System for Analyzing Innovation Activities in Mergers and Acquisitions by Measuring Technological Distance
System for Analyzing Innovation Activities in Mergers and Acquisitions by Measuring Technological Distance
复制标题
通过测量技术距离分析并购创新活动的系统
DOI：
10.1007/978-981-19-3359-2_12
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hiroshi Takahashi
中科院分区：
文献类型：
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作者：
Nozomi Tamagawa;Hiroshi Takahashi
本文利用日本、美国和德国的大规模专利数据，构建了企业并购对创新活动影响的分析系统。在分析中，我们重点研究了并购中收购方与目标公司之间的技术相似性。具体而言，我们使用自然语言处理技术对专利文献数据进行分析，并测量企业之间的技术距离。此外，我们利用专利数据度量并购中的创新产出，并根据技术距离的大小对分析目标进行分类。并对公告后的创新产出趋势进行了分析。通过分析，我们证实了在技术距离中等的并购集团中，并购公告后的创新产出有增加的趋势。在这个结果中，其中一个新颖之处在于非结构化数据和机器学习方法在并购和创新研究领域的适用性。
In this study, we construct an analysis system for the impact of mergers and acquisitions (M&A) on innovation activities using large-scale patent data in Japan, the U.S., and German. In the analysis, we focus on the technological similarity between the acquirer and the target companies in M&A. Specifically, we use natural language processing to analyze the patent document data and measure the technological distance among companies. In addition, we measure the innovation output in M&A using the patent data and classify the analysis targets according to the size of the technological distance. We also analyze the trend of the innovation output after the announcement. By conducting the analysis, we confirm that the innovation output after M&A announcement tends to increase in the M&A group with medium technological distance. In this result, one of the novelties is the applicability of unstructured data and machine learning methods to the research field of M&A and innovation.