Reported Trust and General Concern in External Automated-Vehicle Communication

External human–machine interfaces (eHMIs) may shape reported trust, concern, and acceptance of automated vehicles. This exploratory secondary analysis examines whether a three-item concern index adds information beyond perceived risk and trust while accounting for repeated observations. The dataset contains 310 trials from 10 participants. The legacy security-adjacent concern (SAC) label is retained for traceability, but the index is interpreted as general concern rather than a validated security construct. The Gaussian participant-clustered generalized estimating equations use bias-reduced covariance estimation and approximate Student’s t-inference with nine degrees of freedom. SAC was negatively associated with standardized acceptance in Model 1 (B = −0.765; 95% CI: −0.903 to −0.626). After adding perceived risk and trust, the coefficient was −0.175 (95% CI: −0.369 to 0.020; p = 0.073). Its approximately 77% attenuation is a specification-dependent coefficient change, not a measure of shared psychological mechanisms. Pooled and participant-centered item diagnostics did not support separating SAC from risk and trust. Reduced specifications, independence working correlation, and a participant jackknife supported a negative overall concern–acceptance relationship but not a precise independent SAC contribution. Text and non-text eHMIs were each associated with higher acceptance than no eHMI; their direct contrast was imprecise and does not establish equivalence. Original-condition summaries revealed variation masked by broad grouping. The contribution is a reproducible measurement and inference reassessment, with no direct test of trust calibration, communication integrity, or cybersecurity resilience.