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Promoting trust and intention to adopt health information generated by ChatGPT among healthcare customers: An
Shuangyan Guo1, Yang Song1, Guanyun Chen1
1School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
As a representative product of generative artificial intelligence (GenAI), ChatGPT demonstrates significant potential to enhance healthcare outcomes and improve the quality of life for healthcare consumers. However, current research has not yet quantitatively analysed trust-related issues from both the healthcare consumer perspective and the uncertainty perspective of human-computer interaction.
Objective:
This study aims to analyse the antecedents of healthcare consumers' trust in ChatGPT and their adoption intentions towards ChatGPT-generated health information from the perspective of uncertainty reduction.
Methods:
An anonymous online survey was conducted with healthcare customers in China between September and October 2024. This survey included questions on critical constructs such as social influence, situational normality, anthropomorphism, autonomy, personalisation, information quality, information disclosure, trust in ChatGPT, and intention to adopt health information. A 7-point Likert scale was used to score each item, ranging from 1 (strongly disagree) to 7 (strongly agree). SmartPLS 4.0 was used to analyse data and test the proposed theoretical model.
Results:
The findings indicated that trust in ChatGPT had a significant relationship with the intention to adopt health information. The primary factors associated with trust in ChatGPT and the intention to adopt health information were social influence, situational normality, autonomy, personalisation, and information quality. The analysis revealed a negative relationship between social influence and trust in ChatGPT. Familiarity with ChatGPT was identified as a significant control variable.
Conclusion:
Trust in ChatGPT is positively related to healthcare consumers' adoption of health information, with information quality as a key predictor. The findings offer empirical support and practical guidance for enhancing trust and encouraging the use of GenAI-generated health information.
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