Institutional Trust and the Domestic AI Advantage: Evidence from DeepSeek and ChatGPT Users in China
For researchers in human-machine communication, this work highlights the macro-level institutional factors influencing AI trust, though it is incremental as it applies existing trust theories to a new context.
This study introduces the 'Institutional Prism' framework to show that institutional trust shapes user trust in AI models, finding that higher institutional trust in China is associated with stronger affective trust in domestic DeepSeek over global ChatGPT, based on survey data from 405 users.
Public trust in generative artificial intelligence exhibits increasingly divergent patterns across national contexts, yet prevailing research largely overlooks the macro-structural forces underlying this divergence. This study argues that trust in AI is not merely a technical response to performance but a product of institutional refraction. We propose an ``Institutional Prism'' framework to demonstrate how institutional trust shapes user trust in domestic (DeepSeek) and global (ChatGPT) large language models. Drawing on Cognitive-Affective Trust Theory, we distinguish between cognitive and affective dimensions of trust and analyze survey data from 405 Chinese users. The findings show that higher institutional trust is positively associated with stronger affective trust in domestic AI models and shifts cognitive evaluations in a more favorable direction. While under lower institutional trust, this domestic advantage weakens. These findings reveal that institutional trust has emerged as a core dimension of AI trust formation. By linking micro-level psychological judgments with macro-level governance, this research contributes a new perspective to human-machine communication.