AI-driven personalization: Unraveling consumer perceptions in social media engagement

Abstract

This study advances our understanding of the impact of personalized stimuli driven by artificial intelligence on consumer engagement in social media marketing. The research develops and examines an extensive S-O-R model, linking AI stimuli to customer perceptions of trust, privacy concerns, perceived usefulness, and, consequently, consumer engagement. Structural equation modeling was utilized to examine the gathered data and evaluate the hypotheses. The results confirm the hypothesis that AI-enabled personalization positively influences trust, privacy concerns, and perceived usefulness. Trust and perceived usefulness positively impact consumer engagement, while privacy concerns do not. Unexpectedly, AI-enabled personalization doesn't significantly affect customer engagement. By exploring the mediating roles of consumer perceptions, the results emphasize perceived utility and trust as a significant mediating factor, underscoring its crucial contribution to fostering positive interactions between users and technology. The research extends the SOR model in understanding AI's impact on consumer engagement, emphasizing trust and perceived usefulness as crucial mediators. For practical implications, businesses in social media marketing should prioritize trust-building, enhance user experience, address privacy concerns, and adopt a customer-centric approach. These insights provide valuable guidance for navigating AI driven personalization dynamics in social media marketing.