Institutional Trust and the Domestic AI Advantage: Evidence from DeepSeek and ChatGPT Users in China

2026-05-31

AI Summary

This study investigates how national institutional trust shapes Chinese users’ differential trust in domestic (DeepSeek) and global (ChatGPT) large language models. Drawing on cognitive-affective trust theory, the authors propose an “institutional lens” framework that, for the first time, bridges macro-level institutional trust with micro-level AI trust mechanisms, revealing the structural role of institutional contexts in human-AI trust formation. Through survey data and structural equation modeling, the empirical analysis demonstrates that high institutional trust significantly enhances users’ affective trust in and cognitive evaluations of domestic AI systems, whereas weakened institutional trust diminishes the trust advantage typically afforded to locally developed models.

Abstract

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.

Problem

• institutional trust
• generative artificial intelligence
• trust divergence
• large language models
• human-machine communication

Innovation

• Institutional Trust
• AI Trust
• Institutional Prism
• Cognitive-Affective Trust
• Domestic AI Advantage