Introduction: Due to the rapid development of information and communication technology in the past decades, mobile health had a significant impact on the development of healthcare systems as an innovative medical service model. Despite the popularity of mobile health worldwide, its continuance use has not been very high. Therefore, the factors that influence consumers’ continuance intention of mobile health deserve further study. Methods: From July to August 2022, data were collected via a cross-sectional survey conducted with a self-designed questionnaire. The characteristics that affect the intention to continue utilizing mobile health were studied using the partial least squares approach. Results: Functional value, social value, emotional value, and conditional value had a positive effect on perceived value while epistemic value had no correlation with perceived value. Additionally, satisfaction, perceived value and habit were positively related to continuance intention. Furthermore, the mediating effect of satisfaction was significant between confirmation and continuance intention, and between perceived value and continuance intention. Conclusion: Satisfaction, habit and perceived value have a significant effect on mobile health apps continuance intention. At the same time, emotional value, functional value, conditional value and confirmation have an indirect positive effect on continuance intention. Therefore, we suggest that mobile health product developers should improve the functionality of the application in detail to enhance the user experience, so that the apps can be used continuously, and prevent the potential loss caused by the user uninstalling the application.
Cite this paper
Wang, J. and Cao, Y. (2023). Factors Affecting Users’ Continuance Intention toward Mobile Health: Integration of Theory of Consumption Value and Expectation Confirmation. Open Access Library Journal, 10, e9851. doi: http://dx.doi.org/10.4236/oalib.1109851.
China Internet Network Information Center (2021) The 49th Statistical Report on Internet Development in China.
http://www.cnnic.net.cn/n4/2022/0401/c88-1131.html
Li, Y., Liu, R.J., Wang, J.B. and Zhao, T. (2020) How Does mHealth Service Quality Influences Adoption? Industrial Management & Data Systems, 122, 774-795.
https://doi.org/10.1108/IMDS-12-2020-0758
Kim, K.H., Kim, K.J., Lee, D.H. and Kim, M.G. (2019) Identification of Critical Quality Dimensions for Continuance Intention in mHealth Services: Case Study of One Care Service. International Journal of Information Management, 46, 187-197.
https://doi.org/10.1016/j.ijinfomgt.2018.12.008
Debon, R., Coleone, J.D., Bellei, E.A. and De Marchi, A.C.B. (2019) Mobile Health Applications for Chronic Diseases: A Systematic Review of Features for Lifestyle Improvement. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 13, 2507-2512. https://doi.org/10.1016/j.dsx.2019.07.016
Krebs, P. and Duncan, D.T. (2015) Health App Use Among US Mobile Phone Owners: A National Survey. JMIR mHealth and uHealth, 3, 107-119.
https://doi.org/10.2196/mhealth.4924
Mensah, I.K. (2022) Understanding the Drivers of Ghanaian Citizens’ Adoption Intentions of Mobile Health Services. Frontiers in Public Health, 10, Article 906106.
https://doi.org/10.3389/fpubh.2022.906106
Yan, M., Filieri, R., Raguseo, E. and Gorton, M. (2021) Mobile Apps for Healthy Living: Factors Influencing Continuance Intention for Health Apps. Technological Forecasting and Social Change, 166, Article 120644.
https://doi.org/10.1016/j.techfore.2021.120644
Bhattacherjee, A. (2001) Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25, 351-370.
https://doi.org/10.2307/3250921
Chea, S. and Luo, M.M. (2008) Post-Adoption Behaviors of E-Service Customers: The Interplay of Cognition and Emotion. International Journal of Electronic Commerce, 12, 29-56. https://doi.org/10.2753/JEC1086-4415120303
Xie, C.Y., Jia, S.L. and He, C.Z. (2020) An Empirical Study on the Factors Affecting Elderly Users’ Continuance Intention of Shared Nurses. Risk Management and Healthcare Policy, 13, 1849-1860. https://doi.org/10.2147/RMHP.S261827
Silas, F.V. (2019) Digital Textbooks Are Useful but Not Everyone Wants Them: The Role of Technostress. Computers & Education, 140, Article 103591.
https://doi.org/10.1016/j.compedu.2019.05.017
Xu, Q., Hou, X.R., Xiao, T.C. and Zhao, W.L. (2022) Factors Affecting Medical Students’ Continuance Intention to Use Mobile Health Applications. Journal of Multidisciplinary Healthcare, 15, 471-484. https://doi.org/10.2147/JMDH.S327347
Sheth, J.N., Newman, B.I. and Gross, B.L. (1991) Why We Buy What We Buy: A Theory of Consumption Values. Journal of Business Research, 22, 159-170.
https://doi.org/10.1016/0148-2963(91)90050-8
Ariff, A.M., Mohd, N.A.R., Firdaus, M.H., Che, R.C.M.Z. and Suhaiza, Z. (2019) The Impact of Consumption Value on Consumer Behaviour: A Case Study of Halal-Certified Food Supplies. British Food Journal, 121, 2951-2966.
https://doi.org/10.1108/BFJ-10-2018-0692
Singh, N., Sinha, N. and Liebana-Cabanillas, F.J. (2019) Determining Factors in the Adoption and Recommendation of Mobile Wallet Services in India: Analysis of the Effect of Innovativeness, Stress to Use and Social Influence. International Journal of Information Management, 50, 191-205.
https://doi.org/10.1016/j.ijinfomgt.2019.05.022
Coelho, A., Bairrada, C. and Peres, F. (2019) Brand Communities’ Relational Outcomes, through Brand Love. Journal of Product & Brand Management, 28, 154-165.
https://doi.org/10.1108/JPBM-09-2017-1593
Zeithaml, V.A. (1988) Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing, 52, 2-22.
https://doi.org/10.1177/002224298805200302
Moez, L. and Sabine, G.H. (2003) Force of Habit and Information Systems Usage: Theory and Initial Validation. Journal of the Association for Information Systems, 4, 65-97. https://doi.org/10.17705/1jais.00030
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989) User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Wynne, W.C. (2010) Bootstrap Cross-Validation Indices for PLS Path Model Assessment. Handbook of Partial Least Squares, 4, 83-97.
https://doi.org/10.1007/978-3-540-32827-8_4
Zeller, R.A. (2005) Measurement Error, Issues and Solutions. Encyclopedia of Social Measurement, 2, 665-676. https://doi.org/10.1016/B0-12-369398-5/00109-2
Fornell, C. and Larcker, D.F. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50.
Venkatesh, V., Thong, J.Y. and Xu, X. (2012) Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36, 157-178. https://doi.org/10.2307/41410412
Limayem, M. and Cheung, C.M.K. (2008) Understanding Information Systems Continuance: The Case of Internet-Based Learning Technologies. Information & Management, 45, 227-232. https://doi.org/10.1016/j.im.2008.02.005
Cocosila, M. and Turel, O. (2022) Consumer Risk Perceptions in Mobile Health Services Adoption: Do They Matter? E-Health Telecommunication Systems and Networks, 11, 67-84. https://doi.org/10.4236/etsn.2022.112005
