IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v94y2015icp44-64.html
   My bibliography  Save this article

Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea

Author

Listed:
  • Ju, Yonghan
  • Sohn, So Young

Abstract

R&D planning for emerging technologies that reflect customers' future needs has a crucial role in national economies. In this paper, we propose a hierarchical quality function deployment (QFD) framework that enables one to set R&D priorities and then develop corresponding business models to meet future societal needs. The proposed QFD framework consists of a hierarchical structure with three house-of-quality (HOQ) stages, which are based on patent analysis and opinions of specialists and generalists. Based on the results of the HOQ and the convergence of iterated correlation analysis, prospective technology was identified. We applied the proposed framework to robotics technology in Korea and found that, for robotics R&D, position sensors are the most important emerging technologies, followed by distance sensors and motor-driven technologies. In addition, by utilizing reverse QFD, we suggest business models for cleaning, entertainment, and pet robots. We expect this research to open a new avenue in the R&D planning process.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:tefoso:v:94:y:2015:i:c:p:44-64
    DOI: 10.1016/j.techfore.2014.04.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016251400136X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2014.04.015?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. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    2. Sternitzke, Christian & Bartkowski, Adam & Schramm, Reinhard, 2008. "Visualizing patent statistics by means of social network analysis tools," World Patent Information, Elsevier, vol. 30(2), pages 115-131, June.
    3. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    4. Dietmar Harhoff & Francis Narin & F. M. Scherer & Katrin Vopel, 1999. "Citation Frequency And The Value Of Patented Inventions," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 511-515, August.
    5. Lowe, Antony & Ridgway, Keith & Atkinson, Helen, 2000. "QFD in new production technology evaluation," International Journal of Production Economics, Elsevier, vol. 67(2), pages 103-112, September.
    6. Han, Yoo-Jin & Park, Yongtae, 2006. "Patent network analysis of inter-industrial knowledge flows: The case of Korea between traditional and emerging industries," World Patent Information, Elsevier, vol. 28(3), pages 235-247, September.
    7. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    8. Shin, Juneseuk & Park, Yongtae, 2007. "Building the national ICT frontier: The case of Korea," Information Economics and Policy, Elsevier, vol. 19(2), pages 249-277, June.
    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. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    2. Yan, Hong-Bin & Li, Ming, 2022. "Consumer demand based recombinant search for idea generation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Wu, Xuehui & Wu, Zhong & Hu, Jun, 2022. "Global competitiveness analysis of industrial robot technology innovations market layout using visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. 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.
    5. 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.
    6. Yun Liu & Zhe Yan & Yijie Cheng & Xuanting Ye, 2018. "Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry," Sustainability, MDPI, vol. 10(1), pages 1-23, January.
    7. 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.
    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. Ha, Sohee & Geum, Youngjung, 2022. "Identifying new innovative services using M&A data: An integrated approach of data-driven morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    10. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    11. 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.
    12. Elizabeth Gibson & Tugrul Daim & Edwin Garces & Marina Dabic, 2018. "Technology Foresight: A Bibliometric Analysis to Identify Leading and Emerging Methods," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(1), pages 6-24.
    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. 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.
    15. Hyunsook Shim & Taeyeon Kim & Gyunghyun Choi, 2019. "Technology Roadmap for Eco-Friendly Building Materials Industry," Energies, MDPI, vol. 12(5), pages 1-14, February.
    16. 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.
    17. 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.
    18. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    19. Wang, Yu-Hui & Hsieh, Chia-Ching, 2018. "Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 281-290.
    20. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(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. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    2. 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.
    3. Park, Jongyong & Lee, Hakyeon & Park, Yongtae, 2009. "Disembodied knowledge flows among industrial clusters: A patent analysis of the Korean manufacturing sector," Technology in Society, Elsevier, vol. 31(1), pages 73-84.
    4. Carlo Giglio & Roberto Sbragia & Roberto Musmanno & Roberto Palmieri, 2021. "Cross-country learning from patents: an analysis of citations flows in innovation trajectories," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7917-7936, September.
    5. Carlo Giglio & Gianluca Salvatore Vocaturo & Roberto Palmieri, 2023. "Patent Acquisitions in the Healthcare Industry: An Analysis of Learning Mechanisms," IJERPH, MDPI, vol. 20(5), pages 1-13, February.
    6. Fu, Ben-Ran & Hsu, Sung-Wei & Liu, Chih-Hsi & Liu, Yu-Ching, 2014. "Statistical analysis of patent data relating to the organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 986-994.
    7. Ufuk Akcigit & Murat Celik & Daron Acemoglu, 2014. "Young, Restless and Creative: Openness to Disruption and Creative Innovations," 2014 Meeting Papers 377, Society for Economic Dynamics.
    8. Guan-Can Yang & Gang Li & Chun-Ya Li & Yun-Hua Zhao & Jing Zhang & Tong Liu & Dar-Zen Chen & Mu-Hsuan Huang, 2015. "Using the comprehensive patent citation network (CPC) to evaluate patent value," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1319-1346, December.
    9. Sheikh, Shahbaz, 2018. "The impact of market competition on the relation between CEO power and firm innovation," Journal of Multinational Financial Management, Elsevier, vol. 44(C), pages 36-50.
    10. Avimanyu Datta, 2016. "Antecedents To Radical Innovations: A Longitudinal Look At Firms In The Information Technology Industry By Aggregation Of Patents," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(07), pages 1-31, October.
    11. Silverberg, Gerald & Verspagen, Bart, 2002. "A Percolation Model of Innovation in Complex Technology," Research Memorandum 032, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    12. Boeker, Warren & Howard, Michael D. & Basu, Sandip & Sahaym, Arvin, 2021. "Interpersonal relationships, digital technologies, and innovation in entrepreneurial ventures," Journal of Business Research, Elsevier, vol. 125(C), pages 495-507.
    13. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    14. Yusuke Oh & Koji Takahashi, 2020. "R&D and Innovation: Evidence from Patent Data," Bank of Japan Working Paper Series 20-E-7, Bank of Japan.
    15. Emanuele Bacchiocchi & Fabio Montobbio, 2010. "International Knowledge Diffusion and Home‐bias Effect: Do USPTO and EPO Patent Citations Tell the Same Story?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 112(3), pages 441-470, September.
    16. Hans Georg Helmstädter & Anna Gehlke & Lukasz Hill & Bernd Klöver & Laura Wallor & Christoph Badelt & Matthias Firgo & Oliver Fritz & Kathrin Hofmann & Mark Horridge & Jürgen Janger & Peter Mayerhofer, 2020. "Perspektiven der wissenschaftlichen Metropolregion Hamburg – Eine vergleichende Analyse. Anhänge," WIFO Studies, WIFO, number 62513, February.
    17. Per Botolf Maurseth, 2005. "Lovely but dangerous: The impact of patent citations on patent renewal," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(5), pages 351-374.
    18. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & Manuel Jiménez, 2022. "Effects of knowledge spillovers between competitors on patent quality: what patent citations reveal about a global duopoly," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1451-1487, October.
    19. Cassiman, Bruno & Veugelers, Reinhilde & Arts, Sam, 2018. "Mind the gap: Capturing value from basic research through combining mobile inventors and partnerships," Research Policy, Elsevier, vol. 47(9), pages 1811-1824.
    20. Laura Magazzini & Fabio Pammolli & Massimo Riccaboni & Maria Alessandra Rossi, 2009. "Patent disclosure and R&D competition in pharmaceuticals," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 18(5), pages 467-486.

    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:94:y:2015:i:c:p:44-64. 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.