Synthesizing Desire: An Investigation into Consumer Trust and Purchase Intent Towards AI-Generated Product Imaginary and Ad Copy

Abstract

The rapid integration of artificial intelligence in marketing communication has transformed the creation of product imagery and advertising copy, reshaping how desire, credibility, and persuasion are constructed in digital marketplaces. This study, titled “Synthesizing Desire: An Investigation into Consumer Trust and Purchase Intent towards AI-Generated Product Imagery and Ad Copy,” explores the complex psychological, cognitive, and behavioral dynamics that emerge when consumers encounter machine-generated promotional content. Unlike traditional advertising, AI-generated visuals and narratives blend algorithmic precision with simulated emotional appeal, raising critical questions regarding authenticity, perceived transparency, and ethical persuasion.

The research conceptually examines how consumers assess trustworthiness in AI-generated advertisements by analyzing key mediating dimensions such as perceived realism, emotional resonance, message clarity, personalization, source credibility, and disclosure of AI involvement. It further investigates how these trust perceptions translate into purchase intention through mechanisms of cognitive confidence, affective attachment, and risk perception. The framework integrates theories of consumer psychology, source credibility, signaling theory, and human–computer interaction to explain how algorithmic creativity influences decision-making without direct human agency.

This study also addresses emerging tensions between efficiency-driven automation and human preference for authenticity, emphasizing how demographic, technological literacy, and psychographic variables shape acceptance or resistance to AI-mediated persuasion. By positioning desire as a synthesized construct formed through visual simulation, linguistic persuasion, and predictive personalization, the study highlights both the strategic value and psychological implications of AI in contemporary advertising.

The findings are expected to offer a robust theoretical foundation for ethical AI marketing, guide advertisers in designing trust-sensitive AI content, and inform policymakers on transparency standards, while contributing to the evolving discourse on human–algorithm trust in digital consumption environments.