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Identifying Targets for Technology Mergers and Acquisitions Using Patent Information and Semantic Analysis

In: Anticipating Future Innovation Pathways Through Large Data Analysis

Author

Listed:
  • Lu Huang

    (Beijing Institute of Technology)

  • Lining Shang

    (Beijing Institute of Technology)

  • Kangrui Wang

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology, Technology Policy & Assessment Ctr.
    Search Technology Inc.)

  • Yi Zhang

    (Beijing Institute of Technology
    University of Technology Sydney)

Abstract

Technology plays an increasingly important role in today’s enterprise competition. Technology mergers and acquisitions (Tech M&A), as an effective way to acquire the external technology resources rapidly, have attracted attention from researchers for their potential realization of value through synergy. A big challenge is how to identify appropriate targets to support the effective technology integration. In this study, we developed a model of target selection of Tech M&A from the perspective of technology relatedness and R&D capability. We present results for the Tech M&A case in China’s cloud computing industry.

Suggested Citation

  • Lu Huang & Lining Shang & Kangrui Wang & Alan L. Porter & Yi Zhang, 2016. "Identifying Targets for Technology Mergers and Acquisitions Using Patent Information and Semantic Analysis," Innovation, Technology, and Knowledge Management, in: Tugrul U. Daim & Denise Chiavetta & Alan L. Porter & Ozcan Saritas (ed.), Anticipating Future Innovation Pathways Through Large Data Analysis, chapter 0, pages 173-186, Springer.
    • Handle: RePEc:spr:innchp:978-3-319-39056-7_10
    DOI: 10.1007/978-3-319-39056-7_10
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    Cited by:

    1. Katsuyuki Kaneko & Yuya Kajikawa, 2023. "Novelty Score and Technological Relatedness Measurement Using Patent Information in Mergers and Acquisitions: Case Study in the Japanese Electric Motor Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 163-177, June.

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