INTEGRATING TAM AND S–O–R: A STRUCTURAL MODEL OF AI-PERSONALIZED ADVERTISING, CUSTOMER ENGAGEMENT, AND BRAND LOYALTY IN SOCIAL MEDIA

Abstract

This research establishes and empirically verifies a structural model to investigate how AI-personalized advertising in social media affects customer engagement, marketing performance, and brand loyalty. Basing on an integrated theory that blends the Stimulus–Organism–Response (S–O–R) paradigm with the Technology Acceptance Model (TAM). Data were obtained by purposive sampling from 223 active social media users using a self-administered online survey. The model was validated using partial least squares structural equation modeling (PLS-SEM) in SmartPLS. Findings show explanatory power (R² = 0.396), implying that AI-personalized advertising is strongly related to higher levels of perceived marketing effectiveness and customer engagement, which in turn increase brand loyalty. All the postulated relationships held in the sample limitations, and mediation effects indicate indirect paths from AI-personalized ads to brand loyalty via marketing effectiveness and engagement. Theoretically, this research advances the AI marketing literature by blending TAM’s cognitive appraisals into the S–O–R framework, providing a clearer explanation of technology-driven personalization evoking consumer responses. In practice, the results provide evidence-based guidance for marketers about how to utilize AI to craft personalized, interactive, and user-oriented advertisement experiences that enhance engagement and build long-term brand loyalty. Future studies would need to investigate potential boundary conditions, including perceived privacy risk and trust, to identify the personalization–privacy paradox in AI advertising