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Measuring strategic technological strength :Patent Portfolio Model

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  • Li, Shuying
  • Zhang, Xian
  • Xu, Haiyun
  • Fang, Shu
  • Garces, Edwin
  • Daim, Tugrul

Abstract

As technological innovation plays an important role in today's knowledge economy, the most important output of technology development is intellectual property, which is highly valued for generating a monopoly position in providing payoffs to innovation. In this context, this paper considers Intellectual Property Management (IPM) efficiency based on the Patent Portfolio Model (PPM) to help organizations identify, enhance, and evaluate their technological strength. The Patent Portfolio Model (PPM) is built to assess the advantages and disadvantages of an organization, to identify the opportunities of development potentials and optimal distribution, to support the decision-making for optimizing resource allocation, and to develop a layout for the technical field. The case study of the Research Institute of China shows that this method is feasible and fulfills the needs of different institutions to provide suggestions for R&D technology management. The main finding of the paper is that PPM is an effective tool to be used in strategic planning because it identifies the technology advantages to define offensive and defensive strategies against competitors. The use of IPM and PPM helps decision-makers to visualize and simplify complex decision-making problems.

Suggested Citation

  • Li, Shuying & Zhang, Xian & Xu, Haiyun & Fang, Shu & Garces, Edwin & Daim, Tugrul, 2020. "Measuring strategic technological strength :Patent Portfolio Model," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:tefoso:v:157:y:2020:i:c:s0040162520309458
    DOI: 10.1016/j.techfore.2020.120119
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    2. Yang, Zaoli & Wu, Qingyang & Venkatachalam, K. & Li, Yuchen & Xu, Bing & Trojovský, Pavel, 2022. "Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    4. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    5. Huang, Kenneth Guang-Lih & Huang, Can & Shen, Huijun & Mao, Hao, 2021. "Assessing the value of China's patented inventions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    6. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    7. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).

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