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2022 Jan-Dec;8 20552076221129718.

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

Yu-Li Liu, Wenjia Yan, Bo Hu, Zhuoyang Li, Yik Ling Lai
Author Information
  1. Yu-Li Liu: Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
  2. Wenjia Yan: Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
  3. Bo Hu: Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong. ORCID
  4. Zhuoyang Li: Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
  5. Yik Ling Lai: Department of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong.
PMID: 36211799 DOI: 10.1177/20552076221129718

Abstract

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.

Keywords

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

References

  1. J Med Syst. 2019 Apr 4;43(5):135 [PMID: 30949846]
  2. J Med Internet Res. 2018 Feb 07;20(2):e48 [PMID: 29415873]
  3. Digit Health. 2019 Aug 21;5:2055207619871808 [PMID: 31467682]
  4. Int J Med Inform. 2020 Sep;141:104164 [PMID: 32593847]
  5. Technol Health Care. 2014;22(4):515-29 [PMID: 24763205]
  6. Clin Ther. 2016 Nov;38(11):2407-2415 [PMID: 27751674]
  7. Risk Anal. 1999 Jun;19(3):391-400 [PMID: 10765412]
  8. BMC Med Inform Decis Mak. 2021 Sep 3;21(1):257 [PMID: 34479566]
  9. J Med Internet Res. 2019 Nov 7;21(11):e15360 [PMID: 31697237]
  10. Philos Ethics Humanit Med. 2015 Jun 27;10:10 [PMID: 26122270]
  11. Int J Environ Res Public Health. 2022 Mar 31;19(7): [PMID: 35409862]
  12. J Health Commun. 2013;18(9):1039-69 [PMID: 23750972]
  13. JMIR Public Health Surveill. 2020 Sep 1;6(3):e20572 [PMID: 32755882]
  14. J Health Commun. 2018;23(4):399-411 [PMID: 29601271]
  15. Yearb Med Inform. 2021 Aug;30(1):191-199 [PMID: 34479391]
  16. Health Educ Res. 1996 Mar;11(1):97-105 [PMID: 10160231]
  17. J Med Internet Res. 2017 Jun 19;19(6):e218 [PMID: 28630033]
  18. Health Commun. 2008 Jul;23(4):358-68 [PMID: 18702000]
  19. Med Decis Making. 2004 Nov-Dec;24(6):573-83 [PMID: 15534339]
  20. Health Commun. 2018 Jan;33(1):57-67 [PMID: 27911096]
  21. Soc Sci Med. 2013 Nov;97:41-8 [PMID: 24161087]
  22. Cyberpsychol Behav Soc Netw. 2018 Oct;21(10):625-636 [PMID: 30334655]
  23. Health Educ Q. 1988 Summer;15(2):175-83 [PMID: 3378902]
  24. J Adolesc Health. 2011 May;48(5):514-9 [PMID: 21501812]
  25. JMIR Ment Health. 2017 Jun 06;4(2):e19 [PMID: 28588005]
  26. Digit Health. 2022 Mar 30;8:20552076221090031 [PMID: 35381977]
  27. J Med Internet Res. 2019 Apr 05;21(4):e12887 [PMID: 30950796]
  28. J Health Commun. 2018;23(8):743-750 [PMID: 30280998]
  29. J Med Internet Res. 2015 Feb 19;17(2):e45 [PMID: 25700481]
  30. Behav Res Methods. 2007 May;39(2):175-91 [PMID: 17695343]
  31. J Am Med Inform Assoc. 2018 Sep 1;25(9):1248-1258 [PMID: 30010941]
  32. Comput Human Behav. 2017 Jul;72:422-431 [PMID: 32288176]
  33. Can J Psychiatry. 2019 Jul;64(7):456-464 [PMID: 30897957]
  34. J Pers Soc Psychol. 1994 Mar;66(3):460-73 [PMID: 8169760]
Journal Article

Word Cloud

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