AI AND MARKETING ANALYTICS ADOPTION IN DEVELOPING COUNTRIES
Abstract
The rapid advancement of artificial intelligence (AI) and marketing analytics has fundamentally transformed decision-making processes in contemporary marketing practice. While developed economies have experienced accelerated adoption of AI-driven tools, developing countries continue to exhibit uneven and fragmented implementation patterns. This disparity raises critical questions regarding the structural, organizational, and institutional factors shaping AI and analytics adoption in marketing functions within developing economies. This study examines the drivers, barriers, and strategic outcomes of AI-enabled marketing analytics adoption in developing countries. Drawing upon innovation diffusion theory, resource-based view, and institutional theory, the article proposes an integrative conceptual framework explaining adoption dynamics under conditions of resource constraints, data limitations, and institutional volatility. The study further outlines managerial implications and policy considerations aimed at facilitating responsible and effective AI integration in marketing practices across developing markets.
References
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694.
https://doi.org/10.2307/3250951
Bresciani, S., Ferraris, A., & Del Giudice, M. (2018). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 136, 331–338.
https://doi.org/10.1016/j.techfore.2017.03.002
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Institute Report.
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.
https://doi.org/10.1007/s11747-019-00696-0
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
https://store.hbr.org/product/competing-on-analytics-the-new-science-of-winning/8248
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Hair, J. F., Harrison, D. E., & Risher, J. J. (2018). Marketing research in the 21st century: Opportunities and challenges. Journal of Marketing Theory and Practice, 26(1–2), 1–15.
https://doi.org/10.1080/10696679.2018.1392865
Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2020). Digital transformation of emerging markets: The role of marketing capabilities. International Journal of Research in Marketing, 37(3), 453–471.
https://doi.org/10.1016/j.ijresmar.2020.01.004
Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2019). Artificial intelligence in the organization: From big data to business value. Information Systems Frontiers, 21(3), 543–558.
https://doi.org/10.1007/s10796-019-09937-2
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2020). Exploring the relationship between big data analytics capability and competitive performance. MIS Quarterly, 44(1), 103–130.
https://doi.org/10.25300/MISQ/2020/14521
Rai, A., Constantinides, P., & Sarker, S. (2019). Editor’s comments: Next-generation digital platforms: Toward human–AI hybrids. MIS Quarterly, 43(1), iii–ix.
https://doi.org/10.25300/MISQ/2019/14196
Sheth, J. N. (2011). Impact of emerging markets on marketing: Rethinking existing perspectives and practices. Journal of Marketing, 75(4), 166–182.
https://doi.org/10.1509/jmkg.75.4.166
Steenkamp, J.-B. E. M. (2019). Global brand strategy: World-wise marketing in the age of branding. Palgrave Macmillan.
https://link.springer.com/book/10.1057/978-1-137-56973-0
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350.
https://doi.org/10.1002/smj.640
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
https://doi.org/10.2307/30036540
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.
https://doi.org/10.1509/jm.15.0413
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931.
https://doi.org/10.1108/JOSM-04-2018-0119
