AI-Enabled Marketing Capabilities and Consumer Response in Malaysian E-Commerce
Abstract
Artificial intelligence (AI) has become an essential capability in modern e-commerce marketing, enabling platforms to personalize recommendations, automate customer interactions, and optimize marketing communications. While AI-driven marketing improves shopping efficiency and relevance, it may also raise privacy concerns that influence consumers’ perceptions and behavioural responses. This study proposes a conceptual framework to examine how perceived AI-enabled marketing capabilities influence consumer responses in Malaysian e-commerce platforms. Drawing on the Stimulus–Organism–Response (S-O-R) framework, the model conceptualizes perceived AI marketing capability as a stimulus that shapes internal consumer evaluations, including trust, perceived value, and perceived intrusiveness. These psychological mechanisms represent organism states that influence consumers’ behavioural responses, operationalized through purchase intention toward e-commerce platforms. The proposed framework integrates both positive and negative consumer perceptions associated with AI-driven personalization. Trust and perceived value represent favourable cognitive evaluations that strengthen consumer acceptance of AI marketing technologies, whereas perceived intrusiveness captures potential privacy concerns that may weaken consumer response. This study outlines a measurement instrument and proposes using Structural Equation Modeling (SEM) to empirically test the relationships among the constructs. By synthesizing fragmented findings in AI marketing literature, this research contributes a theoretically grounded framework for understanding consumer responses to AI-enabled marketing in emerging digital markets such as Malaysia.
Full text article
References
Abdullah, N. H., Rahman, M. N. A., & Ismail, R. (2024). Consumer trust and AI adoption in Malaysian e-commerce platforms. Journal of Digital Commerce Research, 12(1), 45–62.
Aguirre, E., Roggeveen, A. L., Grewal, D., & Wetzels, M. (2016). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 92(1), 34–49. https://doi.org/10.1016/j.jretai.2015.09.005
Amitabh Avinash, S., Kumar, R., & Singh, P. (2025). AI-driven personalization and consumer behaviour in e-commerce: An S-O-R perspective. International Journal of Retail & Distribution Management, 53(2), 210–228. [⚠ verify author name order — "Amitabh Avinash, S." looks like a given name may have been placed in the surname position; check the original source]
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
Ghabban, F., Alharbi, S., & Alzahrani, A. (2025). Consumer perceptions of AI marketing: Privacy concerns and adoption barriers. Journal of Consumer Behaviour, 24(2), 145–162.
Gaikwad, S. V., Patil, A. R., & Kulkarni, P. (2024). Artificial intelligence applications in e-commerce: Enhancing personalization and customer engagement. Journal of Electronic Commerce Research, 25(1), 67–83.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Huang, M. H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3–19. https://doi.org/10.1177/1094670520902266
Khuong, M. N., Nguyen, T. H., & Pham, L. T. (2025). The role of perceived value in AI-driven e-commerce adoption. Asia Pacific Journal of Marketing and Logistics, 37(3), 512–530.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Madanchian, M. (2024). The impact of artificial intelligence marketing on e-commerce sales. MDPI (Multidisciplinary Digital Publishing Institute)
Markou, C. (2024). AI-enabled marketing and the personalization–privacy paradox in digital commerce. Journal of Interactive Marketing, 65, 102–118.
Massoudi, A. H., Al-Hakim, L., & Alshurideh, M. (2025). AI marketing capabilities and customer loyalty: The mediating role of perceived value. Journal of Business Research, 172, 113456.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1–20.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
Saba, T., Rehman, A., & Khan, S. (2024). AI-enabled marketing and consumer engagement: The mediating role of hedonic and utilitarian value. Computers in Human Behavior, 145, 107743.
Sipos, M. (2025). The impact of AI-based personalization on trust and purchase intention: A structural equation modeling approach. Electronic Commerce Research, 25(2), 389–407.
Sri, D., Kumar, V., & Lee, J. (2025). AI recommendation systems and consumer purchase intention in e-commerce platforms. Journal of Retailing and Consumer Services, 78, 103456.
Zhao, K., & Teo, P.-C. (2023). The adoption of Stimulus-Organism-Response (SOR) model in the social commerce literature. International Journal of Academic Research in Business and Social Sciences, 13(7)
Authors
Copyright (c) 2026 Alireza Rezaei, Mohamad Nasir Saludin, Muhammad Riyad Ghozali, Dzul Fahmi, Muhammad Omar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Article Details
