Marketing adaptability and digital transformation of enterprises from the perspective of dynamic capabilities

Authors

  • Siyuan Wang University of Illinois Urbana-Champaign Gies college of business, Urbana-Champaign, United States

DOI:

https://doi.org/10.54097/91yq4457

Keywords:

Enterprise marketing, digital transformation, adaptability.

Abstract

With the rapid development of digital technology, the marketing environment for enterprises is changing at an ever-increasing speed and unpredictably; therefore, the old model of marketing can no longer meet the demand for real-time response. Based on the theory of dynamic capabilities, this paper will systematically study how enterprises can construct marketing adaptability with the help of digital tools. Research was conducted on the driving factors of big data and AI, platform automation tools, and real-time response systems for marketing adaptability to explore how they are mediated and moderated by the three dimensions of dynamic capabilities: perception, capture, and reconstruction. Netflix, Starbucks Deep Brew and Zara are representative cases used in this paper to illustrate specific ways and effects of real-time insights, intelligent marketing and supply chain collaboration in enhancing marketing adaptability. Based on research, the adaptability of digital marketing also needs continuous development in addition to technological integration; at the same time, small and medium-sized enterprises face problems such as resource scarcity, differences in cross-border application, algorithmic bias and data privacy. Extend the application of dynamic capabilities theory to digital marketing in theory, propose specific strategies for enterprise managers to strengthen the adaptability of marketing in practice, and point out potential risks of digital transformation.

Downloads

Download data is not yet available.

References

[1] Huang, M.-H., & Rust, R. T.. A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 2021, 49, 30–50.

[2] Hajli, N., Tajvidi, M., Gbadamosi, A., & Nadeem, W. Understanding market agility for new product success with big data analytics. Industrial Marketing Management, 2020, 87, 152–164.

[3] Teece, D. J. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 2007, 28(13), 1319–1350.

[4] Ma L, Sun B. Machine Learning and Ai in Marketing: Current Status and Future Directions. Journal Of Marketing Analytics, 2023, 11(2): 81-92.

[5] Harvard Business School. Starbucks Deep Brew: Ai-Powered Customer Experience. Boston: Harvard Business Publishing, 2025.

[6] Ostrom A L, Fotheringham D. Customer Experience in the Age of Ai. Journal Of Service Research, 2023, 26(3): 309-325.

[7] Davenport T H, Ronanki R. Artificial Intelligence for The Real World: The Practical Application of Ai in Business. Mit Sloan Management Review, 2022, 63(1): 1-6.

[8] Ghemawat P, Nueno J L. Zara: Fast Fashion. Boston: Harvard Business School, 2006.

[9] Caro F, Martínez-De-Albéniz V. Fast Fashion: Forecasting and Inventory Management//Agrrawal N, Smith S A. Retail Supply Chain Management: Quantitative Models and Empirical Studies. 2nd Ed. New York: Springer, 2015: 203-222.

[10] Inditex. Annual Report 2024. Arteixo: Inditex Group, 2025.

Downloads

Published

17-07-2026

Issue

Section

Articles