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Cited by 313

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

How do networks and learning drive M&As? An institutional comparison between China and the United States

Lin, Zhiang (John), Peng, Mike W., Yang, Haibin, Sun, Sunny Li·2009·Strategic Management Journal

Abstract

What drives mergers and acquisitions (M&As) in different institutional environments? This article builds on the resource dependence perspective and argues that networks, learning, and institutions represent three building blocks that can enhance our understanding of the drivers behind M&As. Specifically, we consider firms as learning actors embedded in network relations and influenced by institutional development, and compare and contrast firms' acquisition activities across the United States and China. Our findings show that there are indeed important learning and network factors that lead to M&As. More interestingly, the impact of such learning and network factors varies sharply across countries with different market‐based institutions. Copyright © 2009 John Wiley & Sons, Ltd.

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Metadata

Authors
Lin, Z. (., Peng, M. W., Yang, H., & Sun, S. L.
Venue
Strategic Management Journal
Publisher
Wiley
Published
2009-05-15
Volume
30
Issue
10
Pages
1113-1132
ISSN
  • 0143-2095 (ISSN-L)
  • 1097-0266
OpenAlex
W1973323049
Cited by
313
References
13
FWCI
50.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: How do networks and learning drive M&As? An institutional comparison between China and the United States. https://app.cochat.ai/papers/10.1002/smj.777
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Lin, Z. (., Peng, M. W., Yang, H., & Sun, S. L. (2009). How do networks and learning drive M&As? An institutional comparison between China and the United States. Strategic Management Journal. https://doi.org/10.1002/smj.777

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.