Academic publishing in Europe and N. America

Archive Publication ethics Submission Payment Contacts
In the original languageTranslation into English

AI-DRIVEN MARKETING MODELS AS A COMPETITIVE ADVANTAGE IN GLOBAL MARKETS

Authors

Kalinina Elena Evgenievna

Rubric:Marketing
86
0
Quote
86
0

Annotation

The proliferation of artificial intelligence technologies has fundamentally transformed marketing practices in global markets, shifting organizational paradigms from intuition-based decision-making toward data-driven strategic frameworks. This study investigates how AI-driven marketing models function as sources of sustained competitive advantage in contemporary global commerce. Employing a systematic analysis of theoretical frameworks, empirical evidence, and organizational implementations, the research examines mechanisms through which machine learning algorithms, predictive analytics, and autonomous optimization systems enhance marketing effectiveness across heterogeneous market contexts. Findings reveal that AI-driven marketing models generate competitive advantages through superior customer targeting precision, real-time resource allocation optimization, enhanced attribution accuracy, and continuous adaptive learning capabilities. However, competitive advantage sustainability depends critically on organizational capabilities spanning data infrastructure maturity, analytical talent acquisition, ethical governance frameworks, and cultural adaptability.

Keywords

artificial intelligence
marketing models
competitive advantage
predictive analytics
global markets.
machine learning

Authors

Kalinina Elena Evgenievna

References:

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.

Chen, Y., Wang, Y., Nevo, S., Benitez, J., & Kou, G. (2021). Improving strategic flexibility with information technologies: Insights for firm performance in an emerging economy. Journal of Information Technology, 36(2), 152-171.

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.

Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1-8.

Herhausen, D., Miocevic, D., Morgan, R. E., & Kleijnen, M. H. (2024). The digital marketing capabilities gap. Industrial Marketing Management, 90, 276-290.

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.

Kannan, P. K. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45.

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131-151.

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49.

Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889-901.

Other articles of the issue

Asma Zribi, Rached Zouhair, Riadh llahy, Hatem Zgallai, Imen Tlilli, Samir Ben Ali Which extension method is adopted to enhance potato productivity in Tunisia?
121 views
cc-license
About us Journals Books
Publication ethics Terms of use of services Privacy policy
Copyright 2013-2025 Premier Publishing s.r.o.
Praha 8 - Karlín, Lyčkovo nám. 508/7, PSČ 18600, Czech Republic pub@ppublishing.org