Effects of personalization and source expertise on users’ health beliefs and usage intention toward health chatbots : Evidence from an online experiment

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Number of pages18
Journal / PublicationDigital Health
Online published2 Oct 2022
Publication statusPublished - 2022



Objective: Based on the heuristic–systematic model (HSM) and health belief model (HBM), this study aims to investigate how personalization and source expertise in responses from a health chatbot influence users’ health belief-related factors (i.e. perceived benefits, self-efficacy and privacy concerns) as well as usage intention. 
Methods: A 2 (personalization vs. non-personalization) × 2 (source expertise vs. non-source expertise) online between-subject experiment was designed. Participants were recruited in China between April and May 2021. Data from 260 valid observations were used for the data analysis. 
Results: Source expertise moderated the effects of personalization on health belief factors. Perceived benefits and self-efficacy mediated the relationship between personalization and usage intention when the source expertise cue was presented. However, the privacy concerns were not influenced by personalization and source expertise and did not significantly affect usage intention toward the health chatbot. 
Discussion: This study verified that in the health chatbot context, source expertise as a heuristic cue may be a necessary condition for effects of the systematic cue (i.e. personalization), which supports the HSM's arguments. By introducing the HBM in the chatbot experiment, this study is expected to provide new insights into the acceptance of healthcare AI consulting services.

Research Area(s)

  • health belief model (HBM), Health chatbot, heuristic–systematic model (HSM), personalization, source expertise

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