Cited By
View all- Tan LRen CWang CChang PChang Y(2025)A Cross-Country Study of Consumer Behaviour in the Tourism ContextJournal of Global Information Management10.4018/JGIM.36281032:1(1-31)Online publication date: 3-Jan-2025
Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective
Authors: 
Yi Cui, Jingrong Zhu, Meng Gu, Jinlin LiAuthors Info & Claims
Pages 1 - 21
Abstract
Diabetes management applications enable diabetes self-management in a more convenient and cost-effective manner. This study develops a push–pull–mooring model in order to understand patient-switching intentions to the conventional offline and the novel mobile diabetes management. Data collected from 412 adult patients with diabetes in China are analyzed to test the proposed hypotheses. The results show that push effects and pull effects have significantly positive effects on switching intention. Mooring effects negatively affect switching behavior. Meanwhile, the moderating effects of all three mooring factors (switching cost, offline habit, and private risk) on the relationship between both push-switching intentions and pull-switching intentions are also detected. These findings contribute to a deeper understanding of patient switching intentions towards diabetes management applications and, accordingly, can help marketers, healthcare providers, and health policymakers develop and appropriate their future marketing and administrative strategies.
References
[1]
Alarabiat, A., Hujran, O., Soares, D., & Tarhini, A. (2023). Examining students’ continuous use of online learning in the post-COVID-19 era: An application of the process virtualization theory. Information Technology & People, 36(1), 21–47.
[2]
Alnsour, Y., Johnson, M., Albizri, A., & Harfouche, A. H. (2023). Predicting patient length of stay using artificial intelligence to assist healthcare professionals in resource planning and scheduling decisions. Journal of Global Information Management, 31(1), 1–14.
[3]
Bansal, H. S., Taylor, S. F., & St. James, Y. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96–115.
[4]
Bian, M.-M., & Xie, H. (2021). Study on the usage status and the factors influencing the utilisation of mobile health services for diabetes treatment among patients with this disease. Chinese Journal of General Practice, 19(4), 679–683.
[5]
Bluml, B. M., Kolb, L. E., & Lipman, R. (2019). Evaluating the impact of year-long, augmented diabetes self-management support. Population Health Management, 22(6), 522–528. 30668228.
[6]
Breeman, L. D., Keesman, M., Atsma, D. E., Chavannes, N. H., Janssen, V., van Gemert-Pijnen, L., Kemps, H., Kraaij, W., Rauwers, F., Reijnders, T., Scholte op Reimer, W., Wentzel, J., Kraaijenhagen, R. A., & Evers, A. W. M. (2022). A multi-stakeholder approach to eHealth development: Promoting sustained healthy living among cardiovascular patients. International Journal of Medical Informatics, 147, 104364. https://doi.org//j.ijmedinf.2020.10436410.1016
[7]
Call, K. T., McAlpine, D. D., Garcia, C. M., Shippee, N., Beebe, T., Adeniyi, T. C., & Shippee, T. (2014). Barriers to care in an ethnically diverse publicly insured population: Is health care reform enough? Medical Care, 52(8), 720–727. 25023917.
[8]
Chakraborty, D., Mehta, P., Dash, G., Khan, N., Jain, R. K., & Biswas, D. (2023). What drives consumers to adopt mobile payment apps in the post-COVID-19 scenario: The role of openness to change and user involvement. Journal of Global Information Management, 31(1), 1–24.
[9]
Chang, H. H., Wong, K. H., & Li, S. Y. (2017). Applying push–pull–mooring to investigate channel switching behaviors: M-shopping self-efficacy and switching costs as moderators. Electronic Commerce Research and Applications, 24, 50–67.
[10]
Chen, Y.-H., & Keng, C.-J. (2019). Utilizing the push–pull–mooring-habit framework to explore users’ intention to switch from offline to online real-person English learning platform. Internet Research, 29(1), 167–193.
[11]
Chi, M., Wang, J., Luo, X., & Li, H. (2021). Why travelers switch to the sharing accommodation platforms? A push–pull–mooring framework. International Journal of Contemporary Hospitality Management, 33(12), 4286–4310.
[12]
Cristancho, S., Garces, D. M., Peters, K. E., & Mueller, B. C. (2008). Listening to rural Hispanic immigrants in the Midwest: A community-based participatory assessment of major barriers to health care access and use. Qualitative Health Research, 18(5), 633–646. 18420537.
[13]
Díaz de León Castañeda, C., & Martínez Domínguez, M. (2021). Factors related to Internet adoption and its use to seek health information in Mexico. Health Communication, 36(13), 1768–1775. 32700653.
[14]
Dogra, N., Bakshi, S., & Gupta, A. (2023). Exploring the switching intention of patients to e-health consultations platforms: Blending inertia with push–pull–mooring framework. Journal of Asia Business Studies, 17(1), 15–37.
[15]
Dolmark, T., Sohaib, O., Beydoun, G., Wu, K., & Taghikhah, F. (2022). The effect of technology readiness on individual absorptive capacity toward learning behavior in Australian universities. Journal of Global Information Management, 30(1), 1–21.
[16]
Fan, M., Jiang, X., Xu, F., Qian, W., & Zhang, J. (2023). Influencing factors of the willingness to use internet mobile medical services in elderly type 2 diabetes patient. Chinese Journal of Social Medicine, 40(5), 567–571.
[17]
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18(1), 39–50.
[18]
Fu, S., Li, H., & Liu, Y. (2021). Why discontinue Facebook usage? An empirical investigation based on a push–pull–mooring framework. Industrial Management & Data Systems, 121(11), 2318–2337.
[19]
Gao, C., Zhou, L., Liu, Z., Wang, H., & Bowers, B. (2017). Mobile application for diabetes self-management in China: Do they fit for older adults? International Journal of Medical Informatics, 101, 68–74. 28347449.
[20]
Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55–65.
[21]
Hagger, M. S. (2019). Habit and physical activity: Theoretical advances, practical implications, and agenda for future research. Psychology of Sport and Exercise, 42, 118–129.
[22]
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.
[23]
Haridasan, A. C., Fernando, A. G., & Balakrishnan, S. (2021). Investigation of consumers’ cross‐channel switching intentions: A push‐pull‐mooring approach. Journal of Consumer Behaviour, 20(5), 1092–1112.
[24]
Hill-Briggs, F., Adler, N. E., Berkowitz, S. A., Chin, M. H., Gary-Webb, T. L., Navas-Acien, A., Thornton, P. L., & Haire-Joshu, D. (2020). Social determinants of health and diabetes: A scientific review. Diabetes Care, 44(1), 258–279. 33139407.
[25]
Hsieh, J.-K., Hsieh, Y.-C., Chiu, H.-C., & Feng, Y.-C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework. Computers in Human Behavior, 28(5), 1912–1920.
[26]
Hsieh, P.-J. (2015). Healthcare professionals’ use of health clouds: Integrating technology acceptance and status quo bias perspectives. International Journal of Medical Informatics, 84(7), 512–523. 25842155.
[27]
Hsieh, P.-J. (2021). Understanding medical consumers’ intentions to switch from cash payment to medical mobile payment: A perspective of technology migration. Technological Forecasting and Social Change, 173, 121074.
[28]
Jingrong, Z., Jinlin, L., Zengbo, Z., Hao, L., & Lingfei, C. (2019). Exploring determinants of health provider choice and heterogeneity in preference among outpatients in Beijing: A labelled discrete choice experiment. BMJ Open, 9(4), e023363. 30948563.
[29]
Kohlenberger, J., Buber-Ennser, I., Rengs, B., Leitner, S., & Landesmann, M. (2019). Barriers to health care access and service utilization of refugees in Austria: Evidence from a cross-sectional survey. Health Policy (Amsterdam), 123(9), 833–839. 30878171.
[30]
Kullgren, J. T., McLaughlin, C. G., Mitra, N., & Armstrong, K. (2012). Nonfinancial barriers and access to care for US adults. Health Services Research, 47(1, Part 2), 462–485. 22092449.
[31]
Li, C., Chu, J., & Zheng, L. (2022). Better not let me know: Consumer response to reported misuse of personal data in privacy regulation. Journal of Global Information Management, 30(1), 1–22.
[32]
Liu, J., Sun, H. L., & Zheng, J. (2023). Factors affecting users’ intention to use mobile health services of public libraries. Library & Information Science Research, 45(1), 101223.
[33]
Liu, W., Liu, Y., Twum, P., & Li, S. (2016). National equity of health resource allocation in China: Data from 2009 to 2013. International Journal for Equity in Health, 15(1), 1–8. 27093960.
[34]
Liu, X., Wang, X., Li, J., & Chen, M. (2022). The effect of media richness on the stability of physician-patient relationships on e-consultation platforms. Journal of Global Information Management, 30(1), 1–26.
[35]
Meesala, A., & Paul, J. (2018). Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. Journal of Retailing and Consumer Services, 40, 261–269.
[36]
Nel, J., & Boshoff, C. (2019). Online customers’ habit-inertia nexus as a conditional effect of mobile-service experience: A moderated-mediation and moderated serial-mediation investigation of mobile-service use resistance. Journal of Retailing and Consumer Services, 47, 282–292.
[37]
Nguyen, T. H. N., Yeh, Q.-J., & Huang, C.-Y. (2022). Understanding consumer’ switching intention toward traceable agricultural products: Push–pull–mooring perspective. International Journal of Consumer Studies, 46(3), 870–888.
[38]
Nugroho, A., & Wang, W.-T. (2023). Consumer switching behavior to an augmented reality (AR) beauty product application: Push–pull mooring theory framework. Computers in Human Behavior, 142, 107646.
[39]
Perry, R., Gard Read, J., Chandler, C., Kish-Doto, J., & Hoerger, T. (2019). Understanding participants’ perceptions of access to and satisfaction with chronic disease prevention programs. Health Education & Behavior, 46(4), 689–699. 30770033.
[40]
Polites, G. L., & Karahanna, E. J. M. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. Management Information Systems Quarterly, 36(1), 21–42.
[41]
Shaw, R. J., Bonnet, J. P., Modarai, F., George, A., & Shahsahebi, M. (2015). Mobile health technology for personalized primary care medicine. The American Journal of Medicine, 128(6), 555–557. 25613298.
[42]
Shi, L., Lee, D.-C., Haile, G. P., Liang, H., Chung, M., & Sripipatana, A. (2017). Access to care and satisfaction among health center patients with chronic conditions. The Journal of Ambulatory Care Management, 40(1), 69–76. 27902554.
[43]
Stone, J. Y., Mayberry, L. S., Clouse, K., & Mulvaney, S. (2023). The role of habit formation and automaticity in diabetes self-management: Current evidence and future applications. Current Diabetes Reports, 23(4), 43–58. 36749452.
[44]
Sun, H., Saeedi, P., Karuranga, S., Pinkepank, M., Ogurtsova, K., Duncan, B. B., Stein, C., Basit, A., Chan, J. C. N., Mbanya, J. C., Pavkov, M. E., Ramachandaran, A., Wild, S. H., James, S., Herman, W. H., Zhang, P., Bommer, C., Kuo, S., Boyko, E. J., & Magliano, D. J. (2022). IDF diabetes atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Research and Clinical Practice, 183, 109119. https://doi.org/https://doi.org/10.1016/j.diabres.2021.109119. 34879977.
[45]
Sun, Y., Liu, D., Chen, S., Wu, X., Shen, X.-L., & Zhang, X. (2017). Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push–pull–mooring framework. Computers in Human Behavior, 75, 727–738.
[46]
Susanty, A., Handoko, A., & Puspitasari, N. B. (2020). Push–pull–mooring framework for e-commerce adoption in small and medium enterprises. Journal of Enterprise Information Management, 33(2), 381–406.
[47]
Tang, D., Yang, Y., Yan, Y., & Zhou, M. (2016). What determines online consumers to migrate from PCs to mobile devices? An empirical approach on consumers’ internet cross-channel behaviours. International Journal of Services Technology and Management, 22(1–2), 46–62.
[48]
Tang, Z., & Chen, L. (2020). An empirical study of brand microblog users’ unfollowing motivations: The perspective of push–pull–mooring model. International Journal of Information Management, 52, 102066.
[49]
Tomic, D., Shaw, J. E., & Magliano, D. J. (2022). The burden and risks of emerging complications of diabetes mellitus. Nature Reviews. Endocrinology, 18(9), 525–539. 35668219.
[50]
Tseng, H.-T., Ibrahim, F., Hajli, N., Nisar, T. M., & Shabbir, H. (2022). Effect of privacy concerns and engagement on social support behaviour in online health community platforms. Technological Forecasting and Social Change, 178, 121592.
[51]
Wang, L., Luo, X. R., Yang, X., & Qiao, Z. (2019). Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Information & Management, 56(7), 103150.
[52]
Wang, L., Peng, W., Zhao, Z., Zhang, M., Shi, Z., Song, Z., Zhang, X., Li, C., Huang, Z., Sun, X., Wang, L., Zhou, M., Wu, J., & Wang, Y. (2021). Prevalence and Treatment of Diabetes in China, 2013–2018. Journal of the American Medical Association, 326(24), 2498–2506. 34962526.
[53]
Wang, Y. A., Chang, V., Cross, A. R., Xu, Q. A., & Yu, S. (2022). Towards perceived playfulness and adoption of hearables in smart cities of China. Journal of Global Information Management, 30(1), 1–19.
[54]
Wu, X., Guo, X., & Zhang, Z. (2019). The efficacy of mobile phone apps for lifestyle modification in diabetes: Systematic review and meta-analysis. JMIR mHealth and uHealth, 7(1), e12297. 30664494.
[55]
Xie, Y., & Al-Aly, Z. (2022). Risks and burdens of incident diabetes in long COVID: A cohort study. The Lancet Diabetes and Endocrinology, 10(5), 311–321. https://doi.org/ (22)00044-410.1016/S2213-8587
[56]
Xu, X.-Y., Wang, L.-Y., Zhao, K., & Chang, F.-K. (2021). The migration of viewers in gaming streaming: The perspective of a push–pull–mooring model. International Journal of Human-Computer Interaction, 37(14), 1330–1346.
[57]
Yang, A., Liu, C., Talaei-Khoei, A., & Peng, G. (2023). Valuing your patient’s opinion: Online patient reviews and power distance. Journal of Global Information Management, 31(1), 1–24.
[58]
Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2012). Online service switching behavior: The case of blog service providers. Journal of Electronic Commerce Research, 13(3), 184–197.
[59]
Zhang, W., Cheng, B., Zhu, W., Huang, X., & Shen, C. (2020). Effect of telemedicine on quality of care in patients with coexisting hypertension and diabetes: A systematic review and meta-analysis. Telemedicine Journal and e-Health, 27(6), 603–614. 32976084.
[60]
Zhang, X., Guo, X., Lai, K.-H., Yin, C., & Meng, F. (2017). From offline healthcare to online health services: The role of offline healthcare satisfaction and habits. Journal of Electronic Commerce Research, 18(2), 138–154.
[61]
Zhang, X., Liu, S., Chen, X., Wang, L., Gao, B., & Zhu, Q. (2018). Health information privacy concerns, antecedents, and information disclosure intention in online health communities. Information & Management, 55(4), 482–493.
[62]
Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X., & Zhou, Z. (2019). Factors influencing patients’ intentions to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: Web-based survey. Journal of Medical Internet Research, 21(8), e15023. 31411146.
[63]
Zhao, Q., Chen, C.-D., Zhou, Z., & Mao, R. (2023). Factors influencing consumers’ intentions to switch to live commerce from push–pull–mooring perspective. Journal of Global Information Management, 31(1), 1–30.
[64]
Zhao, Y., Segalowitz, N., Voloshyn, A., Chamoux, E., & Ryder, A. G. (2021). Language barriers to healthcare for linguistic minorities: The case of second language-specific health communication anxiety. Health Communication, 36(3), 334–346. 31746231.
[65]
Zhou, M., Zhao, L., Kong, N., Campy, K. S., Qu, S., & Wang, S. (2019). Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model. International Journal of Medical Informatics, 126, 118–127. 31029253.
[66]
Zhou, T. (2022). Understanding online health community users’ information adoption intention: An elaboration likelihood model perspective. Online Information Review, 46(1), 134–146.
Index Terms
- Adoption of Mobile Health Applications for Diabetes Management From a Push–Pull–Mooring Perspective
Index terms have been assigned to the content through auto-classification.
Recommendations
Factors affecting university students switching intention to mobile learning: a push-pull-mooring theory perspective
AbstractAdopting technology by its intended users is one of the most important contributors to that technology’s success. Therefore, the success of mobile learning (ML) depends on the students’ acceptance of the method. Regarding this point, this ...
The Adoption of Mobile Health Management Services: An Empirical Study
As their populations age, many countries are facing the increasing economic pressure of providing healthcare to their people. In Taiwan, this problem is exacerbated by an increasing rate of obesity and obesity-related conditions. Encouraging the ...
Mobile Health Technology Evaluation: Innovativeness and Efficacy vs. Cost Effectiveness
Globally, the advancement of mobile technology and the growing number of mobile phone users has promoted the boom in mobile health services. The influence of mobile technology has, in fact, made healthcare delivery more accessible, affordable and ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2024.
Publisher
IGI Global
United States
Publication History
Published: 17 July 2024
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 23 Feb 2025
Other Metrics
Citations
Cited By
View all- Tan LRen CWang CChang PChang Y(2025)A Cross-Country Study of Consumer Behaviour in the Tourism ContextJournal of Global Information Management10.4018/JGIM.36281032:1(1-31)Online publication date: 3-Jan-2025