Artificial Intelligence Adoption, Labour Productivity, and Inclusive Economic Growth: Evidence from Developing Countries
DOI:
https://doi.org/10.63544/jbii.v5i10.261Keywords:
AI Adoption, Labour Productivity, Inclusive Economic Growth, Developing Countries, MediationAbstract
Artificial intelligence (AI) is reshaping economic development, yet evidence remains limited on whether its adoption promotes inclusive growth in developing countries. This study examines the relationships among AI adoption, labour productivity, and inclusive economic growth, with particular attention to the mediating role of labour productivity. Using cross-sectional secondary data from 42 developing countries, the study applies descriptive statistics, Pearson correlations, ordinary least squares regression, and mediation analysis. The results show that AI adoption is positively and significantly associated with labour productivity and inclusive economic growth. Labour productivity is also positively related to inclusive economic growth and partially mediates the association between AI adoption and inclusive growth. These findings indicate that AI can contribute to shared prosperity through productivity improvements while also operating through other channels, such as enhanced public service delivery, improved market information, and expanded digital employment opportunities. However, productivity gains alone do not ensure equitable outcomes, particularly where digital access, institutional capacity, workforce skills, and labour protections remain weak. The study contributes cross-country quantitative evidence to the technology and development literature by integrating AI adoption, labour productivity, and inclusive growth within a unified empirical framework focused on developing economies. It concludes that governments should complement AI investment with digital infrastructure, workforce reskilling, inclusive labour-market regulation, social protection, and redistributive policies so that technological gains are broadly shared. Given the cross-sectional design, the findings should be interpreted as associations rather than definitive causal effects.
ReferencesAcemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7–72. https://doi.org/10.1257/jel.40.1.7
Acemoglu, D., Autor, D., & Hazell, J. (2023). Artificial intelligence, automation, and the future of work. Journal of Economic Surveys, 37(5), 1456–1489. https://doi.org/10.1111/joes.12547
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Ali, H., Abbasi, F., & Khan, M. (2026). Algorithmic control or empowerment? How AI-driven performance management shapes employee proactivity through the lived experience of job autonomy. Malaysian Journal of Human Resources Management, 3(2), 72–83.
Ali, H., Dil, R., Mustafa, A., Nondani, A., Rajar, L., & Baig, A. (2025). Digitalization and economic growth in Pakistan: An empirical analysis of the services sector (2010–2024). Journal of Media Horizons, 6(5), 649–663. https://jmhorizons.com/index.php/journal/article/view/834
Ali, H., & Khan, M. S. R. (2026). The impact of China’s environmental goods trade on global supply chain resilience: Evidence from emerging market and developing economies (2013–2025). Journal of Business Insight and Innovation, 5(5), 369–383. https://doi.org/10.63544/jbii.v5i5.201
Ali, N. M., Ahmed, M. E., Fakhrul Islam, M., Gomes, C. A., & Islam, M. S. (2026). Toward a circular US economy: Green and artificial intelligence innovation, renewable energy, and domestic material consumption. Energy Sources, Part B: Economics, Planning, and Policy, 21(1), Article 2685043.
Al Kium, A., Sarker, S., Shikha, S. A., Kamal, M. A. T., Jabed, M. I. K., Munifa, N. K., et al. (2023). Health equity and digital disparities in cancer screening and cardiovascular care across socioeconomic and ethnic groups: A systematic review. Vascular and Endovascular Review, 6(2), 35–44.
Autor, D. (2022). The labor market impacts of technological change: From unbridled enthusiasm to vast expectations to vast hopes to vast fears. Journal of Economic Perspectives, 36(4), 3–30. https://doi.org/10.1257/jep.36.4.3
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Dil, R., Ali, H., Nondani, A., Palal, I. U., Zohaib, M., & Hanif, M. (2025). The economic impact of solar energy adoption on industrial growth in Pakistan. Journal of Media Horizons, 6(5), 910–920. https://doi.org/10.5281/zenodo.17424149
Gomes, C. A., Jabed, M. I. K., Ahammad, T., Mandal, S., Hassan, S., Sejan, M. K., Ponduru, P. S., Md Masum Bhuiyan, & Md Luman Jamali. (2025). Operational and financial evidence for generative AI in United States healthcare administrative workflows. Frontiers in Computer Science and Artificial Intelligence, 4(4), 138–145.
Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7
Hasan, M. R., Rahman, M. A., Gomes, C. A. H., Nitu, F. N., Gomes, C. A., Islam, M. R., & Shawon, R. E. R. (2025). Building robust AI and machine learning models for supplier risk management: A data-driven strategy for enhancing supply chain resilience in the USA. Advances in Consumer Research, 2(4), 1152–1171.
Imtiaz, U. (2025). Investigating the impact of phishing attacks on organizational cybersecurity posture. Spectrum of Engineering Sciences, 3(7), 1758–1780. https://thesesjournal.com/index.php/1/article/view/2590
Imtiaz, U., & Elbedour, H. (2025). Cybersecurity risk management in the digital era: The strategic value of ethical hacking. Spectrum of Engineering Sciences, 3(4), 1076–1086. https://www.thesesjournal.com/index.php/1/article/view/2604
Jabed, M. I. K. (2024). Stock market price prediction using machine learning techniques. American International Journal of Sciences and Engineering Research, 7(1), 1–6.
Jabed, M. I. K., Imran, M., Gomes, C. A., Ponduru, P. S., Mandal, S., & Hassan, S. (2025). Deep learning and explainable AI framework for predicting lung cancer severity. Journal of Computer Science and Technology Studies, 7(12), 573–598.
Jabed, M. I. K., Sirazy, M. R. M., Mandal, S., Akter, S. A., Hassan, A., & Esa, H. (2026). Developing AI-based financial forecasting and cybersecurity systems for the US digital economy. Frontiers in Computer Science and Artificial Intelligence, 5(5), 30–38.
Khan, S. R., Baig, M. A., Qaiser, A., Abrar, F., & Ali, H. (2025). Humans and machines at work: Redefining strategic human resource management in the era of artificial intelligence and workforce automation. The Critical Review of Social Sciences Studies, 3(2), 1998–2016. https://doi.org/10.59075/6h0sjx74
Ponduru, P. S. (2024). Decision intelligence for AI and emerging technologies: The AEGIS-DM framework for trustworthy, cost-aware, and low-latency decision making.
Ponduru, P. S., Nandanavanam, P. P. V., & Ponduru, S. K. K. (2026). Ecological proportionality in generative AI: The ethics of marginal capability and environmental sufficiency.
Rahman, M. A., Devnath, R. K., Niloy, S. B., Mehedi, C. M., Chowdhury, T. H., & Jabed, M. I. K. (2025, October). A stacking ensemble framework for predicting employee turnover: Explainable AI with SHAP. In 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS) (pp. 1–6). IEEE.
Rauniyar, G., & Kanbur, R. (2010). Inclusive growth and inclusive development: A review and synthesis of Asian Development Bank literature. Journal of the Asia Pacific Economy, 15(4), 455–469. https://doi.org/10.1080/13547860.2010.516173
Rasheed, M. D., Ahmed, R. H., Khan, W., Imran, M., Ahmad, N., Khan, Y., Akram, M., & Sultana, M. (2025). Towards human-centric smart manufacturing: A digital twin enabled affective ergonomic framework for adaptive human robot collaboration. International Journal of Advances in Signal and Image Sciences, 1544–1555.
Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71–S102. https://doi.org/10.1086/261725
Saeed, L., Kumar, N., Zeb, A., Riaz, T., & Ali, H. (2025). The impact of sustainability initiatives on corporate financial performance: A quantitative analysis. The Critical Review of Social Sciences Studies, 3(2), 1983–1997. https://doi.org/10.59075/v4wzdp20
Sen, A. (1999). Development as freedom. Knopf.
Solow, R. M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics, 39(3), 312–320. https://doi.org/10.2307/1926047
World Bank. (2023). World development report 2023: Technology for inclusive growth. World Bank Publications.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Yasran Rasheed, Hina Shafiq, Huda Saleem, Muhammad Usama, Seerat Ali, Mamoona Altaf

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.