Reverse Knowledge Transfer in Cross-Border Mergers and Acquisitions in the Chinese High-Tech Industry under Government Intervention
Yi Su,
Wen Guo,
Zaoli Yang and
Hocine Cherifi
Complexity, 2021, vol. 2021, 1-18
Abstract:
The high-tech industry is the main force promoting the development of China’s national economy. As its industrial economic strength grows, China’s high-tech industry is increasingly using cross-border mergers and acquisitions (CBM&A) as an important way to “go out.†To explore the rules governing the process and operation mechanism of reverse knowledge transfer (RKT) through the CBM&A of China’s high-tech industry under government intervention, a tripartite evolutionary game model of the government, the parent company, and the subsidiary as the main subjects is constructed in this paper. The strategies adopted by the three subjects in the RKT game process are analysed, and the factors influencing RKT through CBM&A under government intervention are simulated and analysed using Python 3.7 software. The results show that, under government intervention, the parent company and subsidiary have different degrees of influence on each other. Subsidiaries are highly sensitive to the compensation rate of RKT. Positive intervention by the government tends to foster stable cooperation between the parent company and the subsidiary. However, over time, the government gradually relaxes its intervention in the RKT and innovation of multinational companies.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/8881989.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/8881989.xml (application/xml)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8881989
DOI: 10.1155/2021/8881989
Access Statistics for this article
More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().