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Identification of the technology life cycle of telematics: A patent-based analytical perspective

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  • Chang, Shu-Hao
  • Fan, Chin-Yuan

Abstract

Identifying technology life cycles (TLCs), particularly TLCs that relate to promising technology, is crucial to managers, technological product investors, and inventors. Telematics technology has gained prevalence in the information and communication technology fields and been increasingly applied. This study determined the current TLC of telematics and investigated using a mainstream technology and development focus at each TLC stage. A supervised assessment method and the indicator pattern of current anchoring technology were employed, and a significance test of the results generated from a curve matching analysis was used to identify the TLC stages of telematics. The results revealed that telematics is in the maturity stage, and the technological focus of each of its TLC stages is distinct. At the maturity stage, telematics emphasizes wireless communication networks and diversified market applications. We assessed the development stage of telematics; governments can refer to this assessment to facilitate strategic development in technological industries.

Suggested Citation

  • Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    • Handle: RePEc:eee:tefoso:v:105:y:2016:i:c:p:1-10
    DOI: 10.1016/j.techfore.2016.01.023
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    as
    1. Jackie Krafft & Francesco Quatraro & Pier Saviotti, 2014. "Knowledge characteristics and the dynamics of technological alliances in pharmaceuticals: empirical evidence from Europe, US and Japan," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 587-622, July.
    2. Meyer, Martin, 2000. "Does science push technology? Patents citing scientific literature," Research Policy, Elsevier, vol. 29(3), pages 409-434, March.
    3. Iain M. Cockburn & Megan J. MacGarvie & Elisabeth Müller, 2010. "Patent thickets, licensing and innovative performance," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 19(3), pages 899-925, June.
    4. Bessen, James, 2008. "The value of U.S. patents by owner and patent characteristics," Research Policy, Elsevier, vol. 37(5), pages 932-945, June.
    5. Tong, Xuesong & Frame, J. Davidson, 1994. "Measuring national technological performance with patent claims data," Research Policy, Elsevier, vol. 23(2), pages 133-141, March.
    6. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    7. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    8. Bruno Van Pottelsberghe & Eleftherios Sapsalis & Ran Navon, 2006. "Academic vs. industry patenting: an in-depth analysis of what determines patent value," Working Papers CEB 05-008.RS, ULB -- Universite Libre de Bruxelles.
    9. Loet Leydesdorff, 2015. "Can technology life-cycles be indicated by diversity in patent classifications? The crucial role of variety," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1441-1451, December.
    10. Marco Campani & Ruggero Vaglio, 2015. "A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 75-83, April.
    11. 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.
    12. Taylor, Margaret & Taylor, Andrew, 2012. "The technology life cycle: Conceptualization and managerial implications," International Journal of Production Economics, Elsevier, vol. 140(1), pages 541-553.
    13. Thanh-Dong Pham & Byeong-Kyu Lee & Chi Hyeon Lee & Minh Viet Nguyen, 2015. "Emission Control Technology," Chapters, in: Farhad Nejadkoorki (ed.), Current Air Quality Issues, IntechOpen.
    14. Jackie Krafft & Francesco Quatraro & Pier Paolo Saviotti, 2014. "Knowledge characteristics and the dynamics of technological alliances in pharmaceuticals: empirical evidence from Europe, US and Japan," Post-Print hal-02097581, HAL.
    15. E. Bacchiocchi & F. Montobbio, 2009. "Knowledge diffusion from university and public research. A comparison between US, Japan and Europe using patent citations," The Journal of Technology Transfer, Springer, vol. 34(2), pages 169-181, April.
    16. Birgitte Andersen, 1999. "The hunt for S-shaped growth paths in technological innovation: a patent study," Journal of Evolutionary Economics, Springer, vol. 9(4), pages 487-526.
    17. Loet Leydesdorff & Floortje Alkemade & Gaston Heimeriks & Rinke Hoekstra, 2015. "Patents as instruments for exploring innovation dynamics: geographic and technological perspectives on “photovoltaic cells”," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 629-651, January.
    18. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    19. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    20. Lizin, Sebastien & Leroy, Julie & Delvenne, Catherine & Dijk, Marc & De Schepper, Ellen & Van Passel, Steven, 2013. "A patent landscape analysis for organic photovoltaic solar cells: Identifying the technology's development phase," Renewable Energy, Elsevier, vol. 57(C), pages 5-11.
    21. Dubaric, Ervin & Giannoccaro, Dimitris & Bengtsson, Rune & Ackermann, Thomas, 2011. "Patent data as indicators of wind power technology development," World Patent Information, Elsevier, vol. 33(2), pages 144-149, June.
    22. Chen-Yuan Liu & Jhen-Cheng Wang, 2010. "Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 21-36, January.
    23. Yu-Shan Chen & Chun-Yu Shih & Ching-Hsun Chang, 2013. "Patents and market value in the U.S. pharmaceutical industry: new evidence from panel threshold regression," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 161-176, November.
    24. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    25. 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.
    26. Nemet, Gregory F., 2012. "Inter-technology knowledge spillovers for energy technologies," Energy Economics, Elsevier, vol. 34(5), pages 1259-1270.
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    5. Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    6. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    7. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    8. 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.
    9. Ahn, Sang-Jin, 2020. "Three characteristics of technology competition by IoT-driven digitization," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    10. 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).
    11. Vesselkov, Alexandr & Hämmäinen, Heikki & Töyli, Juuso, 2018. "Technology and value network evolution in telehealth," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 207-222.
    12. 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.
    13. Cheng-Wen Lee & Budi Hasyim & Jan-Yan Lin, 2024. "Digital Technology for Supply Chain Management- marketing Integration," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(1), pages 1-4.
    14. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
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    16. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
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