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Cited by 643

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Institutional Effects on Strategic Alliance Partner Selection in Transition Economies: China vs. Russia

Hitt, Michael A., Ahlstrom, David, Dacin, M. Tina, Levitas, Edward, Svobodina, Lilia·2004·Organization Science

Abstract

China and Russia represent major economies in transition from command economies, yet their paths to the market have differed greatly. Their divergent approaches have helped create distinct institutional environments. This study focuses on a particularly important strategic decision firms face—alliance partner selection. The study's results suggest that China's more stable and supportive institutional environment has helped Chinese firms take a longer-term view of alliance partner selection, focusing more on the potential partner's intangible assets along with technological and managerial capabilities. In contrast, the less stable Russian institutional environment has influenced Russian managers to focus more on the short term, selecting partners that provide access to financial capital and complementary capabilities so as to enhance their firms—ability to weather that nation's turbulent environment. This study contributes to knowledge about the influence of the institutional environment on alliance partner selection decisions for firms domiciled in transition (and emerging) economies.

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Crossref · 10.1287/orsc.1030.0045

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Metadata

Authors
Hitt, M. A., Ahlstrom, D., Dacin, M. T., Levitas, E., & Svobodina, L.
Venue
Organization Science
Published
2004-04
Volume
15
Issue
2
Pages
173-185
ISSN
  • 1047-7039 (ISSN-L)
  • 1526-5455
OpenAlex
W2147281917
Cited by
643
References
4
FWCI
54.95
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.