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Women entrepreneurship in the age of AI and ESG: Micro- and macro-level drivers of adoption

Razieh Sadraei & Francesca Dal Mas

International Journal of Entrepreneurship and Innovation2026https://doi.org/10.1177/14657503251408253article
AJG 2ABDC C
Weight
0.50

What the paper says

Artificial intelligence (AI) holds significant potential to advance women entrepreneurship and environmental, social, and governance (ESG) goals, yet adoption in emerging markets is constrained by both individual and institutional barriers. This study applies a two-panel Delphi method with 11 women entrepreneurs (Study 1) and 18 institutional experts (Study 2) to explore micro- and macro-level drivers of AI adoption. Across three rounds, participants rated competences, motivations, risks and institutional factors, with consensus assessed through Kendall's. Results show that women entrepreneurs view AI as a values-driven tool for sustainability but face challenges in skills, trust and resources, while institutional experts highlight regulatory frameworks, policy support and cultural norms as decisive enablers or barriers. The study contributes to Institutional Theory by integrating micro- and macro-level perspectives and offers practical insights for designing gender-sensitive policies and support mechanisms. Future research should extend these findings through longitudinal and cross-country analyses of women-led ESG ventures.

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Cite this paper

https://doi.org/https://doi.org/10.1177/14657503251408253

Or copy a formatted citation

@article{razieh2026,
  title        = {{Women entrepreneurship in the age of AI and ESG: Micro- and macro-level drivers of adoption}},
  author       = {Razieh Sadraei & Francesca Dal Mas},
  journal      = {International Journal of Entrepreneurship and Innovation},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14657503251408253},
}

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Women entrepreneurship in the age of AI and ESG: Micro- and macro-level drivers of adoption

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.