IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v144y2019icp205-220.html
   My bibliography  Save this article

Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies

Author

Listed:
  • Zhou, Yuan
  • Dong, Fang
  • Kong, Dejing
  • Liu, Yufei

Abstract

The convergence of multi-disciplinary knowledge may spur emerging technologies. It is important to understand this convergence process that helps to identify these emergent technologies; however, relevant research remains sparse. Therefore, this study aims to develop a novel framework to reveal the convergence process of scientific knowledge. This novel framework integrates the machine-learning topology clustering and visualization methods, and analyzes paper citation networks. This study selects the biological–informatics domain (bioinformatics) to conduct the empirical analysis. This paper finds two major stages throughout the convergence process: the fast-changing incubation stage and the stabilized development stage. In the incubation stage, the interactions between the biology and informatics knowledge domains becomes increasingly intensive, while emergent technology is yet to form; in the stable development stage, the emergent technology starts to form as a core cluster, and based on which it grows amid stabilized knowledge interactions between the original two domains. The revelation of this convergence process contributes to the formation theory of emerging technologies that are inter-disciplinary, and is of great interest to researchers, policy makers, and industrialists.

Suggested Citation

  • Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    • Handle: RePEc:eee:tefoso:v:144:y:2019:i:c:p:205-220
    DOI: 10.1016/j.techfore.2019.03.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162518310096
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2019.03.014?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. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large‐scale journal database," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    2. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    3. Yan Zhang & Haiyang Li, 2010. "Innovation search of new ventures in a technology cluster: the role of ties with service intermediaries," Strategic Management Journal, Wiley Blackwell, vol. 31(1), pages 88-109, January.
    4. Barnes, Stuart J. & Mattsson, Jan, 2016. "Understanding current and future issues in collaborative consumption: A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 200-211.
    5. Rosenberg, Nathan, 1963. "Technological Change in the Machine Tool Industry, 1840–1910," The Journal of Economic History, Cambridge University Press, vol. 23(4), pages 414-443, December.
    6. Gambardella, Alfonso & Torrisi, Salvatore, 1998. "Does technological convergence imply convergence in markets? Evidence from the electronics industry," Research Policy, Elsevier, vol. 27(5), pages 445-463, September.
    7. Ismael Rafols & Martin Meyer, 2010. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 263-287, February.
    8. Geum, Youngjung & Kim, Moon-Soo & Lee, Sungjoo, 2016. "How industrial convergence happens: A taxonomical approach based on empirical evidences," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 112-120.
    9. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    10. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    11. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    12. Kim, Dong-hyu & Lee, Heejin & Kwak, Jooyoung, 2017. "Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network," Research Policy, Elsevier, vol. 46(7), pages 1234-1254.
    13. Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    14. Xing, Wan & Ye, Xuan & Kui, Lv, 2011. "Measuring convergence of China's ICT industry: An input-output analysis," Telecommunications Policy, Elsevier, vol. 35(4), pages 301-313, May.
    15. Jaeyong Song & Paul Almeida & Geraldine Wu, 2003. "Learning--by--Hiring: When Is Mobility More Likely to Facilitate Interfirm Knowledge Transfer?," Management Science, INFORMS, vol. 49(4), pages 351-365, April.
    16. Paola Criscuolo, 2006. "The 'home advantage' effect and patent families. A comparison of OECD triadic patents, the USPTO and the EPO," Scientometrics, Springer;Akadémiai Kiadó, vol. 66(1), pages 23-41, January.
    17. Ávila-Robinson, Alfonso & Miyazaki, Kumiko, 2013. "Dynamics of scientific knowledge bases as proxies for discerning technological emergence — The case of MEMS/NEMS technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1071-1084.
    18. 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.
    19. Matti Karvonen & Matti Lehtovaara & Tuomo Kässi, 2012. "Build-Up Of Understanding Of Technological Convergence: Evidence From Printed Intelligence Industry," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-24.
    20. Kim, Namil & Lee, Hyeokseong & Kim, Wonjoon & Lee, Hyunjong & Suh, Jong Hwan, 2015. "Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data," Research Policy, Elsevier, vol. 44(9), pages 1734-1748.
    21. Andy Stirling, 2007. "A General Framework for Analysing Diversity in Science, Technology and Society," SPRU Working Paper Series 156, SPRU - Science Policy Research Unit, University of Sussex Business School.
    22. Xu, Guannan & Wu, Yuchen & Minshall, Tim & Zhou, Yuan, 2018. "Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 208-221.
    23. 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.
    24. Pao-Long Chang & Chao-Chan Wu & Hoang-Jyh Leu, 2010. "Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 5-19, January.
    25. Yuan Zhou & Meijuan Pan & Frauke Urban, 2018. "Comparing the International Knowledge Flow of China’s Wind and Solar Photovoltaic (PV) Industries: Patent Analysis and Implications for Sustainable Development," Sustainability, MDPI, vol. 10(6), pages 1-34, June.
    26. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    27. Liu, Haiyan & Yu, Jianning & Xu, Jian & Fan, Yu & Bao, Xiaojun, 2007. "Identification of key oil refining technologies for China National Petroleum Co. (CNPC)," Energy Policy, Elsevier, vol. 35(4), pages 2635-2647, April.
    28. Chen, Hongshu & Zhang, Guangquan & Zhu, Donghua & Lu, Jie, 2017. "Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 39-52.
    29. Ta-Shun Cho & Hsin-Yu Shih, 2011. "Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 795-811, December.
    30. Hyunseok Park & Janghyeok Yoon & Kwangsoo Kim, 2012. "Identifying patent infringement using SAO based semantic technological similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 515-529, February.
    31. Nordensvard, Johan & Zhou, Yuan & Zhang, Xiao, 2018. "Innovation core, innovation semi-periphery and technology transfer: The case of wind energy patents," Energy Policy, Elsevier, vol. 120(C), pages 213-227.
    32. Fai, Felicia & von Tunzelmann, Nicholas, 2001. "Industry-specific competencies and converging technological systems: evidence from patents," Structural Change and Economic Dynamics, Elsevier, vol. 12(2), pages 141-170, July.
    33. Kong, Dejing & Zhou, Yuan & Liu, Yufei & Xue, Lan, 2017. "Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 80-97.
    34. Sakata, Ichiro & Sasaki, Hajime & Akiyama, Masanori & Sawatani, Yuriko & Shibata, Naoki & Kajikawa, Yuya, 2013. "Bibliometric analysis of service innovation research: Identifying knowledge domain and global network of knowledge," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1085-1093.
    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. Mario Coccia, 2020. "The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 451-487, July.
    2. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    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. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    5. Zhu, Chen & Motohashi, Kazuyuki, 2022. "Identifying the technology convergence using patent text information: A graph convolutional networks (GCN)-based approach," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    6. 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).
    7. Ruxu Sheng & Juntian Du & Songqi Liu & Changan Wang & Zidi Wang & Xiaoqian Liu, 2021. "Solar Photovoltaic Investment Changes across China Regions Using a Spatial Shift-Share Analysis," Energies, MDPI, vol. 14(19), pages 1-14, October.
    8. Jinho Choi & Nina Shin & Hee Soo Lee, 2020. "Exploring the Dynamics between M&A Activities and Industry-Level Performance," Sustainability, MDPI, vol. 12(11), pages 1-24, May.
    9. Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    10. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    11. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    12. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    13. Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
    14. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    15. Mario Coccia, 2019. "How do scientific disciplines evolve in applied sciences? The properties of scientific fission and ambidextrous scientific drivers," Papers 1911.05363, arXiv.org.
    16. Chen Zhu & Kazuyuki Motohashi, 2023. "Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3035-3065, May.
    17. 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).
    18. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    19. Xie, Xuemei & Liu, Xiaojie & Chen, Jialing, 2023. "A meta-analysis of the relationship between collaborative innovation and innovation performance: The role of formal and informal institutions," Technovation, Elsevier, vol. 124(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. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    2. 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.
    3. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Wenjing Zhu & Bohong Ma & Lele Kang, 2022. "Technology convergence among various technical fields: improvement of entropy estimation in patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7731-7750, December.
    5. Yawei Wang & Frauke Urban & Yuan Zhou & Luyi Chen, 2018. "Comparing the Technology Trajectories of Solar PV and Solar Water Heaters in China: Using a Patent Lens," Sustainability, MDPI, vol. 10(11), pages 1-29, November.
    6. 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.
    7. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    8. Sick, Nathalie & Preschitschek, Nina & Leker, Jens & Bröring, Stefanie, 2019. "A new framework to assess industry convergence in high technology environments," Technovation, Elsevier, vol. 84, pages 48-58.
    9. Jihong Chen & Kai Zhang & Yuan Zhou & Yufei Liu & Lingfeng Li & Zheng Chen & Li Yin, 2019. "Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning," Sustainability, MDPI, vol. 11(12), pages 1-38, June.
    10. Pan, Maomao & Bai, Min & Ren, Xiaoxiao, 2022. "Does internet convergence improve manufacturing enterprises’ competitive advantage? Empirical research based on the mediation effect model," Technology in Society, Elsevier, vol. 69(C).
    11. Patrick Wolf & Tobias Buchmann, 2021. "Analyzing development patterns in research networks and technology," Review of Evolutionary Political Economy, Springer, vol. 2(1), pages 55-81, April.
    12. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    13. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    14. Kim, Juram & Kim, Seungho & Lee, Changyong, 2019. "Anticipating technological convergence: Link prediction using Wikipedia hyperlinks," Technovation, Elsevier, vol. 79(C), pages 25-34.
    15. Seongkyoon Jeong & Jong-Chan Kim & Jae Young Choi, 2015. "Technology convergence: What developmental stage are we in?," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 841-871, September.
    16. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
    17. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    18. 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).
    19. Kim, Namil & Lee, Hyeokseong & Kim, Wonjoon & Lee, Hyunjong & Suh, Jong Hwan, 2015. "Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data," Research Policy, Elsevier, vol. 44(9), pages 1734-1748.
    20. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.

    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:tefoso:v:144:y:2019:i:c:p:205-220. 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.sciencedirect.com/science/journal/00401625 .

    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.