Barriers to Artificial Intelligence and Machine Learning Adoption among Women Entrepreneurs: Evidence from Zimbabwe
This study examines barriers to artificial intelligence (AI) and machine learning (ML) adoption among women entrepreneurs in Zimbabwe. Drawing on an integrated Technology–Organization–Environment (TOE) and Diffusion of Innovation (DOI) framework, the research employs a quantitative cross-sectional survey of 384 women entrepreneurs across urban and rural locations and multiple business sectors. Questionnaire data were analysed using descriptive statistics and multiple regression. The findings reveal significant negative associations between AI/ML adoption and three barrier domains: technological and digital skills limitations, financial constraints, and socio-cultural and institutional barriers. Financial constraints emerged as the strongest predictor (β = −0.389, p < .001), while the regression model explained 71.8% of the variation in adoption. Advanced applications, particularly predictive analytics and business-intelligence tools, exhibited relatively low adoption levels. The study highlights the interconnected nature of financial, technological and institutional barriers and underscores the importance of affordable financing, targeted AI skills development and supportive digital infrastructure. By integrating TOE and DOI perspectives, it contributes context-specific evidence to understanding AI/ML adoption among women entrepreneurs in resource-constrained economies.
Copyright: © The Author(s) 2026. This article is distributed under the terms and conditions of the license Creative Commons Attribution (CC BY) license.
Article info: Received: 10 July, 2026; Revised: 31 July, 2026; Accepted: 22 August, 2026; Published: 30 August, 2026.
Moyo, T & Zvitambo, K. (2026). Barriers to Artificial Intelligence and Machine Learning Adoption among Women Entrepreneurs: Evidence from Zimbabwe.Journal of Emerging Technologies for Society, Volume I, 1(1), 79-86. https://doi.org/10.57017/jets.v1.iss1.05
Credit Authorship Contribution Statement: Moyo, T.: Conceptualisation, methodology, investigation, data curation, formal analysis and writing—original draft. Zvitambo, K.: Supervision, validation and writing—review and editing.
Acknowledgments: The authors thank the women entrepreneurs who participated in the study and the academic reviewers who provided comments on the manuscript.
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of commercial or financial relationships that could be construed as a potential conflict of interest.
Data Availability Statement: The data supporting the findings are available from the corresponding author upon reasonable request, subject to participant confidentiality and applicable ethical restrictions.
Ethical Approval Statement: The study involved human participants who voluntarily completed a questionnaire. Informed consent was obtained from all participants, and confidentiality and anonymity were maintained throughout the research process. Formal ethics committee approval was not required for this type of non-interventional, questionnaire-based research.
- Badghish, S., & Soomro, Y. A. (2024). Artificial intelligence adoption by SMEs to achieve sustainable business performance: Application of technology–organization–environment framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864
- Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.
- Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approach (6th ed.). SAGE Publications.
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M., Al-Busaidi, K. A., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate Data Analysis (9th ed.). Cengage Learning.
- Hamid, E. S., & Artha, B. (2026). Artificial intelligence adoption, implementation barriers, and business impact in small and medium-sized enterprises: A systematic literature review. Archives of Business Research, 14(7), 86–110. https://doi.org/10.14738/abr.1407.12031
- Landini, C., & Machado, M. C. (2026). A systematic review on organizational adoption and performance constructs related to artificial intelligence in SMEs. Discover Artificial Intelligence, 6, Article 721. https://doi.org/10.1007/s44163-026-01460-4
- Malatji, M. (2026). Bridging the AI divide in sub-Saharan Africa: Challenges and opportunities for inclusivity. arXiv. https://doi.org/10.48550/arXiv.2601.06145
- Mashapure, R., Nyagadza, B., Muzondo, P., Mutanda, B., Mthombeni, A., Tapera, J., & Hamunakwadi, P. (2025). Bridging the digital technology gender gap: Challenges faced by women entrepreneurs in Mashonaland West Zimbabwe. Journal of Asian and African Studies. https://doi.org/10.1177/00219096251369519
- OECD/BCG/INSEAD. (2025). The Adoption of Artificial Intelligence in Firms: New Evidence for Policymaking. OECD Publishing. https://doi.org/10.1787/f9ef33c3-en
- Oldemeyer, L., Jede, A., & Teuteberg, F. (2025). Investigation of artificial intelligence in SMEs: A systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 75, 1185–1227. https://doi.org/10.1007/s11301-024-00405-4
- Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
- Sagala, G. H., & Őri, D. (2024). Toward SMEs digital transformation success: A systematic literature review. Information Systems and e-Business Management, 22, 667–719. https://doi.org/10.1007/s10257-024-00682-2
- Saunders, M., Lewis, P., & Thornhill, A. (2023). Research Methods for Business Students (9th ed.). Pearson.
- Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.