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Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry

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  • Ma, Tingting
  • Zhang, Yi
  • Huang, Lu
  • Shang, Lining
  • Wang, Kangrui
  • Yu, Huizhu
  • Zhu, Donghua

Abstract

Technology strategy plays an increasingly important role in today's Mergers and Acquisitions (M&A) activities. Informing that strategy with empirical intelligence offers great potential value to R&D managers and technology policy makers. This paper proposes a methodology, based on patent analysis, to extract technical intelligence to identify M&A target technologies and evaluate relevant target companies to facilitate M&A target selection. We apply the term clumping process and a trend analysis together with policy and market information to profile present R&D status and capture future development signals and trends in order to grasp a range of significant domain-based technologies. Furthermore, a comparison between a selected acquirer and leading players is used to identify significant technologies and sub-technologies for specific strategy-oriented technology M&A activities. Finally, aiming to recommend appropriate M&A target companies, we set up an index-based system to evaluate the acquired target candidates from both firms-side perspective and target firm-side perspective and differentially weigh for specific M&A situations. We provide an empirical study in the field of computer numerical control machine tools (CNCMT) in China to identify technology M&A targets for an emerging Chinese CNCMT company — Estun Automation under different M&A strategies.

Suggested Citation

  • Ma, Tingting & Zhang, Yi & Huang, Lu & Shang, Lining & Wang, Kangrui & Yu, Huizhu & Zhu, Donghua, 2017. "Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 162-180.
    • Handle: RePEc:eee:tefoso:v:116:y:2017:i:c:p:162-180
    DOI: 10.1016/j.techfore.2016.10.061
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    Cited by:

    1. Xu, Jianguo & Guo, Lixiang & Jiang, Jiang & Ge, Bingfeng & Li, Mengjun, 2019. "A deep learning methodology for automatic extraction and discovery of technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 339-351.
    2. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).

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