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Using patent analysis to establish technological position: Two different strategic approaches

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  • Chang, Shann-Bin

Abstract

Discussions on business strategy formation in the past 50years can be separated into two categories: the inside-out and the outside-in approach. Technology is a critical factor when manager formulate their business strategy, and patents have served as an important indicator of technology. A patent portfolio can be used to understand the capabilities of a firm, as an inside resource pattern; and the patent citation of firms can be used to find the relationship of a firm, as an outside dependency. This study uses patent information to establish an effective model for the technological position of business methods. The 5 by 6 matrix was generated and four situations between firms were induced. Researchers and managers can use that matrix and situations to recognize the real competitors or cooperators, and formulate the technological strategies which include competition, cooperation, or complementary cooperation.

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  • Chang, Shann-Bin, 2012. "Using patent analysis to establish technological position: Two different strategic approaches," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 3-15.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:1:p:3-15
    DOI: 10.1016/j.techfore.2011.07.002
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    Cited by:

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    2. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    3. Pantano, Eleonora & Pizzi, Gabriele, 2020. "Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    4. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    5. H. Simon & N. Sick, 2016. "Technological distance measures: new perspectives on nearby and far away," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1299-1320, June.
    6. Kuan, Chung-Huei & Chen, Dar-Zen & Huang, Mu-Hsuan, 2019. "Bibliographically coupled patents: Their temporal pattern and combined relevance," Journal of Informetrics, Elsevier, vol. 13(4).
    7. Johannes VAN DER POL, 2016. "The French Aerospace Sector Collaboration Network : Structural Dynamics And Firm Performance," Cahiers du GREThA (2007-2019) 2016-24, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    8. 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).
    9. Johannes van Der Pol, 2015. "Structural dynamics of the French aerospace sector: A network analysis," Working Papers hal-01284993, HAL.
    10. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    11. Antonio Messeni Petruzzelli & Daniele Rotolo & Vito Albino, 2014. "Determinants of Patent Citations in Biotechnology: An Analysis of Patent Influence Across the Industrial and Organizational Boundaries," SPRU Working Paper Series 2014-05, SPRU - Science Policy Research Unit, University of Sussex Business School.
    12. Li, Yung-Ta & Huang, Mu-Hsuan & Chen, Dar-Zen, 2014. "Positioning and shifting of technology focus for integrated device manufacturers by patent perspectives," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 363-375.
    13. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    14. Faria, Lourenço Galvão Diniz & Andersen, Maj Munch, 2017. "Sectoral patterns versus firm-level heterogeneity - The dynamics of eco-innovation strategies in the automotive sector," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 266-281.
    15. Kato, Masatoshi & Zhou, Haibo, 2018. "Numerical labor flexibility and innovation outcomes of start-up firms: A panel data analysis," Technovation, Elsevier, vol. 69(C), pages 15-27.
    16. 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.

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