Artificial Intelligence in Marketing: A Conceptual Framework for Customer Personalization and Engagement

Abstract

This study examines the role of Artificial Intelligence (AI) in enhancing customer engagement through personalization and underlying psychological mechanisms. A conceptual framework is developed linking AI, customer personalization, perceived usefulness, customer trust, and customer engagement, with privacy concerns incorporated as a moderating factor. Using a quantitative research design, data were collected from 320 digital consumers interacting with AI-enabled platforms such as e-commerce, social media, and digital banking. Structural Equation Modeling (SEM) was employed to test the proposed relationships. The results indicate that AI significantly influences personalization (β = 0.65, p < 0.001), perceived usefulness (β = 0.48, p < 0.001), and customer trust (β = 0.45, p < 0.001). Personalization has a strong positive effect on customer engagement (β = 0.53, p < 0.001). Mediation analysis reveals that perceived usefulness (β = 0.22) and customer trust (β = 0.20) partially mediate the personalization–engagement relationship. Furthermore, privacy concerns negatively moderate this relationship (β = −0.19, p < 0.001). The model demonstrates good fit (CFI = 0.96, RMSEA = 0.048), confirming its robustness. The findings highlight that while AI-driven personalization enhances engagement, its effectiveness depends on perceived value, trust, and ethical data practices.