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A structured approach to explore technological competencies through R&D portfolio of photovoltaic companies by patent statistics

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  • Kuei-Kuei Lai

    (Chaoyang University of Technology)

  • Chien-Yu Lin

    (National Yunlin University of Science and Technology)

  • Yu-Hsin Chang

    (Chaoyang University of Technology)

  • Ming-Chung Yang

    (National Chin-Yi University of Technology)

  • Wen-Goang Yang

    (Chaoyang University of Technology)

Abstract

When the technological development of an enterprise is path dependent, core technological competencies will develop. In addition, core technological competencies promote technological development. Consequently, enterprises should always examine the advantages of their core technological competencies. Under dynamic competition, enterprises should monitor their own performance as well as their competitors at all times and consequently adjust their technological strategies. This study used two patent indices, Patent Share and Revealed Technological Advantage, to measure the internal core technological competencies of manufacturers. It also integrated four other indices namely: (1) Technology Attractiveness (Relative Growth Rate), (2) growth potential of technologies (Relative Development of Technology Growth Rate), (3) Relative Patent Position, and (4) Revealed Patent Advantages. These were used to analyze the external strengths and weaknesses of the research and development (R&D) portfolios of companies. These two analytical methods can effectively identify the internal core technological competencies and the external advantages of R&D portfolios of leading companies in the solar photovoltaic (PV) industry. This study also discussed the relationship between R&D portfolios and core technological competencies of leading solar photovoltaic companies and compared those with two core technological competencies with those that have a single core technological competence. The study results show that the R&D portfolios of companies engaged in a single, specific technology field have advantages. This study helps improve the quality of technological planning and decision-making of manufacturers, proposes a method of using core technological competencies to analyze the advantages of R&D portfolios, and helps solar PV manufacturers monitor their own core technological competencies as well as their competitors and partner companies.

Suggested Citation

  • Kuei-Kuei Lai & Chien-Yu Lin & Yu-Hsin Chang & Ming-Chung Yang & Wen-Goang Yang, 2017. "A structured approach to explore technological competencies through R&D portfolio of photovoltaic companies by patent statistics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1327-1351, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2376-0
    DOI: 10.1007/s11192-017-2376-0
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    References listed on IDEAS

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    1. Fabry, Bernd & Ernst, Holger & Langholz, Jens & Köster, Martin, 2006. "Patent portfolio analysis as a useful tool for identifying R&D and business opportunities--an empirical application in the nutrition and health industry," World Patent Information, Elsevier, vol. 28(3), pages 215-225, September.
    2. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    3. Ernst, Holger, 2001. "Patent applications and subsequent changes of performance: evidence from time-series cross-section analyses on the firm level," Research Policy, Elsevier, vol. 30(1), pages 143-157, January.
    4. repec:fth:harver:1473 is not listed on IDEAS
    5. Ching-Yan Wu, 2014. "Comparisons of technological innovation capabilities in the solar photovoltaic industries of Taiwan, China, and Korea," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 429-446, January.
    6. Constance E. Helfat, 1994. "Firm-Specificity in Corporate Applied R&D," Organization Science, INFORMS, vol. 5(2), pages 173-184, May.
    7. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
    8. Patel, Pari & Pavitt, Keith, 1997. "The technological competencies of the world's largest firms: Complex and path-dependent, but not much variety," Research Policy, Elsevier, vol. 26(2), pages 141-156, May.
    9. Marc H. Meyer & Edward B. Roberts, 1986. "New Product Strategy in Small Technology-Based Firms: A Pilot Study," Management Science, INFORMS, vol. 32(7), pages 806-821, July.
    10. Richard A. Bettis & Michael A. Hitt, 1995. "The new competitive landscape," Strategic Management Journal, Wiley Blackwell, vol. 16(S1), pages 7-19.
    11. Brockhoff, Klaus K. & Ernst, Holger & Hundhausen, Eckhard, 1999. "Gains and pains from licensing: Patent-portfolios as strategic weapons in the cardiac rhythm management industry," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 497, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    12. Wu, Ching-Yan & Mathews, John A., 2012. "Knowledge flows in the solar photovoltaic industry: Insights from patenting by Taiwan, Korea, and China," Research Policy, Elsevier, vol. 41(3), pages 524-540.
    13. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    14. Parida, Bhubaneswari & Iniyan, S. & Goic, Ranko, 2011. "A review of solar photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1625-1636, April.
    15. John Mathews & Mei-Chih Hu & Ching-Yan Wu, 2011. "Fast-Follower Industrial Dynamics: The Case of Taiwan's Emergent Solar Photovoltaic Industry," Industry and Innovation, Taylor & Francis Journals, vol. 18(2), pages 177-202.
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    2. Xiaodong Yuan & Weiling Song, 2022. "Evaluating technology innovation capabilities of companies based on entropy- TOPSIS: the case of solar cell companies," Information Technology and Management, Springer, vol. 23(2), pages 65-76, June.

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