U-test for testing non-linearity.
收藏Figshare2024-11-20 更新2026-04-28 收录
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Research backgroundM&A (Mergers and acquisitions) is a strategic measure for enterprises to expand their scale, enhance their competitiveness and improve productivity in the market competition. As a new factor of production, data is changing the factor input model and value creation path of enterprises.Research objectivesFrom the perspective of serial M&A, this study explores the impact of serial M&A on enterprises’ TFP (total factor productivity) and the mechanism of digital transformation between them.Research methodsTake the serial M&A transactions of China’s A-share listed companies from 2010 to 2019 as samples, using the theory of organizational learning to analyze the relationship among serial M&A, enterprises’ TFP and the degree of digital transformation. Three-step regression is used to construct a model that serial M&A indirectly affects enterprises’ TFP through intermediary variable digital transformation.Research findingThere is a significant inverse U-shaped relationship between serial M&A and enterprises’ TFP, and digital transformation plays a mediating role in this relationship. The impact of serial M&A on enterprises’ TFP shows an upward trend at first and then a downward trend and this relationship is indirectly realized through digital transformation. The results are still valid after considering the change-explained variables, lag test, Sobel-Goodman test, and Bootstrap test. Heterogeneity analysis shows that for enterprises with non-state-owned property rights, smaller enterprise scale, and higher business environment index, serial M&A has a more obvious effect on TFP indirectly through the degree of digital transformation.Research valueIt further enriches the existing literature on the decision-making of M&A from the perspective of serial M&A and profoundly reveals the mechanism of the degree of digital transformation in the relationship between serial M&A and enterprises’ TFP. The research provides theoretical support and empirical evidence for enterprises to achieve high-quality development.
创建时间:
2024-11-20



