Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry
Huizhu Yu and
Technological Forecasting and Social Change, 2017, vol. 116, issue C, 162-180
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.
Keywords: Technical intelligence; Mergers and acquisitions; Patent analysis; Text mining (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:116:y:2017:i:c:p:162-180
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