From Personalization to Permission: Consumer Attitudes Toward AI-Generated Social Media Advertisements and Consumer-Brand Interactions
Abstract
Purpose: Artificial intelligence has moved social media advertising from broad audience segmentation to algorithmic personalization, automated creative generation and real-time campaign optimization. Yet the same practices that make advertising relevant may also create consumer anxiety about surveillance, manipulation and loss of control. This second research paper extends the earlier manuscript on AI-powered advertising effectiveness by focusing specifically on consumer attitudes toward AI-generated advertisements on social media. Design/methodology/approach: The paper develops an integrated conceptual and empirical framework grounded in AI marketing literature, personalization-privacy paradox theory, advertising value theory and trust-based consumer-brand interaction research. It proposes a cross-sectional survey of social media users combined with focus-group validation and structural equation modelling. Findings: The paper argues that perceived personalization and transparency are likely to strengthen advertising relevance and trust, whereas privacy concern is likely to weaken attitude toward AI-generated advertisements. Trust is positioned as a central mediating mechanism between AI-enabled advertising cues and engagement or brand loyalty intention. Originality/value: The study contributes by separating the positive efficiency logic of AI advertising from the consumer-side acceptance logic. It provides a Scopus-style framework for examining when AI-generated social media advertisements become persuasive and when they become intrusive.
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How to Cite
Riddhi Mahesh Kapoor, Monika Chaudhary, "From Personalization to Permission: Consumer Attitudes Toward AI-Generated Social Media Advertisements and Consumer-Brand Interactions", Vol. 3, Issue 3, 26-06-2025, pp. 142-151.
