인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Firms in high-tech industries frequently pursue technological acquisitions to enhance innovation and sustain a competitive advantage. While prior research has offered valuable insights into organizational-level outcomes, relatively little attention has been paid to individual-level consequences, particularly for employees in the acquiring firm who constitute the technical core of innovation. This study examines how social dynamics that unfold in the post-acquisition context shape the innovation productivity of these acquirer inventors. Drawing on network theory, we argue that competitive crowding―defined as intensified rivalry with inventors from the acquired firm within overlapping technological domains―inhibits collaboration and intensifies competition for organizational resources and recognition. These dynamics contribute to a productivity slowdown during the initial post-acquisition period. We further suggest that the impact of competitive crowding is contingent upon the network context, specifically the degree of status similarity and the extent of network segregation. Analyzing data on 130,600 acquiring inventors involved in U.S.
high-tech acquisitions between 2002 and 2015, we find empirical support for these claims. By uncovering how social structures influence individual productivity during periods of organizational change, this research contributes significantly to the acquisition literature and broadens the theoretical scope of network theory.
high-tech acquisitions between 2002 and 2015, we find empirical support for these claims. By uncovering how social structures influence individual productivity during periods of organizational change, this research contributes significantly to the acquisition literature and broadens the theoretical scope of network theory.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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