harhoff, Dietmar & Hoisl, Karin, 2007. "Institutionalized incentives for ingenuity--Patent value and the German Employees' Inventions Act," Research Policy, Elsevier, vol. 36(8), pages 1143-1162, October.
    10. Farrell, Joseph & Saloner, Garth, 1986. "Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation," American Economic Review, American Economic Association, vol. 76(5), pages 940-955, December.
    11. Lee, Deok Joo & Hwang, Jooho, 2010. "Decision support for selecting exportable nuclear technology using the analytic hierarchy process: A Korean case," Energy Policy, Elsevier, vol. 38(1), pages 161-167, January.
    12. Tassey, Gregory, 2000. "Standardization in technology-based markets," Research Policy, Elsevier, vol. 29(4-5), pages 587-602, April.
    13. Mu, Qing & Lee, Keun, 2005. "Knowledge diffusion, market segmentation and technological catch-up: The case of the telecommunication industry in China," Research Policy, Elsevier, vol. 34(6), pages 759-783, August.
    14. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    15. Geert Duysters & John Hagedoorn, 1998. "Technological Convergence in the IT Industry: The Role of Strategic Technology Alliances and Technological Competencies," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 5(3), pages 355-368.
    16. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    17. Dou, Henri Jean-Marie, 2004. "Benchmarking R&D and companies through patent analysis using free databases and special software: a tool to improve innovative thinking," World Patent Information, Elsevier, vol. 26(4), pages 297-309, December.
    18. 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.
    19. Reitzig, Markus, 2004. "Improving patent valuations for management purposes--validating new indicators by analyzing application rationales," Research Policy, Elsevier, vol. 33(6-7), pages 939-957, September.
    20. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2016. "Forecasting technology substitution based on hazard function," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 259-272.
    21. Bauer, Johannes M., 0. "Regulation, public policy, and investment in communications infrastructure," Telecommunications Policy, Elsevier, vol. 34(1-2), pages 65-79, February.
    22. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
    23. Rosenberg,Nathan, 1994. "Exploring the Black Box," Cambridge Books, Cambridge University Press, number 9780521459556.
    24. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    25. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, April.
    26. Park, Yongtae & Yoon, Byungun & Lee, Sungjoo, 2005. "The idiosyncrasy and dynamism of technological innovation across industries: patent citation analysis," Technology in Society, Elsevier, vol. 27(4), pages 471-485.
    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. Song, Kisik & Yun, Siyeong & Kim, Leehee & Lee, Sungjoo, 2022. "Investigating new design concepts based on customer value and patent data: The case of a future mobility door," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    3. 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.
    4. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    5. Inchae Park & Byungun Yoon, 2018. "Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis," Sustainability, MDPI, vol. 10(11), pages 1-32, November.
    6. Park, Jiyoun & Nam, Changi & Kim, Hye-jin, 2019. "Exploring the key services and players in the smart car market," Telecommunications Policy, Elsevier, vol. 43(10).
    7. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    8. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    9. Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
    10. 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.
    11. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    12. Seok Jin Youn & Yong-Jae Lee & Ha-Eun Han & Chang-Woo Lee & Donggyun Sohn & Chulung Lee, 2024. "A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems," Sustainability, MDPI, vol. 16(15), pages 1-32, August.
    13. Park, Jiyoun & Nam, Changi & Kim, Hye-jin & Kim, Seongcheol, 2018. "What are the relative importance of smart car utilities from consumer perspective and who will lead them?," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190334, International Telecommunications Society (ITS).
    14. Noh, Heeyong & Kim, Kyuwoong & Song, Young-Keun & Lee, Sungjoo, 2021. "Opportunity-driven technology roadmapping: The case of 5G mobile services," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    15. Hossein Sabzian & Mohammad Ali Shafia & Mehdi Ghazanfari & Ali Bonyadi Naeini, 2020. "Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory," Sustainability, MDPI, vol. 12(7), pages 1-36, April.
    16. Parcu, Pier Luigi & Innocenti, Niccolò & Carrozza, Chiara, 2022. "Ubiquitous technologies and 5G development. Who is leading the race?," Telecommunications Policy, Elsevier, vol. 46(4).
    17. Jiang, Man & Yang, Siluo & Gao, Qiang, 2024. "Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow," Journal of Informetrics, Elsevier, vol. 18(1).
    18. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    19. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    20. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    21. Lee, Sungjoo & Lee, Hakyeon & Lee, Changyong, 2020. "Open innovation at the national level: Towards a global innovation system," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    22. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    23. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2018. "Knowledge Push Curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers' competitiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 150-160.
    24. Noh, Heeyong & Lee, Sungjoo, 2020. "What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives," Technological Forecasting and Social Change, Elsevier, vol. 157(C).

    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. Grimaldi, Michele & Cricelli, Livio & Di Giovanni, Martina & Rogo, Francesco, 2015. "The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 286-302.
    2. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Carsten C. Guderian, 2019. "Identifying Emerging Technologies with Smart Patent Indicators: The Example of Smart Houses," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 1-24, April.
    4. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    5. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    6. Xie, Zongjie & Hall, Jeremy & McCarthy, Ian P. & Skitmore, Martin & Shen, Liyin, 2016. "Standardization efforts: The relationship between knowledge dimensions, search processes and innovation outcomes," Technovation, Elsevier, vol. 48, pages 69-78.
    7. Lee, Won Sang & Sohn, So Young, 2018. "Effects of standardization on the evolution of information and communications technology," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 308-317.
    8. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    9. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.
    10. Nicola Melluso & Andrea Bonaccorsi & Filippo Chiarello & Gualtiero Fantoni, 2021. "Rapid detection of fast innovation under the pressure of COVID-19," Papers 2102.00197, arXiv.org.
    11. Nicolas van Zeebroeck & Bruno van Pottelsberghe de la Potterie, 2011. "Filing strategies and patent value," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(6), pages 539-561, February.
    12. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Raven, Michael & Blind, Knut, 2017. "The characteristics and impacts of scientific publications in biotechnology research referenced in standards," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 167-179.
    14. Wen, Wen & Forman, Chris & Jarvenpaa, Sirkka L, 2022. "The effects of technology standards on complementor innovations: Evidence from the IETF," Research Policy, Elsevier, vol. 51(6).
    15. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    16. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    17. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    18. Dong Geun Choi & Heesang Lee & Tae-kyung Sung, 2011. "Research profiling for ‘standardization and innovation’," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 259-278, July.
    19. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    20. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.

    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:eee:telpol:v:40:y:2016:i:10:p:956-970. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30471/description#description .

    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.