AIGC Advertising Content and Consumer Responses in Social Media Communication: The Mediating Roles of Perceived Authenticity and Consumer Trust

Abstract

Purpose: This study examines how consumers' evaluations of artificial intelligence-generated content (AIGC) advertising quality in social media are associated with consumer responses through perceived authenticity and consumer trust. The Persuasion Knowledge Model (PKM) explains how consumers interpret AI-related production and disclosure cues, while Source Credibility Theory explains how authenticity judgments are associated with trust.

Method: A cross-sectional survey of 412 active social media consumers was analysed using partial least squares structural equation modelling (PLS-SEM). Eligibility screening required respondents to be at least 18 years old, to have used social media in the previous three months, and to have encountered social media advertising during that period. The assessment covered measurement quality, structural associations, and the serial indirect pathway. Common-method concerns were addressed procedurally and assessed statistically using Harman's single-factor diagnostic and full-collinearity variance inflation factors (VIFs).

Findings: AIGC advertising content quality was positively associated with perceived authenticity and consumer responses. Perceived authenticity was positively associated with consumer trust, which was positively associated with consumer responses. The serial indirect association through perceived authenticity and consumer trust was also significant.

Originality/Implications: The study conceptualises AIGC as both a production technology and a persuasion cue and separates the measured content-quality construct from AI transparency/disclosure as a contextual cue. It shows that favourable content evaluation alone is insufficient: clear disclosure, human oversight, brand-voice consistency, and claim verification remain central to preserving authenticity and trust.