Global Research Trends In Digital Marketing: A Bibliometric Analysis From Scopus (2015–2025)
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The rapid diffusion of artificial intelligence (AI) has reshaped the foundations of digital marketing, transforming how organizations analyze data, predict consumer behavior, and design customer engagement strategies. This study aims to provide a systematic overview of global research on AI-driven digital marketing by applying bibliometric techniques to publications indexed in the Scopus database between 2018 and 2025.
A total of 1,405 documents including 1,307 articles and 98 review papers were retrieved and analyzed directly from the Scopus database to map publication performance, journal productivity, authorship patterns, thematic structures, and geographical distribution. Results indicate a sharp rise in academic attention after 2021, reflecting the growing convergence of artificial intelligence, machine learning, and consumer analytics within marketing research. The United States, India, and China remain the most influential contributors, while emerging economies such as Malaysia and Indonesia have shown increasing scholarly visibility in recent years.
Keyword and thematic analyses highlight four major research streams: (i) AI adoption and marketing automation, (ii) consumer behavior and engagement analytics, (iii) data ethics and transparency, and (iv) sustainable and responsible AI marketing. Overall, the findings demonstrate that the field is transitioning from experimental adoption toward a mature, interdisciplinary, and globally distributed research domain. The study offers valuable insights for scholars and practitioners seeking to understand how intelligent technologies are redefining marketing theory and practice in the digital era.
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1. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
2. Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2023). Artificial intelligence in marketing: A systematic literature review and future research agenda. Journal of Business Research, 160, 113829. https://doi.org/10.1016/j.jbusres.2023.113829
3. Dwivedi, Y. K., Hughes, D. L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., & Buhalis, D. (2021). Metaverse marketing: How the future of consumer interaction will unfold. Psychology & Marketing, 39(8), 1556–1580. https://doi.org/10.1002/mar.21669
4. Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2023). AI-powered marketing: Emerging trends, opportunities, and research directions. Industrial Marketing Management, 110, 132–148. https://doi.org/10.1016/j.indmarman.2023.04.012
5. Saura, J. R. (2021). Using data science in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), 92–102. https://doi.org/10.1016/j.jik.2020.08.001
6. Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2021). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 224, 107568. https://doi.org/10.1016/j.ijpe.2019.107568
7. 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.2019.101994
8. Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2021). Social media marketing and artificial intelligence: Opportunities and challenges. Technological Forecasting & Social Change, 172, 121037. https://doi.org/10.1016/j.techfore.2021.121037
9. Jabeen, F., Al-Kassem, A. H., & Faisal, M. N. (2022). Artificial intelligence and digital marketing: A systematic review and future research agenda. Sustainability, 14(16), 10344. https://doi.org/10.3390/su141610344
10. Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019
11. Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2021). Big data analytics in marketing and supply chain management: Bibliometric and content analyses. Industrial Marketing Management, 101, 1–18. https://doi.org/10.1016/j.indmarman.2021.01.008
12. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
13. Verma, S., Sharma, R., & Deb, M. (2023). Exploring the role of generative AI in digital marketing communication: A research agenda. Technological Forecasting and Social Change, 196, 122943. https://doi.org/10.1016/j.techfore.2023.122943
14. Mariani, M. M., & Borghi, M. (2022). Big data and marketing: A bibliometric and network analysis. Journal of Business Research, 139, 1315–1331. https://doi.org/10.1016/j.jbusres.2021.10.020
15. Taroun, A., & Yang, J. B. (2020). The integration of artificial intelligence in marketing decision-making: A bibliometric review. Computers in Human Behavior Reports, 2, 100028. https://doi.org/10.1016/j.chbr.2020.100028
16. Gursoy, D., Chi, O. H., & Lu, L. (2019). Artificial intelligence applications in service marketing and customer experience management. Journal of Service Management, 31(5), 1053–1079. https://doi.org/10.1108/JOSM-05-2019-0154
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