The intrinsic mechanism of public technology acceptance, risk perception, and pharmaceutical safety trust in the context of AI-driven pharmaceutical regulation

Artificial intelligence is being deeply embedded throughout the entire chain of pharmaceutical regulation. However, technological efficacy does not automatically translate into public trust. Existing technology acceptance models default to the causal sequence in which risk and trust determine acceptance, yet they overlook the possibility that the public may adjust risk perceptions in ways that are consistent with their prior attitudes toward the technology.

Moreover, the over-aggregation of risk perception obscures the heterogeneous nature of different risk types. Grounded in cognitive consistency theory, this study developed a theoretical model in which technology acceptance is associated with three-dimensional risk perceptions (data privacy risk, algorithmic error risk, accountability risk), which in turn are associated with pharmaceutical safety trust. The aim was to examine whether the data are consistent with the proposed pathway running from acceptance to risk to trust.

A survey was conducted among Chinese adult citizens through an online platform, yielding 412 valid responses (48.1% male, 51.9% female). Structural equation modeling was employed to test the hypothesized relationships. Technology acceptance showed significant negative associations with data privacy risk (β = − 0.39, p