Mapping research pattern and knowledge on female entrepreneurship in Asia: a text mining approaches to uncover networks and emerging themes

Rahisha (), Mohammed Jamshed (), Waseem Khan () and Aruna Dhamija ()
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Rahisha: Jamia Hamdard
Mohammed Jamshed: Jamia Hamdard
Waseem Khan: Dr. Vishwanath Karad MIT World Peace University
Aruna Dhamija: GLA University

Journal of Global Entrepreneurship Research, 2025, vol. 15, issue 1, 1-17

Abstract: Abstract This study aims to explore the networks and emerging topics related to female entrepreneurship in Asia by using advanced text-mining algorithms on comprehensive textual data. A dataset of 193 research publications on female entrepreneurship in Asia was collected from the Web of Science database, covering the period from 1994 to February 2024. Topic modeling technique, namely Latent Dirichlet Allocation (LDA), are employed to identify and examine the main themes in female entrepreneurship and their evolution over time. The main themes that were found in the majority of articles were “economic and cultural aspects of women’s entrepreneurship,” “the impact of the gender gap on entrepreneurs,” and “social capital and women’s entrepreneurship.” The findings of the study have substantial implications for governments, support organizations, and young female entrepreneurs in Asia. The study can provide valuable insights into the underlying networks and topics that can be used to develop targeted efforts, mentorship programs, and resource allocation strategies to support women-led enterprises.

Keywords: Female entrepreneurship; Text-mining; Asia; Natural language processing; Topic model; Web of Science (search for similar items in EconPapers)
JEL-codes: C89 L26 N35 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40497-025-00437-4

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