Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Dec 02, 2024)
Date Submitted: Sep 5, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring perception differences in AI medical chatbot trust: Comparison of Robot perception and human perception by PLS-MGA
ABSTRACT
Background:
AI-driven medical chatbots allow patients to seek consultations without the constraints of time and space. Despite the rapid advancements and, in some cases, superior performance of AI medical chatbots compared to human physicians in specific domains, user hesitation persists. Furthermore, AI medical chatbots are still relatively new to patients in China. Understanding how patients' perceptions (AI versus human physicians) influence the trust-building process is crucial for the broader adoption of this technology.
Objective:
This study aims to explore how different perception (Robot and Human-like) of users build trust in AI medical chatbot. Moreover, this study examines the moderating role of privacy concern on trust in technology and trust in AI.
Methods:
PLS-MGA was adopted with data collected from 1547 participants, both online and offline, to examine the empirical results.
Results:
Perceived ease of use (β = .433, p < .001), privacy concern (β = .079, p = .006), and brand reputation (β = .264, p < .001) were positively associated with trust in technology and trust in brand. Trust in brand (β = .151, p < .001) positively influenced trust in technology, and trust in technology significantly predicted trust in AI across all dimensions: cognition (β = .20, p < .001), information (β = .19, p < .001), and behavior (β = .17, p < .001). Privacy concern moderated the relationship between trust in technology and trust in AI (β = .11, p = .001), information (β = .08, p < .001), and behavior (β = .09, p < .001). No significant differences were found for AI experience, health status, and perceived risk. However, the paths for perceived ease of use, trust in brand, and trust in technology (benevolence) were significantly stronger in the human group.
Conclusions:
This study contributes both theoretically and practically by advancing the current understanding of the trust-building process in AI healthcare. It also examines the moderating effect of privacy concerns in the trust-building process within the AI healthcare context.
Citation
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