Users' Willingness to Adopt Health Information in WeChat Public Platform of China
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Abstract
Background: WeChat public platform has become an important source for the public to obtain health information. We aimed to explore the key factors affecting users’ willingness to adopt health information and their action mechanisms.
Methods: From April 2023 to May 2023, the users of WeChat public platforms were surveyed via online questionnaires, and the factors influencing users’ willingness to adopt health information and their action mechanisms were analyzed using quantitative statistics and structural equation model (SEM).
Results: The influencing factors of users’ willingness to adopt health information in WeChat public platforms could be divided into the following three dimensions: health belief, information, and platform. Perceived benefits, perceived threats, information quality, source credibility, and platform atmosphere all have significantly positive effects on information adoption willingness. Among them, information quality also positively affects users’ perceived benefits; perceived barriers negatively affect the willingness to adopt information.
Conclusion: Through the analysis of the influencing factors of users’ willingness to adopt health information in WeChat public platforms, it could provide reference for enhancing the public health information service capability of WeChat public platforms and elevating the health self-management level of the public.
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28. Ajzen L (2005). Attitudes, Personality, and Be-havior. McGraw-Hill Education, UK, pp.:78-85.
29. Tarkang EE (2015). Application of the health belief model (HBM) in HIV pre-vention: A literature review. Cent Afr J Public Health, 1(1):1-8.
30. Cao D, Yi J, Wang W, et al (2022). Study on influencing factors of health infor-mation adoption behavior of users on WeChat public platform. Sci Inf Res,4(02):74-89.
31. Chen YJ, Pan P (2021). Investigation on fac-tors influencing perception of infor-mation usefulness of health short videos. J Mod Inf, 41(11):43-56.
32. Yang HJ (2017). An empirical research on the influencing factors of users infor-mation adoption among comprehensive Q&A sites—based on informational and normative social influence perspec-tives. J Mod Inf, 37(10):42-8.
2. Tang XL, Zhang B, Zhang Y (2018). Ex-plore users’ information adoption in-tention in online health communities: A health literacy and trust perspective. J Inf Resour Manag, 8(03):102-12.
3. Becker MH, Drachman RH, Kirscht JP (1974). A new approach to explaining sick-role behavior in low-income pop-ulations. Am J Public Health, 64(3): 205–16.
4. Mou J, Shin DH, Cohen JF (2016). Health beliefs and the valence frame-work in health information seeking behaviors. Inform Technol Peopl, 29(4):876-900.
5. Crossler RE, Long JH, Loraas TM, et al (2014). Understanding compliance with bring your own device policies utilizing protection motivation theory: Bridging the intention-behavior gap. J Inf Syst, 28(1): 209-26.
6. Milne S, Sheeran P, Orbell S (2000). Prediction and intervention in health‐related behavior: A meta‐analytic re-view of protection motivation theory. J Appl Soc Psychol, 30(1): 106-43.
7. Yang J, Zhang Y, Lanting CJM (2017). Exploring the impact of QR codes in authentication protection: A study based on PMT and TPB. Wireless Pers Commun, 96(4): 5315-334.
8. Witte, Kim (1992). Putting the fear back into fear appeals: The extended paral-lel process model. Commun Monogr, 61(4):113-32.
9. Huang J (2010). Remote health monitoring adoption model based on artificial neu-ral networks. Expert Syst Appl, 37(1):307-14.
10. Shin SY, Brandon VDH, Beyea D, et al (2017). Investigating moderating roles of goals, reviewer similarity, and self-disclosure on the effect of argument quality of online consumer reviews on attitude formation. Comput Hum Behav, 76(11):218-26.
11. Jin XL, Zhang DY, Feng HH (2018). An empirical study on the effect of health information communication in mobile social media. Inf Sci, 36(09):129-35.
12. Li CY (2015). The effects of source credi-bility and argument quality on employ-ees' responses toward information sys-tem usage. Asia Pac Manag Rev, 20(2): 56-64.
13. Sussman SW, Siegal WS (2003). Informa-tional influence in organizations: An in-tegrated approach to knowledge adop-tion. Inform Syst Res, 14(1):47-65.
14. Jin XL, Yin M, Zhou Z, et al (2021). The differential effects of trusting beliefs on social media users' willingness to adopt and share health knowledge. Inform Pro-cess Manag, 58(1): 1-16.
15. Chung N, Han H, Koo C (2015). Adoption of travel information in user-generated content on social media: The moderat-ing effect of social presence. Behav In-form Technol, 34(9):1-18.
16. Jintao L, Naiding Y, Jinfu Y, et al (2014). The influence paths of emotion on the occupational safety of rescuers involved in environmental emergencies- system-atic review article. Iran J Public Health, 43(11):1478-485.
17. Bock G, Zmud WR, Kim Y, et al (2005). Be-havioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. Mis Quart, 29(1):87-111.
18. Tan CH, Yi Y, Li L (2021). Research on the influential factors of users' continuance intention towards academic WeChat public account. J Mod Inf, 41(01):50-9+77.
19. Yuen KF, Saidi MSB, Bai X, et al (2021). Cruise transport service usage post COVID-19: The health belief model application. Transport Policy (OXF), 111: 185-96.
20. Yoon C, Kim H (2013). Understanding computer security behavioral intention in the workplace: An empirical study of Korean firms. Inf Technol People, 26(4):401-19.
21. Mo PKH, Mak WWS (2009). Help-seeking for mental health problems among Chinese: The application and extension of the theory of planned behavior. Soc Psych Psych Epid, 44(8):675-84.
22. Lee Y (2011). Understanding anti-plagiarism software adoption: An ex-tended protection motivation theory perspective. Decis Support Syst, 50(2):361-69.
23. Guo X, Han X, Zhang X, et al (2015). Inves-tigating m-health acceptance from a protection motivation theory perspec-tive: Gender and age differences. Telemed J E-Health, 21(8):661-69.
24. Wixom BH, Todd PA (2005). A theoretical integration of user satisfaction and technology acceptance. Inform Syst Res,16(1):85-102.
25. Huo C, Zhang M, Ma F (2018). Factors in-fluencing people's health knowledge adoption in social media: The mediat-ing effect of trust and the moderating effect of health threat. Libr Hi Tech, 36(1):129-51.
26. Yang K (2010). Determinants of US con-sumer mobile shopping services adop-tion: implications for designing mobile shopping services. J Consum Mark, 27(3):262-70.
27. Michel W, Cato W, Koen P (2020). Tracing the COVID-19 virus: A health belief model approach to the adoption of a contact tracing App. Jmir Public Hlth Sur, 6(3): e20572.
28. Ajzen L (2005). Attitudes, Personality, and Be-havior. McGraw-Hill Education, UK, pp.:78-85.
29. Tarkang EE (2015). Application of the health belief model (HBM) in HIV pre-vention: A literature review. Cent Afr J Public Health, 1(1):1-8.
30. Cao D, Yi J, Wang W, et al (2022). Study on influencing factors of health infor-mation adoption behavior of users on WeChat public platform. Sci Inf Res,4(02):74-89.
31. Chen YJ, Pan P (2021). Investigation on fac-tors influencing perception of infor-mation usefulness of health short videos. J Mod Inf, 41(11):43-56.
32. Yang HJ (2017). An empirical research on the influencing factors of users infor-mation adoption among comprehensive Q&A sites—based on informational and normative social influence perspec-tives. J Mod Inf, 37(10):42-8.
| Files | ||
| Issue | Vol 53 No 11 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v53i11.16951 | |
| Keywords | ||
| WeChat public platforms Health information Adoption willingness Perceived benefits | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Tang L, Sun H. Users’ Willingness to Adopt Health Information in WeChat Public Platform of China. Iran J Public Health. 2024;53(11):2482-2490.
