Purpose: This study examines associations between perceived intelligence, trust, usefulness, ease of use, social influence, and young consumers' intention to adopt Artificial Intelligence (AI) marketing applications in Medan, Indonesia.
Research methodology: A cross-sectional online survey collected 373 responses from consumers aged 18–35 between January and March 2025. Partial least squares structural equation modeling in SmartPLS 4 used 5,000 bootstrap subsamples to assess direct and indirect relationships.
Results: Perceived intelligence, trust in AI, perceived usefulness, and social influence were positively associated with adoption intention. Algorithmic transparency was positively associated with trust. The reported indirect effect through trust was 0.129. Ease of use was associated with usefulness and indirectly with adoption intention, but its direct path to intention was not significant. The model explained 58.4% of the variance in adoption intention.
Conclusions: The findings support examining functional benefits, trust, and social endorsement together when explaining AI marketing adoption intentions.
Limitations: Cross-sectional self-reports from one city restrict causal inference and generalizability. The reported model does not establish whether transparency's association with intention is fully mediated.
Contribution: The study connects acceptance models with the updated signaling-theory perspective and explainable-AI research, positioning perceived transparency as a potential trust signal and distinguishing it from perceived intelligence and functional usefulness in a young Indonesian consumer sample.