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Technological innovation strategies in converging industries

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  • Matti Karvonen
  • Tuomo Kassi
  • Rahul Kapoor

Abstract

The objective of this paper is to analyse how industry convergence and technological trajectories affect the evolution of industries. The paper identifies different convergence types and utilises the framework to analyse the evolution of RFID markets. Research material is based on the patents and financial data of the main RFID industry players. RFID companies were characterised into four different clusters under the following headings: vertically integrated players, upstream focused players, downstream players, and paper and printing companies. The results reveal that the emerging industry is in the early stages of evolution where market dynamics are driven by complementary technological convergence. Patent and market data analysis also reveals that the previously distinct technological trajectories of the industries have started to overlap and merge to form new products based on integrated technologies. In parallel with convergence a restructuring of industries is taking place in the near future.

Suggested Citation

  • Matti Karvonen & Tuomo Kassi & Rahul Kapoor, 2010. "Technological innovation strategies in converging industries," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 4(5), pages 391-410.
    • Handle: RePEc:ids:ijbire:v:4:y:2010:i:5:p:391-410
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    Cited by:

    1. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    2. Lee, Hyunmin, 2023. "Converging technology to improve firm innovation competencies and business performance: Evidence from smart manufacturing technologies," Technovation, Elsevier, vol. 123(C).
    3. Song, Chie Hoon & Elvers, David & Leker, Jens, 2017. "Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 98-115.
    4. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).

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