JOURNAL ARTICLE

Dynamic Capabilities Framework: Prospects and Limitations for Advancing International Marketing Knowledge and Practice.

  • Published In: Journal of International Marketing, 2026, v. 34, n. 2. P. 43 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Cavusgil, S. Tamer; Deligonul, Seyda Z. 3 of 3

Abstract

This article focuses on the Dynamic Capabilities Framework (DCF) and its relevance to international marketing, emphasizing how firms sense opportunities, seize strategic initiatives, and transform resources to compete in volatile global markets. It identifies specific conditions where the DCF is particularly applicable—such as foreign market entry, digital transformation, global supply chain management, strategic alliances, crisis response, and sustainability—and contrasts these with contexts where its explanatory power is limited. The article advances five propositions linking the DCF’s core mechanisms to international marketing outcomes like export performance, market entry effectiveness, cross-border innovation, crisis resilience, and early internationalization by born-global firms. It also compares the DCF with six alternative theoretical frameworks, highlighting its strengths and boundary conditions, and illustrates its practical application through the case of Progetto Quid, an Italian social enterprise. The authors conclude that while the DCF offers a valuable lens for understanding dynamic competitive advantage in international marketing, it is most effective when integrated with complementary theories tailored to specific environmental and institutional contexts.

Additional Information

  • Source:Journal of International Marketing. 2026/06, Vol. 34, Issue 2, p43
  • Document Type:Article
  • Subject Area:Marketing
  • Publication Date:2026
  • ISSN:1069-031X
  • DOI:10.1177/1069031X261436803
  • Accession Number:193622534
  • Copyright Statement:Copyright of Journal of International Marketing is the property of American Marketing Association 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.)

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