JOURNAL ARTICLE
The impact of biculturalism and native strategic leadership on immigrant entrepreneurs' innovation and initial public offering performance.
Published In: International Journal of Entrepreneurship & Innovation, 2026, v. 27, n. 2. P. 203 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Le, Son; Walters, Bruce; Kroll, Mark J.; Hoang, Bao 3 of 3
Abstract
This article examines the influence of bicultural immigrant entrepreneurs (BIEs)—defined as immigrant founders who have deeply integrated both their home and host country cultures—on innovation and performance in young high-tech initial public offering (IPO) firms in the United States. Drawing on cognitive and social embeddedness theories, the study finds that BIEs' biculturalism enhances their cognitive complexity and creativity, leading to greater innovation, which in turn improves IPO performance. However, due to limited social embeddedness in the host country, BIEs benefit significantly from the presence of experienced native top management team (TMT) members and outside directors, whose business experience amplifies the positive effect of innovation on firm performance. The findings are based on a sample of 351 U.S.-based high-tech IPO firms founded by BIEs from 24 countries, with India, Israel, China, and Taiwan most represented, and suggest that biculturalism and native strategic leadership jointly contribute to the success of immigrant-founded ventures in mainstream markets.
Additional Information
- Source:International Journal of Entrepreneurship & Innovation. 2026/05, Vol. 27, Issue 2, p203
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2026
- ISSN:1465-7503
- DOI:10.1177/14657503251396660
- Accession Number:193488541
- Copyright Statement:Copyright of International Journal of Entrepreneurship & Innovation is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.