An Agent-Based Model for Evaluating Post-acquisition Integration Strategies

被引：0

作者：

Su, Jing ^{[1
]}

Songhori, Mohsen Jafari ^{[1
,2
]}

Kikuchi, Takamasa ^{[1
]}

Toriyama, Masahiro ^{[3
]}

Terano, Takao ^{[1
]}

机构：

[1] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Interdisciplinary Grad Sch Sci & Engn, Tokyo, Japan

[2] JSPS, Tokyo, Japan

[3] Ritsumeikan Univ, Grad Sch Management, Kyoto, Japan

来源：

NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE | 2017年 / 10247卷

关键词：

SEARCH; ACQUISITIONS; PERFORMANCE; SPEED;

D O I：

10.1007/978-3-319-61572-1_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mergers and acquisitions become popular means for the development of modern corporations, allowing companies to obtain quick access to new markets and source to grow. Post-acquisition integration has been recognized to be influential to the success of M&A. In this paper, we develop an agent-based model to study post-acquisition integration strategies for M&A according to the behavioral theory of the firm. Especially, the model conceptualizes firms conducting search over associated NK performance landscapes. Using this model, our simulation experiments indicate that strategies of personnel allocation, high level manager's feedback and the frequency of exchanging information could have impact on company's performance after M&A.

引用

页码：188 / 203

页数：16

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