Artificial Intelligence in Economics, Management, and Marketing: Micro–Macro Foundations and Frontier Applications

Dear Colleagues,

Artificial Intelligence (AI) is becoming increasingly recognized as a general-purpose technology (GPT) capable of transforming economic systems at both microeconomic and macroeconomic levels (Acemoglu, 2024; Varian, 2023). Beyond efficiency and productivity gains, AI is now central to addressing global sustainability challenges, including climate change, resource allocation, and social inequality, while raising important questions regarding responsible innovation and governance (Floridi et al., 2020).

This Special Issue adopts a sustainability-centered perspective, examining how AI reshapes economic systems while contributing to environmental stewardship, social inclusion, and long-term resilience. Sustainability is understood in a broad sense, encompassing environmental, economic, and societal dimensions, consistent with ESG frameworks and responsible AI principles (Floridi et al., 2020).

At the microeconomic level, AI is redefining firm behavior, production processes, and consumer interactions. AI-enabled systems can improve resource efficiency, reduce waste, and optimize energy use, contributing to more sustainable production models (Brynjolfsson et al., 2025; Agrawal et al., 2022). At the same time, these technologies introduce new environmental externalities, including the energy consumption associated with large-scale computing and data infrastructures. These developments challenge traditional assumptions about cost structures, efficiency, and welfare, extending them to include sustainability trade-offs (Varian, 2023).

At the macroeconomic level, AI has the potential to accelerate green growth and enhance policy design through improved forecasting and data-driven decision-making (Filippucci et al., 2025; Okur, 2025). However, its impacts remain uneven, with risks related to labor displacement, inequality, and technological concentration (Acemoglu & Restrepo, 2022; Salari et al., 2025). Sustainable economic transitions depend on aligning AI innovation with institutional frameworks, regulatory policies, and long-term societal objectives (Acemoglu, 2024).

In management and marketing, AI enables sustainable business models, circular economy practices, and responsible consumption patterns. Organizations are increasingly relying on AI for ESG reporting, supply chain optimization, and strategic decision-making, while marketing systems are being reshaped through personalization and predictive analytics (Davenport et al., 2022; Huang & Rust, 2021). At the same time, these transformations raise critical concerns related to ethics, transparency, and trust in data-driven environments (Binns et al., 2017; Floridi et al., 2020).

This Special Issue aims to advance interdisciplinary research at the intersection of AI, economics, and sustainability, integrating micro–macro mechanisms with managerial and societal implications.

Topics of Interest (Citation-Enriched Where Relevant)

(1) AI in Sustainable Microeconomics:

• AI for resource efficiency, energy optimization, and productivity (Brynjolfsson et al., 2025).
• Consumer behavior and sustainable decision-making (Agrawal et al., 2022).
• Environmental externalities of AI systems and digital infrastructures.

(2) AI in Sustainable Macroeconomics:

• AI and green growth, productivity, and innovation (Acemoglu, 2024; Filippucci et al., 2025).
• Inequality, welfare, and sustainability (Salari et al., 2025).
• AI in macroeconomic and environmental policy modeling (Okur, 2025).

(3) AI in Sustainable Management and Organizations:

• AI-driven ESG strategies and governance (Floridi et al., 2020).
• Sustainable supply chains and digital transformation (Davenport et al., 2022).
• Human–AI collaboration and responsible decision-making (Raisch & Krakowski, 2022).

(4) AI in Sustainable Marketing and Consumer Analytics:

• AI and sustainable consumption patterns (Huang & Rust, 2021).
• Trust, transparency, and ethical personalization (Binns et al., 2017).
• Generative AI in responsible branding (Dwivedi et al., 2023).

(5) Cross-Cutting Themes:

• Explainable AI (XAI) and trust (Floridi et al., 2020).
• Ethical and regulatory implications of AI (Floridi et al., 2020).
• AI for sustainability transitions and ESG impact assessment.
• AI in emerging economies and sustainable development.

Submissions should aim to

• Advance AI-driven sustainability frameworks across micro–macro economic systems;
• Provide robust empirical evidence using advanced methodologies (Brynjolfsson et al., 2025);
• Offer policy-relevant insights on growth, inequality, and environmental sustainability (Acemoglu, 2024);
• Explore ethical and governance dimensions of sustainable AI (Floridi et al., 2020).

References

Acemoglu, D. (2024). The simple macroeconomics of artificial intelligence. National Bureau of Economic Research Working Paper Series, No. 32487. https://www.nber.org/system/files/working_papers/w32487/w32487.pdf

Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in U.S. wage inequality. Econometrica, 90(5), 1973–2016. https://doi.org/10.3982/ECTA19815

Agrawal, A., Gans, J., & Goldfarb, A. (2022). Prediction machines, updated: The simple economics of artificial intelligence. Harvard Business Review Press. https://www.amazon.com/Prediction-Machines-Updated-Expanded-Intelligence/dp/1647824672#

Binns, R., Veale, M., Van Kleek, M., Shadbolt, N. (2017). Like Trainer, Like Bot? Inheritance of Bias in Alg orithmic Content Moderation. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_32

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjad019

Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2022). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 50(1), 24–42. https://doi.org/10.1007/s11747-021-00811-0

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. A., 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. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Filippucci, F., Guglielminetti, E., & Rossi, L. (2025). Artificial intelligence and productivity growth: Evidence from advanced economies. OECD Economics Department Working Papers, No. 1805.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Science and engineering ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5

Goldman Sachs. (2023). The potentially large effects of artificial intelligence on economic growth. Global Economics Analyst Report.

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

Okur F, Özdemir E (2025), "Artificial intelligence, automation and employment dynamics: empirical evidence from G7 economies". Journal of Economic Studies, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JES-06-2025-0414  & OECD Publishing.

Raisch, S., & Krakowski, S. (2022). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 47(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Salari, N., Beiromvand, M., Hosseinian-Far, A., Habibi, J., Babajani, F., & Mohammadi, M. (2025). Impacts of generative artificial intelligence on the future of labor market: A systematic review. Computers in Human Behavior Reports, 18, 100652. https://doi.org/10.1016/j.chbr.2025.100652

Varian, H. R. (2023). Artificial intelligence, economics, and industrial organization. Journal of Economic Perspectives, 37(4), 3–30. https://doi.org/10.1257/jep.37.4.3

Prof. Dr. Rocsana Manea Tonis
Prof. Dr. Oliva M. D. Martins
Prof. Dr. Gheorghe Orzan
Guest Editors

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• artificial intelligence
• productivity growth
• economic transformation
• labor markets
• algorithmic decision-making
• digital transformation
• consumer behavior analytics
• AI governance and ethics
• innovation and competitiveness
• sustainable economic development