IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v127y2018icp281-290.html
   My bibliography  Save this article

Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology

Author

Listed:
  • Wang, Yu-Hui
  • Hsieh, Chia-Ching

Abstract

Advances in wireless Internet and mobile communications devices have driven significant development in the Internet of Things (IoT), bringing a stream of innovative technologies and services. This study explores the technology innovation and intelligence for IoT (internet of things) based eyewear technology. This study proposes a two-stage patent analysis based on the quality function development (QFD) method which adopts customer requirement and technology viewpoints to explore key technologies. This methodology can recognize the specific technologies with development potential in the eyewear industry, and identify holders of key relevant patents. This study finds that consumers value functions including motion tracking, reminders, eye state detection, and non-eye disease detection. Key technologies with development potential for satisfying customer demand include eyewear, communications protocols, and sensors. Thus, embedding micron-scale sensors directly into contact lenses to monitor user physiological data can satisfy customer demand and is considered as emerging technology in the smart eyewear industry. Furthermore, patent portfolios of these technologies vary among different countries and regions, with the US and EU focusing on eye tracking, motion tracking, and identity verification, while China focuses on eye fatigue detection, distance measurement, and wireless frequency technologies. Visualizations of overall research results can benefit eyewear-related patent holders, eyewear manufacturers and smart wearable manufacturers to build their patent portfolio strategies on the basis of regional or country considerations.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:127:y:2018:i:c:p:281-290
    DOI: 10.1016/j.techfore.2017.10.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162516304383
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2017.10.001?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. Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    2. 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.
    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. Ly, Pham Thi Minh & Lai, Wen-Hsiang & Hsu, Chiung-Wen & Shih, Fang-Yin, 2018. "Fuzzy AHP analysis of Internet of Things (IoT) in enterprises," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 1-13.
    2. Federica Murmura & Laura Bravi & Gilberto Santos, 2021. "Sustainable Process and Product Innovation in the Eyewear Sector: The Role of Industry 4.0 Enabling Technologies," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    3. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    4. Magni, Domitilla & Scuotto, Veronica & Pezzi, Alberto & Giudice, Manlio Del, 2021. "Employees’ acceptance of wearable devices: Towards a predictive model," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Lo, Fang-Yi & Campos, Nayara, 2018. "Blending Internet-of-Things (IoT) solutions into relationship marketing strategies," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 10-18.

    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 & 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.
    2. 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.
    3. 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.
    4. Yan, Hong-Bin & Li, Ming, 2022. "Consumer demand based recombinant search for idea generation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. 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.
    6. Nicoló Barbieri & François Perruchas & Davide Consoli, 2020. "Specialization, Diversification, and Environmental Technology Life Cycle," Economic Geography, Taylor & Francis Journals, vol. 96(2), pages 161-186, March.
    7. 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.
    8. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Yoonjung An & Mintak Han & Yongtae Park, 2017. "Identifying dynamic knowledge flow patterns of business method patents with a hidden Markov model," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 783-802, November.
    10. 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.
    11. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    12. 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.
    13. Fredström, Ashkan & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak & Parida, Vinit, 2021. "Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    14. Ryosuke L. Ohniwa & Aiko Hibino, 2019. "Generating process of emerging topics in the life sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1549-1561, December.
    15. Jose M. Vicente-Gomila & Anna Palli & Begoña Calle & Miguel A. Artacho & Sara Jimenez, 2017. "Discovering shifts in competitive strategies in probiotics, accelerated with TechMining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1907-1923, June.
    16. 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).
    17. 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.
    18. 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.
    19. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    20. 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.

    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:127:y:2018:i:c:p:281-290. 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.