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<p>Trust transfer theory has been extensively validated in low-risk contexts such as e-commerce, yet its applicability in high-risk health information scenarios remains underexplored. This study addresses this gap by integrating trust theory and trust transfer theory to examine how users progress from technology trust to information trust and subsequently to adoption behaviour on GAI-based health platforms. Data were collected from 417 valid responses via an online questionnaire distributed through the China Questionnaire Star platform between 8 and 15 May 2025, and partial least squares structural equation modelling was employed for hypothesis testing. The empirical results reveal that trust in GAI technology serves as the key antecedent of health information trust, while threat perception and information confirmation significantly strengthen both users’ trust and adoption intentions. Government regulation, technical quality, and familiarity positively influence technology trust, and credibility and impartiality enhance information trust. The intra-entity transfer path from technology trust to health information trust is verified, providing new evidence for the application of trust transfer theory in medical AI contexts. These findings verify the intra-entity trust transfer path from technology to information in medical AI contexts, providing new evidence for trust transfer theory application in healthcare. The results offer actionable guidance for platform designers to enhance information traceability and credibility labelling, and inform regulatory policies for AI-generated health information standards.</p>

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What determines users’ adoption of a GAI-based health platform: the role of technology trust and trust transfer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月14日

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  • Junren Ming,
  • Chenyu Gao,
  • Jing Li,
  • Xue Yang

摘要

Trust transfer theory has been extensively validated in low-risk contexts such as e-commerce, yet its applicability in high-risk health information scenarios remains underexplored. This study addresses this gap by integrating trust theory and trust transfer theory to examine how users progress from technology trust to information trust and subsequently to adoption behaviour on GAI-based health platforms. Data were collected from 417 valid responses via an online questionnaire distributed through the China Questionnaire Star platform between 8 and 15 May 2025, and partial least squares structural equation modelling was employed for hypothesis testing. The empirical results reveal that trust in GAI technology serves as the key antecedent of health information trust, while threat perception and information confirmation significantly strengthen both users’ trust and adoption intentions. Government regulation, technical quality, and familiarity positively influence technology trust, and credibility and impartiality enhance information trust. The intra-entity transfer path from technology trust to health information trust is verified, providing new evidence for the application of trust transfer theory in medical AI contexts. These findings verify the intra-entity trust transfer path from technology to information in medical AI contexts, providing new evidence for trust transfer theory application in healthcare. The results offer actionable guidance for platform designers to enhance information traceability and credibility labelling, and inform regulatory policies for AI-generated health information standards.