PREDICTORS OF INTENTION TO USE FUNCTIONAL APPLICATIONS FOR MOBILE HEALTH IN THE REPUBLIC OF SERBIA USING THE EXPANDED UTAUT2 METHOD
Abstract
Introduction/Aim: Health mobile applications provide an opportunity for each user to proactively monitor and manage their health. The mHealth mobile application uses the latest technology with the aim of making healthcare more accessible and affordable to more users. The aim of this paper was to identify factors, defined according to the expanded
UTAUT2 (Unified Theory of Acceptance and Use of Technology) model, which have an impact on the intention to use mobile health applications (mHealth) in the Republic of Serbia.
Methods: The cross-sectional study included 64 respondents (opportunity sample), former students, teachers and associates of the Academy of Vocational Studies in Western Serbia, who filled out an online questionnaire in the period May - November 2024. Cronbach's coefficient α, Pearson correlation coefficient and regression analysis were used in the data analysis.
Results: Five of the 7 elements (Performance Expectancy - PE, Effort Expectancy - EE, Social Influence - SI, Price Value - PV and Facilitating Conditions - FC) of the UTAUT2 model are significant predictors of respondents' intention to accept and use mHealth mobile application services. Habit (H) elements and hedonic motivation (HM) have no significant effect on the use of mHealth mobile applications. Correlation analysis indicates that behavioral intention (BI) correlates significantly strongly positively with EE, facilitating conditions - FC and PE, and significantly positively but weakly with SI and PV. No significant correlation was found with HM and H.
Conclusion: Further research in this area is necessary, especially research related to the testing and use of a specific mobile application for mHealth according to the elements of the UTAUT2 model.
References
DBS Interactive (n.d.) In: Six Examples of Great Healthcare Apps. (Accessed September 2024) Available at: https://www.dbswebsite.com/blog/examples-of-great-healthcare-apps/
Mohamed H, Tawfik H, Al-Jumeily O, Dhiya N, Lin U. MoHTAM: a technology acceptance model for mobile health applications. In: Proceedings of the 2011 Developments in E-systems Engineering (DeSE), Dubai, UAE. IEEE; 2011. p. 250–5. doi:10.1109/DeSE.2011.79
Palos-Sanchez P, Saura J, Rios M, Aguayo M. Toward a better understanding of the intention to use mHealth apps: exploratory study. JMIR Mhealth Uhealth. 2021;9(9):e27021. doi:10.2196/27021
Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157–78.
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425–78. doi:10.2307/30036540
Baptista G, Oliveira T. Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Comput Human Behav. 2015; 50:418–30. doi:10.1016/j.chb.2015.04.024
Basri S. Determinants of adoption of mobile banking: evidence from rural Karnataka in India. Int J Trade Glob Mark. 2018;11(1-2):77–86.
Zhu Y, Zhao Z, Guo J, Wang Y, Zhang C, Zheng J, et al. Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China. Int J Environ Res Public Health. 2023;20(4):3139. doi: 10.3390/ijerph20043139.
Marinković V, Kalinić Z. Antecedents of customer satisfaction in mobile commerce: exploring the moderating effect of customization. Online Inf Rev. 2017;41(1):138–54. doi:10.1108/OIR-11-2015-0364.
Yavuz S, Coşkun E. Sınıf öğretmenliği öğrencilerinin eğitimde teknoloji kullanımına ilişkin tutum ve düşünceleri. Hacettepe Univ Egit Fak Derg. 2008; 34:276–86.
Chóliz M, Fernández-Abascal EG, Martínez-Sánchez F. Infant crying: pattern of weeping, recognition of emotion and affective reactions in observers. Span J Psychol. 2012;15(3):978-88. doi: 10.5209/revsjop.2012.v15.n3.39389.
She L, Rasiah R, Weissmann M, Kaur H. Using the theory of planned behaviour to explore predictors of financial behaviour among working adults in Malaysia. FIIB Bus Rev. 2023;13(2):1–12. doi:10.1177/23197145231169336.
Fadzil, Fakhri, A Study on Factors Affecting the Behavioral Intention to Use Mobile Apps in Malaysia (December 19, 2017). Available at SSRN: https://ssrn.com/abstract=3090753 or http://dx.doi.org/10.2139/ssrn.3090753
Chang H, Fu C, Jain H. Modifying UTAUT and innovation diffusion theory to reveal online shopping behavior: Familiarity and perceived risk as mediators. Inf. Dev. 2016; 32:1757–1773. doi: 10.1177/0266666915623317.
Schomakers E, Lidynia C, Vervier L, Calero A, Ziefle M. Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study. JMIR Mhealth Uhealth. 2022;10(1): e27095, doi: 10.2196/27095.
Schretzlmaier P, Hecker A, Ammenwerth E. Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study. JMIR Hum Factors 2022;9(1): e34918 doi: 10.2196/34918
Scheibe M, Reichelt J, Bellmann M, Kirch W. Acceptance factors of mobile apps for diabetes by patients aged 50 or older: a qualitative study. Med 2.0 2015 Mar 02;4(1).
Peng W, Kanthawala S, Yuan S, Hussain S. A qualitative study of user perceptions of mobile health apps. BMC Public Health. 2016;16(1):1158.
Nelson L, Mulvaney S, Johnson K, Osborn C. mHealth intervention elements and user characteristics determine utility: a mixed-methods analysis. Diabetes Technol Ther. 2017;19(1):9-17.
Yuan S, Ma W, Kanthawala S, Peng W. Keep using my health apps: discover users' perception of health and fitness apps with the UTAUT2 model. Telemed J E Health 2015;735-741.
Breil B, Kremer L, Hennemann S, Apolinário J. Acceptance of mHealth apps for self-management among people with hypertension. Stud Health Technol Inform 2019;267:282-288.
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform 2017;101:75-84.
Quaosar G, Hoque M, Bao Y. Investigating factors affecting elderly's intention to use m-health services: an empirical study. Telemed J E Health 2018;24(4):309-14.
Alam M, Alam M, Rahman S, Taghizadeh S. Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: a SEM-ANN approach. J Biomed Inform. 2021;116:103722.
Duarte P, Pinho J. A mixed methods UTAUT2-based approach to assess mobile health adoption. J Busin Res. 2019;102:140-50.
Cimperman M, Makovec Brenčič M, Trkman P. Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model. Int J Med Inform. 2016; 90:22-31. doi: 10.1016/j.ijmedinf.2016.03.002.
Macedo IM. Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior. 2017; https://doi.org/10.1016/j.chb.2017.06.013.
Alam MB, Hu W, Barua Z. Using the UTAUT Model to Determine Factors Affecting Acceptance and Use of Mobile Health (mHealth) Services in Bangladesh, Journal of Studies in Social Sciences 2018; 17(2): 137-72. ISSN 2201-4624
Semiz B, Semiz T. Examining consumer use of mobile health applications by the extended UTAUT model. BMIJ. 2021; 9(1): 267-81.
Vidal-Silva C, Sánchez-Ortizb A, Serrano-Malebránb J, Arriagadab V, Floresb M, Godoyb A, et al. Social influence, performance expectancy, and price value as determinants of telemedicine services acceptance in Chile. Heliyon. 2024;10(5):e27067.
Chang Y, Chao C, Yu C, Lin F. Extending the Utility of UTAUT2 for Hospital Patients’ Adoption of Medical Apps: Moderating Effects of e‐Health Literacy. Mobile Information Systems. 2021(1):8882317.
Haikal F, Tyas NK, Felcia X, Oktavia T, Sundaram D. User acceptance analysis of the e-health information system using utaut2 method. JATIT. 2022; 100(7): 2193-206.
Akdur G, Aydin M, Akdur G. Adoption of Mobile Health Apps in Dietetic Practice: Case Study of Diyetkolik.JMIR Mhealth Uhealth 2020;8(10):e16911. doi: 10.2196/16911.