How privacy calculus builds user engagement through trust in AI medical consultation

Yumei Luo (), Suxin Li (), Qiongwei Ye () and Jialiang Zheng ()
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Yumei Luo: Yunnan University
Suxin Li: Yunnan University
Qiongwei Ye: Yunnan University of Finance and Economics
Jialiang Zheng: Yunnan University

Electronic Markets, 2025, vol. 35, issue 1, No 50, 18 pages

Abstract: Abstract AI medical consultation is becoming more common, offering a promising avenue for improving the efficiency and effectiveness of healthcare delivery. However, researches on enhancing user engagement in AI medical consultation remains inadequate. This study aims to enhance user engagement by examining the influence of perceived benefits and risks on two types of trust—function-based trust and institution-based trust—and how these forms of trust contribute to distinct dimensions of user engagement. We tested the research model with 449 AI medical consultation users. The results show that both types of trust significantly predict users’ attitudinal and actionable engagement. However, only institution-based trust is associated with informational engagement. Convenience is critical in the formation of both types of trust; however, privacy invasion is only associated with institution-based trust, misdiagnosis with function-based trust, and personalization with neither type of trust. The findings have significant theoretical and practical implications for the design and implementation of such services.

Keywords: AI medical consultation; Engagement; Trust; Privacy calculus theory (search for similar items in EconPapers)
JEL-codes: I10 O33 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12525-025-00795-7

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