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Issue title: Artificial Intelligence and Advanced Manufacturing (AIAM 2020)
Guest editors: Shengzong Zhou
Article type: Research Article
Authors: Hua, Xinga | Yue, Qib; *
Affiliations: [a] Department of Finance and Economics, Guangdong Youth Vocational College, GuangZhou, China | [b] School of Economics and Management, South China Normal University, GuangZhou, China
Correspondence: [*] Corresponding author. Qi Yue, School of Economics and Management, South China Normal University, GuangZhou, China. E-mail: yueqi@m.scnu.edu.cn.
Abstract: Based on a survey database of cross-border acquisitions by Chinese private firms, this study uses a fuzzy-set qualitative comparative analysis (fsQCA) to explore the holistic impact of acquisition ownership, organizational factors and environmental factors on acquisition performance in cross-border acquisitions. It is found that the cross-border acquisitions taken by Chinese private enterprises have four kinds of acquisition ownership strategies leading to high acquisition performance under different internal and external conditions. This study points out that ownership strategy is a key decision affecting cross-border acquisition performance and provides a variety of paths leading to the same outcome rather than just finding the linear relationship between corporate activity and performance. This study supports the assumption of equivalence, and reveals a variety of scenarios in which cross-border acquisition ownership contributes to the outcome of high cross-border acquisition performance, and further confirms the view of causal asymmetry between condition and outcome. This study reveals whether the proportion of cross-border acquisition ownership affects cross-border acquisition performance and under what circumstances is conducive to the realization of expected cross-border acquisition performance.
Keywords: Fuzzy-set qualitative comparative analysis (fsQCA), acquisition ownership, acquisition performance, cross-border acquisition
DOI: 10.3233/JIFS-189720
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 3, pp. 4597-4607, 2021
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