Tag Archives: M&A

A hybrid similarity measure method for patent portfolio analysis

Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China’s medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.

Highlights

  • An application that introduces fuzzy sets to transform IPCs to numeric values.
  • A 3-level tree structure that arranges terms hierarchically for similarity measure.
  • A study that applies similarity measure for technology mergers and acquisitions.

http://www.sciencedirect.com/science/article/pii/S1751157715302169

Author(s): Yi Zhang, Lining Shang, Lu Huang, Alan L. Porter, Guangquan Zhang, Jie Lu, Donghua Zhu
Organization(s): University of Technology Sydney, Beijing Institute of Technology, Georgia Institute of Technology
Source: Journal of Informetrics
Year: 2016

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

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.

http://dx.doi.org/10.1016/j.techfore.2016.10.061

Author(s): Tingting Ma, Yi Zhang, Lu Huang, Lining Shang, Kangrui Wang, Huizhu Yu, Donghua Zhu
Organization(s): Beijing Wuzi University, Beijing Institute of Technology, University of Technology Sydney
Source: Technological Forecasting and Social Change
Year: 2017

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

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.

http://www.sciencedirect.com/science/article/pii/S0040162516305364

Author(s): Tingting Ma, Yi Zhang, Lu Huang , Lining Shang, Kangrui Wang, Huizhu Yu, Donghua Zhu
Organization(s):  Beijing Wuzi University; Beijing Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2016