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

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New Frontiers in Artificial Intelligence (JSAI-isAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10247))

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Abstract

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.

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Authors and Affiliations

  1. Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan

    Jing Su, Mohsen Jafari Songhori, Takamasa Kikuchi & Takao Terano

  2. JSPS, Tokyo, Japan

    Mohsen Jafari Songhori

  3. Graduate School of Management, Ritsumeikan University, Kyoto, Japan

    Masahiro Toriyama

Authors
  1. Jing Su
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  2. Mohsen Jafari Songhori
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  3. Takamasa Kikuchi
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  4. Masahiro Toriyama
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  5. Takao Terano
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Corresponding author

Correspondence to Jing Su .

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Editors and Affiliations

  1. Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  2. Fujitsu Laboratories Ltd., Kanagawa, Japan

    Yuiko Ohta

  3. Chiba University, Chiba, Japan

    Sachiyo Arai

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

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Su, J., Jafari Songhori, M., Kikuchi, T., Toriyama, M., Terano, T. (2017). An Agent-Based Model for Evaluating Post-acquisition Integration Strategies. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2016. Lecture Notes in Computer Science(), vol 10247. Springer, Cham. https://doi.org/10.1007/978-3-319-61572-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-61572-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61571-4

  • Online ISBN: 978-3-319-61572-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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