Continuance intention toward AI-driven mHealth apps: an extended expectation confirmation model based on a cross-sectional study of young Chinese adults
- 1School of Journalism and Communication, Xiamen University
- 2School of Journalism and Communication, Central China Normal University
Abstract
Background: Cutting-edge artificial intelligence (AI) technologies have accelerated the global development of AI-driven mHealth apps, offering diverse functionalities to support personal health management. While China’s AI health industry has expanded rapidly within a collectivist cultural context, limited empirical attention has been given to how specific AI features are associated with users’ continued use of these apps, particularly among young adults.
Objective: Guided by the Expectation Confirmation Model and extended AI-related features, this study examined the factors associated with continuance intention toward AI-driven mHealth apps among young Chinese adults aged 18–34.
Methods: A cross-sectional online survey was conducted, collecting data from 319 valid respondents. Data were analyzed using SPSS 29.0 and Jamovi 2.6.24 to evaluate direct, mediation, and serial mediation associations among the constructs.
Results: The findings demonstrate that satisfaction statistically bridged the indirect associations between core ECM constructs (expectation confirmation and perceived performance) and continuance intention, as well as between AI quality and continuance intention. Furthermore, bootstrapping analysis supported a sequential mediation pathway, showing that expectation confirmation was indirectly associated with continuance intention through the proposed sequential pathway of perceived performance and satisfaction.
Conclusion: The findings suggest that developers should consider the technical reliability and performance of AI-driven health management and monitoring functions to better align with user needs. Moreover, collaboration between health experts and industry practitioners may support the development of content for AI-driven mHealth adoption.
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BibTeX
@article{wang2026continuance,
title = {{Continuance intention toward AI-driven mHealth apps: an extended expectation confirmation model based on a cross-sectional study of young Chinese adults}},
author = {Jiaqi Wang and Donghwa Chung},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1946882},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1946882/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Continuance intention toward AI-driven mHealth apps: an extended expectation confirmation model based on a cross-sectional study of young Chinese adults
AU - Jiaqi Wang
AU - Donghwa Chung
PY - 2026
DA - 2026/10/08
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1946882
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1946882/full
AB - Background: Cutting-edge artificial intelligence (AI) technologies have accelerated the global development of AI-driven mHealth apps, offering diverse functionalities to support personal health management. While China’s AI health industry has expanded rapidly within a collectivist cultural context, limited empirical attention has been given to how specific AI features are associated with users’ continued use of these apps, particularly among young adults. Objective: Guided by the Expectation Confirmation Model and extended AI-related features, this study examined the factors associated with continuance intention toward AI-driven mHealth apps among young Chinese adults aged 18–34. Methods: A cross-sectional online survey was conducted, collecting data from 319 valid respondents. Data were analyzed using SPSS 29.0 and Jamovi 2.6.24 to evaluate direct, mediation, and serial mediation associations among the constructs. Results: The findings demonstrate that satisfaction statistically bridged the indirect associations between core ECM constructs (expectation confirmation and perceived performance) and continuance intention, as well as between AI quality and continuance intention. Furthermore, bootstrapping analysis supported a sequential mediation pathway, showing that expectation confirmation was indirectly associated with continuance intention through the proposed sequential pathway of perceived performance and satisfaction. Conclusion: The findings suggest that developers should consider the technical reliability and performance of AI-driven health management and monitoring functions to better align with user needs. Moreover, collaboration between health experts and industry practitioners may support the development of content for AI-driven mHealth adoption.
SN - 2296-2565
ER -