File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Knowledge complementarity, knowledge absorption effectiveness, and new product performance: The exploration of international joint ventures in China

TitleKnowledge complementarity, knowledge absorption effectiveness, and new product performance: The exploration of international joint ventures in China
Authors
KeywordsOrganizational culture
International joint venture (IJV)
Knowledge complementarity
New product performance
Organizational structure
Issue Date2013
Citation
International Business Review, 2013, v. 22, n. 1, p. 216-227 How to Cite?
AbstractFirms use international joint ventures (IJVs) to access and learn from partners' knowledge and thus enhance their new product performance, especially when the partners have complementary knowledge bases. Most of the existing literature assumes that knowledge complementarity can directly lead to enhanced new product performance, while ignoring the mediating role of knowledge absorption effectiveness and moderating effects of organizational structure and organizational culture to integrate and manage knowledge complementarity. Using dyadic data from 119 IJVs in China, this article suggests that knowledge complementarity influences IJV new product performance through the full mediation of knowledge absorption effectiveness. Also, the results suggest that an IJV's departmentalization of organizational structure significantly hurts the effect of knowledge complementarity on knowledge absorption effectiveness, while a strong learning culture of the IJV can significantly enhance such effects. © 2012 Elsevier Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/230911
ISSN
2015 Impact Factor: 1.669
2015 SCImago Journal Rankings: 1.100

 

DC FieldValueLanguage
dc.contributor.authorYao, Zheng-
dc.contributor.authorYang, Zhi-
dc.contributor.authorFisher, Gregory J.-
dc.contributor.authorMa, Chaoqun-
dc.contributor.author(Er) Fang, Eric-
dc.date.accessioned2016-09-01T06:07:08Z-
dc.date.available2016-09-01T06:07:08Z-
dc.date.issued2013-
dc.identifier.citationInternational Business Review, 2013, v. 22, n. 1, p. 216-227-
dc.identifier.issn0969-5931-
dc.identifier.urihttp://hdl.handle.net/10722/230911-
dc.description.abstractFirms use international joint ventures (IJVs) to access and learn from partners' knowledge and thus enhance their new product performance, especially when the partners have complementary knowledge bases. Most of the existing literature assumes that knowledge complementarity can directly lead to enhanced new product performance, while ignoring the mediating role of knowledge absorption effectiveness and moderating effects of organizational structure and organizational culture to integrate and manage knowledge complementarity. Using dyadic data from 119 IJVs in China, this article suggests that knowledge complementarity influences IJV new product performance through the full mediation of knowledge absorption effectiveness. Also, the results suggest that an IJV's departmentalization of organizational structure significantly hurts the effect of knowledge complementarity on knowledge absorption effectiveness, while a strong learning culture of the IJV can significantly enhance such effects. © 2012 Elsevier Ltd.-
dc.languageeng-
dc.relation.ispartofInternational Business Review-
dc.subjectOrganizational culture-
dc.subjectInternational joint venture (IJV)-
dc.subjectKnowledge complementarity-
dc.subjectNew product performance-
dc.subjectOrganizational structure-
dc.titleKnowledge complementarity, knowledge absorption effectiveness, and new product performance: The exploration of international joint ventures in China-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ibusrev.2012.04.002-
dc.identifier.scopuseid_2-s2.0-84870239661-
dc.identifier.volume22-
dc.identifier.issue1-
dc.identifier.spage216-
dc.identifier.epage227-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats