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Exploring potential R&D collaborators with complementary technologies: The case of biosensors

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  • Wang, Ming-Yeu

Abstract

The study provides a framework for exploring potential R&D collaborators with technological complementarity in products consisting of multidisciplinary technologies. This framework is proper when firms have insufficient information on who may possess the desired complementary technologies. The proposed framework applies two exploratory methods to patent information. The first method, association analysis, mines the interaction between different technologies for the studied products, and produces results that are useful to understanding the complementarity of various technologies. The proposed framework then uses nonlinear principal components analysis to determine the relationship among integrated technologies, specific technology fields, and patentees. The proposed method allows firms to identify patentees with complementary technologies and locate potential R&D collaborators. This study uses an empirical case from the biosensor industry to illustrate how to identify potential R&D collaborators.

Suggested Citation

  • Wang, Ming-Yeu, 2012. "Exploring potential R&D collaborators with complementary technologies: The case of biosensors," Technological Forecasting and Social Change, Elsevier, vol. 79(5), pages 862-874.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:5:p:862-874
    DOI: 10.1016/j.techfore.2011.11.002
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    Cited by:

    1. Sanja Puzović & Jasmina Vesić Vasović & Dragan D. Milanović & Vladan Paunović, 2023. "A Hybrid Fuzzy MCDM Approach to Open Innovation Partner Evaluation," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
    2. Zhang, Guiyang & Tang, Chaoying, 2017. "How could firm's internal R&D collaboration bring more innovation?," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 299-308.
    3. Song, Bomi & Seol, Hyeonju & Park, Yongtae, 2016. "A patent portfolio-based approach for assessing potential R&D partners: An application of the Shapley value," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 156-165.
    4. Adrián Kovács & Bart Looy & Bruno Cassiman, 2015. "Exploring the scope of open innovation: a bibliometric review of a decade of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 951-983, September.
    5. Denicolai, Stefano & Ramirez, Matias & Tidd, Joe, 2016. "Overcoming the false dichotomy between internal R&D and external knowledge acquisition: Absorptive capacity dynamics over time," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 57-65.
    6. Kim, Hyunwoo & Hong, Suckwon & Kwon, Ohjin & Lee, Changyong, 2017. "Concentric diversification based on technological capabilities: Link analysis of products and technologies," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 246-257.
    7. Arroyabe, Marta F. & Arranz, Nieves & Fdez. de Arroyabe, Juan Carlos, 2015. "R&D partnerships: An exploratory approach to the role of structural variables in joint project performance," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 623-634.
    8. Greco, Marco & Grimaldi, Michele & Cricelli, Livio, 2017. "Hitting the nail on the head: Exploring the relationship between public subsidies and open innovation efficiency," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 213-225.

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