AI-Generated Content Exposure and User Trust: A Moderated Mediation Model of Information Overload and Digital Literacy
Abstract
The proliferation of AI-generated content (AIGC) across digital platforms has fundamentally transformed the information ecosystem, raising critical questions about user trust and information processing. This study investigates the psychological mechanisms through which AIGC exposure influences user trust, specifically examining the mediating role of information overload and the moderating role of digital literacy. Drawing upon the heuristic-systematic model and media dependency theory, we propose and test a moderated mediation model using structural equation modeling with data from 312 university students who actively engage with digital media platforms. The findings reveal that AIGC exposure significantly increases perceived information overload (β = 0.624, p < 0.001), which in turn negatively affects user trust (β = -0.482, p < 0.001). Information overload mediates the relationship between AIGC exposure and user trust, with a significant indirect effect (β = -0.301, 95% CI [-0.412, -0.198]). Furthermore, digital literacy moderates the relationship between AIGC exposure and user trust, such that individuals with higher digital literacy demonstrate greater resilience to trust erosion. The moderated mediation analysis confirms that the indirect effect varies across digital literacy levels (β = 0.167, 95% CI [0.083, 0.251]). These findings contribute to the theoretical understanding of cognitive processing in AI-driven communication environments and offer practical implications for digital literacy education and platform governance.
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