Results of firm size heterogeneity test.

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.研究背景 并购（Mergers and Acquisitions，简称M&A）是企业在市场竞争中扩大规模、提升竞争力、提高生产效率的战略举措。作为一种新型生产要素，数据正在改变企业的要素投入模式与价值创造路径。 研究目标 本研究从串行并购视角出发，探讨串行并购对企业全要素生产率（Total Factor Productivity，简称TFP）的影响，以及二者之间的数字化转型（digital transformation）传导机制。 研究方法 以2010-2019年中国A股上市公司的串行并购交易为研究样本，运用组织学习理论（organizational learning theory）分析串行并购、企业全要素生产率与数字化转型程度三者间的关联；采用三步回归法（three-step regression）构建模型，探究串行并购通过中介变量数字化转型间接影响企业全要素生产率的路径。 研究发现 串行并购与企业全要素生产率之间存在显著的倒U型关系，且数字化转型在该关系中发挥中介作用。串行并购对企业全要素生产率的影响呈现先升后降的趋势，且这一影响通过数字化转型间接实现。在更换被解释变量、滞后检验、Sobel-Goodman检验与Bootstrap检验后，上述研究结论依然成立。异质性分析结果表明，对于非国有产权、企业规模较小、营商环境指数（business environment index）较高的企业，串行并购通过数字化转型程度对全要素生产率的间接影响更为显著。 研究价值 本研究进一步丰富了现有从串行并购视角展开的并购决策相关研究文献，深刻揭示了数字化转型程度在串行并购与企业全要素生产率关系中的传导机制，可为企业实现高质量发展提供理论支撑与实证依据。