기업의 지식기반 특성이 기술혁신형 M&A 후 혁신 성과에 미치는 영향: 머신러닝 기반 지식 분류를 이용한 실증 연구

This study is an empirical research on the impact of acquiring firm’s knowledge base characteristics on technological M&A innovation performance using a novel machine-learning-based knowledge classification system. Based on extant literature on absorptive capacity and innovation performance, two hypotheses were established on the impact of acquiring firm’s knowledge diversity and similarity of knowledge base between acquiring firm and acquired firm on post-M&A innovation performance. The validity of machine-learning-base new knowledge classification system was confirmed by empirically analyzing these two hypotheses. In addition, we define knowledge base flexibility as the degree of knowledge a firm has in areas that cannot be defined by the extant classification system, and empirically verify whether the flexibility can affect post-M&A innovation performance. As a result of the empirical analysis, we find that the diversity of acquiring firm’s knowledge base and the similarity in the knowledge field between the acquiring and acquired firms have positive impact on the number of post-M&A innovation, while the quality of the innovation was not affected. Moreover, it was found that acquiring firm’s knowledge flexibility has a positive effect on the quantity of post-M&A innovation, but there was no significant correlation with the quality of post-M&A innovation. Through these empirical tests, we derive important theoretical implications on the relationship between the knowledge base characteristics and knowledge transfer via technological M&A. In addition, this research provides critical strategic insight that can be applied in determining M&A partners for managers and practitioners who are looking to find new technological opportunities from outside the firm via M&A. Lastly, this research suggests a new research direction in the field of technology management by utilizing natural language processing and machine learning.