Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining
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- Title
- Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining
- Authors
- Park, H; Yoon, J; Kim, K
- Date Issued
- 2013-12
- Publisher
- Springer
- Abstract
- This paper proposes a framework to identify and evaluate companies from the technological perspective to support merger and acquisition (M&A) target selection decision-making. This employed a text mining-based patent map approach to identify companies which can fulfill a specific strategic purpose of M&A for enhancing technological capabilities. The patent map is the visualized technological landscape of a technology industry by using technological proximities among patents, so companies which closely related to the strategic purpose can be identified. To evaluate the technological aspects of the identified companies, we provide the patent indexes that evaluate both current and future technological capabilities and potential technology synergies between acquiring and acquired companies. Furthermore, because the proposed method evaluates potential targets from the overall corporate perspective and the specific strategic perspectives simultaneously, more robust and meaningful result can be obtained than when only one perspective is considered. Thus, the proposed framework can suggest the appropriate target companies that fulfill the strategic purpose of M&A for enhancing technological capabilities. For the verification of the framework, we provide an empirical study using patent data related to flexible display technology.
- Keywords
- M&A target selection; Technology acquisition; Patent analysis; Subject-action-object; SAO; Technological similarity; RESEARCH-AND-DEVELOPMENT; TECHNOLOGY; INNOVATION; CAPABILITIES; ALLIANCES; DECISIONS; KNOWLEDGE; BUSINESS; INDUSTRY; DATABASE
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/15257
- DOI
- 10.1007/S11192-013-1010-Z
- ISSN
- 0138-9130
- Article Type
- Article
- Citation
- Scientometrics, vol. 97, no. 3, page. 883 - 909, 2013-12
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