Extending the Information Adoption Model to Explain Consumer Adoption of AI-Generated Advertising Through Credibility, Relevance, Transparency, Trust, Risk, Experience, and Usefulness

Prior Information Adoption Model (IAM) research explains adoption of online reviews, e-WOM, and social media information, but offers limited evidence on how consumers adopt advertising information generated or personalized through AI. This study extends IAM by integrating message-level evaluations and AI-process evaluations within a partial-mediation framework. Survey data from 462 digitally active consumers were analyzed using PLS-SEM. The model explained 41.2% of the variance in perceived information usefulness and 44.6% of the variance in Intention to Adopt. Information relevance showed the strongest association with perceived information usefulness (β = 0.276, p < 0.001), while trust in AI-generated advertising showed the strongest direct association with Intention to Adopt (β = 0.217, p < 0.001). Perceived AI risk was negatively associated with both perceived information usefulness (β = -0.153, p < 0.001) and Intention to Adopt (β = -0.136, p < 0.001). Perceived information usefulness was also positively associated with Intention to Adopt (β = 0.304, p < 0.001), and all specific indirect effects were significant. The findings refine IAM by showing that usefulness remains central, while consumer adoption of AI-generated advertising is also associated with credibility, relevance, transparency, trust, risk, and prior AI-mediated experience.