基于 NK 模型的并购目标、分拆与组织关系网络仿真研究

    M&A Targets, Spin-offs and Interfirm Networks — A Simulation Study Based on the NK Model

    • 摘要: 为研究企业在探索式和利用式并购目标下采取不同整合行为的效果,基于网络视角将企业并购整合中的分拆行为划分为“加法”和“减法”分拆,结合复杂适应系统理论构建“并购目标−整合行为−并购结果”分析框架,建立NK仿真模型,对比分析两种目标下采取不同分拆方式的绩效及组织关系网络变化。研究发现,加法分拆提高了中介中心性,降低了约束度、全局聚类系数和网络全局效率,使自我网络更开放,有利于探索式目标的实现;减法分拆提高了约束度和中介中心性,降低全局聚类系数和网络全局效率,使自我网络更封闭,有利于利用式目标的实现。相比不分拆行为,决策复杂度的增加使得分拆行为对并购绩效的正向影响更明显。

       

      Abstract: To examine the impact of different integration behaviors adopted by firms with exploratory and exploitative M&A targets, this study categorizes spin-off behaviors into “additive” and “subtractive” ones from a network perspective. Integrating complex adaptive system theory, we develop an analytical framework that links M&A targets, integration behaviors, and outcomes. Additionally, an NK model is employed to compare the performance and evolution of interfirm networks for different spin-offs with the two types of M&A targets. Results show that: additive spin-offs increase betweenness centrality, reduce the constraint degree, global clustering coefficient, and overall network efficiency, rendering the ego network more open and facilitating the explorative target. Whereas the subtractive spin-offs improve the constraint degree and betweenness centrality while reducing the global clustering coefficient and overall network efficiency, making the ego network more closed, which is conducive to the exploitative target. The higher decision complexity makes a positive effect of spin-offs on M&A performance more pronounced than that of no spin-off behavior.

       

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