Vol. 9 No. 1 (2021): Business & Management Studies: An International Journal

Examining consumer use of mobile health applications by the extended UTAUT model

Buket Bora Semiz
Asst. Prof., Bilecik Şeyh Edebali University
Tarık Semiz
Asst. Prof., Bilecik Seyh Edebali University

Published 2021-03-25


  • Extended UTAUT, Mobile Health, Behavioral Intention
  • Genişletilmiş UTAUT, Mobil Sağlık, Davranışsal Niyet

How to Cite

Semiz, B. B., & Semiz, T. (2021). Examining consumer use of mobile health applications by the extended UTAUT model. Business & Management Studies: An International Journal, 9(1), 267-281. https://doi.org/10.15295/bmij.v9i1.1773


Today, rapid changes and innovations in technology cause changes in the health sector as in many areas. Especially mobile technologies and applications are increasing their usage areas in the health sector day by day. Thanks to these mobile health applications, consumers provide a lot of convenience and advantages in healthy eating, reproductive health, disease monitoring, access to health records, etc.
The study aims to investigate consumers’ usage of mobile health (mHealth) applications with the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. It is possible to say that it is an empirical study since the data were collected with the questionnaire method. Because this is research based on a cause-and-result relationship, the relationships were revealed with Structural Equation Modelling (SEM). The data were collected between November 2020 and January 2021 via the Google Forms platform from 354 individuals using convenience sampling through social media channels. The SPSS and SmartPLS programs were used for the analyses. First of all, it was determined that the scales' validity and reliability were ensured by performing validity and reliability analysis of the research model. According to the findings, it was revealed that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, hedonic motivation, and perceived trust have a significant effect on the intention to use mHealth applications and, the intention to use mHealth applications has a significant effect on the behaviour of use mHealth applications.


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  1. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Eaglewood-Cliffs, NJ: Prentice-Hall.
  2. Alalwan, A. A., Dwivedi, Y. K., and Rana, N. P. (2017). Factors Influencing Adoption Of Mobile Banking By Jordanian Bank Customers: Extending UTAUT2 With Trust. International Journal Of Information Management, 37(3), 99–110.
  3. Alam, M. Z., Hu, W. and Barua, Z. (2018). Using The UTAUT Model To Determine Factors Affecting Acceptance And Use Of Mobile Health (mHealth) Services In Bangladesh. Journal Of Studies In Social Sciences, 17(2), 137-172.
  4. Alam, M. Z., Hoque, M. R., Hu, W. and Barua, Z. (2020). Factors Influencing The Adoption Of mHealth Services In A Developing Country: A Patient-Centric Study. International Journal Of Information Management, 50, 128-143.
  5. Arslan, E. T. and Demir, H. (2017). Üniversite Öğrencilerinin Mobil Sağlık Ve Kişisel Sağlık Kaydı Yönetimine İlişkin Görüşleri. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(2), 17-36.
  6. Bawack, R. E., and Kamdjoug, J. R. K. (2018). Adequacy of UTAUT In Clinician Adoption Of Health İnformation Systems In Developing Countries: The Case Of Cameroon. International Journal Of Medical Informatics, 109, 15-22.
  7. Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V. and Papasratorn, B. (2012). Factors Influencing The Thai Elderly Intention To Use Smartphone For E-Health Services. 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483.
  8. Brown, S. A. and Venkatesh, V. (2005). Model Of Adoption Of Technology In Households: A Baseline Model Test And Extension Incorporating Household Life Cycle. MIS Quarterly, 399-426.
  9. Chandra, S., Srivastava, S. and Theng, Y.L. (2010). Evaluating The Role Of Trust In Consumer Adoption Of Mobile Payment Systems: An Empirical Analysis. Communications Of The Association For Information Systems, Vol. 27, 561–588.
  10. Childers, T. L., Carr, C. L., Peck, J. and Carson, S. (2001). Hedonic And Utilitarian Motivations For Online Retail Shopping Behavior. Journal Of Retailing, 77 (4), 511-535.
  11. Compeau, D. R. ve Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure Initial Test. MIS Quarterly, 19(2), 189-211.
  12. Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10-114.
  13. Chen, C. C., Wu , J. and Crandall, R. E. (2007). Obstacles To The Adoption Of Radio Frequency Identification Technology In The Emergency Rooms Of Hospitals. International Journal of Electron Healthcare, 3,193-207.
  14. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
  15. DeVeer, A. J., Peeters, J. M., Brabers, A. E., Schellevis, F. G., Rademakers, J. J. J. and Francke, A. L. (2015). Determinants Of The Intention To Use E-Health By Community Dwelling Older People. BMC Health Services Research, 15(1), 103.
  16. Der Van, H. H. (2004). User Acceptance Of Hedonic Information System. MIS Quarterly, 28(4), 695-704.
  17. Dünnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J. M. and Krcmar, H. (2012). Determinants Of Physicians’ Technology Acceptance For E-Health In Ambulatory Care. International Journal Of Medical Informatics, 81(11), 746-760.
  18. Fan, W., Liu, J., Zhu, S. and Pardalos, P. M. (2018). Investigating The Impacting Factors For The Healthcare Professionals To Adopt Artificial Intelligence-Based Medical Diagnosis Support System (Aimdss). Annals Of Operations Research, 1-26.
  19. Farooq, M.S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Markovic, M.R. and Sajid, A. (2017). Acceptance And Use Of Lecture Capture System (LCS) In Executive Business Studies Extending UTAUT2. Interactive Technology And Smart Education, 14 (4), 329-348.
  20. Field, A. (2013). Discovering statistics using IBM SPSS (4th ed.). London: Sage Publications.
  21. Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P. and Haines, A. (2013). The effectiveness Of Mobile-Health Technologies To Improve Health Care Service Delivery Processes: A Systematic Review And Meta-Analysis. PLoS Med, 10(1), e1001363.
  22. Fornell C. and Larcker, D.F. (1981). Evaluating Structural Equation Models With Unobservable Variables And Measurement Error. Journal of Marketing Research, 18(1), 39–50.
  23. Garavand, A., Samadbeik, M., Nadri, H., Rahimi, B. and Asadi, H. (2019). Effective Factors In Adoption Of Mobile Health Applications Between Medical Sciences Students Using The UTAUT Model. Methods of Information In Medicine, 58(04/05), 131-139.
  24. Goulão, A. P. B. A. (2014). E-Health Individual Adoption-Empirical Model Based On UTAUT 2. Doctoral Dissertation, Lisboa: Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa.
  25. Gunasinghe, A., Abd Hamid, J., Khatibi, A., & Azam, S. F. (2019). The adequacy of UTAUT-3 in interpreting academician’s adoption to e-Learning in higher education environments. Interactive Technology and Smart Education, 17 (1), 86-106.
  26. Greenspun, H. and Coughlin, S. (2012). mHealth In An mWorld: How Mobile Technology Is Transforming Healthcare. Deloitte Center For Health Solutions.
  27. Güler, E. (2015). Mobil Sağlık Hizmetlerinde Oyunlaştırma. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 1(2), 82-101.
  28. Güler, E. and Eby, G. (2015). Akıllı Ekranlarda Mobil Sağlık Uygulamaları. Eğitim ve Öğretim Araştırmaları Dergisi, 4, 45-51.
  29. Hair, J. F., Ringle, C. M. and Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
  30. Hair, J. F., Hult, G. T. M., Ringle, C. and Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  31. Hair, J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2017). A Primer On Partial Least Squares Structural Equation Modeling (PLS-SEM). (2nd ed.), Thousand Oaks, CA: Sage.
  32. Henseler, Jörg, Christian M. Ringle and Rudolf R. Sinkovics (2009). The Use of Partial Least Square Path Modelling in International Marketing. in R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in International Marketing, Emerald, Bingley, 277-320.
  33. Hernandez, A. I., Mora, F., Villegas, M., Passariello, G. and Carrault, G. (2001). Real-Time ECG Transmission Via Internet For Nonclinical Applications. IEEE Transactions On Information Technology In Biomedicine, 5(3), 253-257.
  34. Hoque R. and Sorwar, G. (2017). Understanding Factors Influecing The Adoption of mHealth By The Elderly: An Extension of The UTAUT Model. International Journal of Medicine Information, 101, 75-84.
  35. https://digitalage.com.tr/2020de-mobil-saglik-uygulamalari-kullanimi-dunya-genelinde-yukseldi arastirma/(Accessed Date: 04.10.2020).
  36. https://abainnolab.com/teknoloji-ve-saglik-sektorunun-birlesiminde-mobil-saglik-uygulamalari/ (Accessed Date: 04.12.2020).
  37. https://sensortower.com/blog/top-countries-worldwide-q1-2019-downloads, (Accessed Date: 01.01.2021).
  38. https://tusiad.org/tr/tum/item/8677, (Accessed Date: 25.12.2020).
  39. Kim, D. J., Ferrin, D. L. and Rao, H. R. (2008). A Trust-Based Consumer Decision-Making Model In Electronic Commerce: The Role Of Trust, Perceived Risk, And Their Antecedents. Decision Support Systems, 44(2), 544-564.
  40. Kumar, S., Nilsen, W. J. and Swendeman, D. (2013). Mobile Health Technology Evaluation. American Journal of Preventive Medicine, 45 (2), 228-236.
  41. Lee, T. (2005). The Impact of Perceptions of Interactivity on Customer Trust And Transaction Intentions In Mobile Commerce. Journal of Electronic Commerce Research, 6, 165-180.
  42. Lee, E. and Han, S. (2015). Determinants of Adoption of Mobile Health Services. Online Information Review, 39 (4), 556 – 573.
  43. Lestari, T. and Rofianto, W. (2020). Multi-Dimensional Consumer Value and Adoption of Mobile Health Service: A Study During COVID-19 Outbreak in Indonesia. Business Innovation & Engineering Conference, Indonesia.
  44. Limayem, M., Hirt, S. G. and Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 31(4), 705-737.
  45. Liu, C., Zhu, Q., Holroyd, K. A. and Seng, E. K. (2011). Status And Trends Of Mobile-Health Applications For Ios Devices: A Developer's Perspective. Journal of Systems and Software, 84(11), 2022-2033.
  46. Lu, J., Yu, C. S. and Liu, C. (2009). Mobile Data Service Demographics in Urban China. Journal of Computer Information Systems, 50(2), 117-126.
  47. Martínez-Caro E, Cegarra-Navarro JG and Solano-Lorente M. (2013). Understanding Patient E-Loyalty Toward Online Health Care Services. Health Care Management Review, 38(1), 61–70.
  48. Mun, Y. Y., Jackson, J. D., Park, J. S. and Probst, J. C. (2006). Understanding Information Technology Acceptance by Individual Professionals: Toward an Integrative View. Information & Management, 43(3), 350-363.
  49. Ndayizigamiye, P., Kante, M. and Shingwenyana, S. (2020). An Adoption Model Of Mhealth Applications That Promote Physical Activity. Cogent Psychology, 7(1), 1764703.
  50. Ni, Z., Wu, B., Samples, C. and Shaw, R. J. (2014). Mobile Technology For Health Care in Rural China. International Journal of Nursing Sciences, 1(3), 323-324.
  51. Qingfei, M. I. N., Shaobo, J. I. and Gang, Q. U. (2012). Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Methodology, 374, 382.
  52. Or, C. K., Karsh, B. T., Severtson, D. J., Burke, L. J., Brown, R. L. and Brennan, P. F. (2011). Factors Affecting Home Care Patients’ Acceptance Of A Web-Based Interactive Selfmanagement Technology. Journal of the American Medical Informatics Association, 18(1), 51–59.
  53. Phichitchaisopa, N. and Naenna, T. (2013). Factors Affecting The Adoption Of Healthcare Information Technology. EXCLI Journal, 12, 413.
  54. Rawstorne P, Jayasuriya R. and Caputi P. (2000). Issues in Predicting And Explaining Usage Behaviors With The Technology Acceptance Model And The Theory Of Planned Behavior When Usage is Mandatory. In: Proceedings of the 21st International Conference On Information Systems; 35–44.
  55. Ringle, C. M., Wende, S. and Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH.
  56. Saumell, R.P., Forgas-Coll, S., Sánchez-García, J. and Robres, E. (2019). User Acceptance Of Mobile Apps For Restaurants: An Expanded And Extended UTAUT-2. Sustainability, 11(4), 1210.
  57. Schaper, L. and Pervan, G. (2007). ICT & OTs: A Model of Information And Communications Technology Acceptance And Utilisation By Occupational Therapists (part 2). Studies in Health Technology And Informatics, 130, 91-101.
  58. Shin, D. H. (2010). Modelling The Interaction Of Users And Mobile Payment System: Conceptual Framework. International Journal of Human-Computer Interaction, 26, 917–940.
  59. Sun, Y., Wang, N., Guo, X. and Peng, Z. (2013). Understanding The Acceptance Of Mobile Health Services: A Comparison And Integration of Alternative Model. Journal of Electronic Commerce Research, 14(2), 183–200.
  60. Tak, P. and Panwar, S. (2017). Using UTAUT 2 Model To Predict Mobile App Based Shopping: Evidences From India. Journal of Indian Business Research, 9 (3), 248-264.
  61. Venkatesh, V. and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  62. Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). User Acceptance Of İnformation Technology: Toward A Unified View. MIS Quarterly, 425-478.
  63. Venkatesh, V. and Bala, H. (2008). Technology Acceptance Model 3 And A Research Agenda On Interventions. Decision Sciences, 39(2), 273-315.
  64. Venkatesh, V., Thong, J. Y. and Xu, X. (2012). Consumer Acceptance And Use Of İnformation Technology: Extending The Unified Theory Of Acceptance And Use Of Technology. MIS Quarterly, 157-178.
  65. Venugopal, P., Jinka, S., & Priya, S. A. (2016). User acceptance of electronic health records: Cross validation of utaut model. Global Management Review, 10(3), 42-54.
  66. Yan, H., Huo, H., Xu, Y. and Gidlund, M. (2010). Wireless Sensor Network Based E-Health System-İmplementation And Experimental Results. IEEE Transactions on Consumer Electronics, 56(4), 2288-2295.