JOURNAL ARTICLE

Tracking the Impact of Women Entrepreneurs on Innovation: A Review.

  • Published In: Small Enterprises Development, Management & Extension Journal (SEDME), 2026, v. 53, n. 1. P. 59 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Mumtaz, Uzma; Ahmad, Naseeb 3 of 3

Abstract

This study systematically examines the evolution and current state of research on the impact of women entrepreneurs on innovation from 1987 to 2024 through a bibliometric analysis of 300 English-language articles sourced from the Scopus database. It identifies key contributors, influential institutions, leading countries, and thematic clusters—namely resource access, entrepreneurial challenges, gender inequality in innovation, and economic growth—highlighting trends such as increasing publication activity since 2015 and significant international collaboration. Prominent findings emphasize the role of education, social capital, and technology in empowering women entrepreneurs, while also noting persistent barriers related to gender biases and resource limitations. The study underscores research gaps including the need for longitudinal, intersectional, and regionally diverse studies, and recommends future directions focusing on integrative theoretical frameworks, enhanced global partnerships, and targeted policies to foster a more equitable and innovative entrepreneurial ecosystem for women.

Additional Information

  • Source:Small Enterprises Development, Management & Extension Journal (SEDME). 2026/03, Vol. 53, Issue 1, p59
  • Document Type:Article
  • Subject Area:Women's Studies and Feminism
  • Publication Date:2026
  • ISSN:0970-8464
  • DOI:10.1177/09708464261424574
  • Accession Number:192656197
  • Copyright Statement:Copyright of Small Enterprises Development, Management & Extension Journal (SEDME) 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.)

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