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Health and Technology

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14.04.2020 | Original Paper

An empirical investigation of the adoption of mobile health applications: integrating big data and social media services

verfasst von: Tahereh Saheb

Erschienen in: Health and Technology | Ausgabe 5/2020

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Abstract

With the advancement of digital technologies, the mobile healthcare industry aims to enhance health intelligence through delivering transformational digital services. This study integrates knowledge derived from three models of innovation diffusion, privacy calculus, and information systems success with the current studies on technology acceptance literature in the context of mHealth apps. It focuses on the contribution of big data and social media in enhancing mobile health apps and their impact on patients’ behavior and adoption. Through structural equation modeling of 582 questionnaires, this study develops a framework analyzing the impact of data quality, social interactivity, personalization, data risk, and performance risk. The study confirms the association between personalization, data quality, and data risk on the adoption decisions, while it did not find a link between social connectivity and the adoption decision. Theoretically, this paper builds new knowledge on the technology adoption literature by emphasizing digital services in the context of mobile health apps. Practically, this paper can assist the healthcare care industry to re-engineer its traditional business models by delivering enhanced digital services during the design of mobile health apps.

Vorheriger Artikel A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect

Nächster Artikel The relationship between users’ technology approaches and experiences in a child development mobile application

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Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Res Notes. 2010;3:250. https://doi.org/10.1186/1756-0500-3-250.CrossRef

C.K. Martin, L.A. Gilmore, J.W. Apolzan, C.A. Myers, D.M. Thomas, L.M. Redman, Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion, JMIR MHealth UHealth. 4 (2016). doi:https://doi.org/10.2196/MHEALTH.5027.

Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. Npj Digit Med. 2018;1:53. https://doi.org/10.1038/s41746-018-0058-9.CrossRef

K. Wagner, How Mobile health is changing care delivery, HFMA. (2014). https://www.hfma.org/leadership/mobilehealth/ (accessed December 31, 2018).

Vazquez M, Garibaldi JA, Nieto JI, de Sanchez JD. Model for Personalization of Mobile Health Systems for Monitoring Patients with Chronic Disease. IEEE Lat Am Trans. 2016;14:965–70. https://doi.org/10.1109/TLA.2016.7437247.CrossRef

Coppieters Y, Levêque A. Ethics, privacy and the legal framework governing medical data: opportunities or threats for biomedical and public health research? Arch Public Health. 2013;71:15. https://doi.org/10.1186/0778-7367-71-15.CrossRef

Hussain M, Zaidan AA, Zidan BB, Iqbal S, Ahmed MM, Albahri OS, et al. Conceptual framework for the security of mobile health applications on android platform. Telemat Informatics. 2018;35:1335–54. https://doi.org/10.1016/J.TELE.2018.03.005.CrossRef

F.H. Mckay, C. Cheng, A. Wright, J. Shill, H. Stephens, M. Uccellini, Evaluating mobile phone applications for health behaviour change: A systematic review, (n.d.). doi:https://doi.org/10.1177/1357633X16673538.

Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mHealth apps: a systematic literature review. J Med Syst. 2015;39:1. https://doi.org/10.1007/s10916-014-0182-2.CrossRef

10.

Eng DS, Lee JM. The promise and peril of Mobile health applications for diabetes and endocrinology. Pediatr Diabetes. 2013;14:231–8. https://doi.org/10.1111/pedi.12034.CrossRef

11.

Lee J. Hype or hope for diabetes mobile health applications? §. Diabetes Res Clin Pract. 2014;106:390–2. https://doi.org/10.1016/j.diabres.2014.11.001.CrossRef

12.

Schnall R, Rojas M, Bakken S, Brown W, Carballo-Dieguez A, Carry M, et al. A user-centered model for designing consumer mobile health (mHealth) applications (apps). J Biomed Inform. 2016;60:243–51. https://doi.org/10.1016/J.JBI.2016.02.002.CrossRef

13.

Ahmed E, Yaqoob I, Hashem IAT, Shuja J, Imran M, Guizani N, et al. Recent advances and challenges in Mobile big data. IEEE Commun Mag. 2018;56:102–8. https://doi.org/10.1109/MCOM.2018.1700294.CrossRef

14.

Anshari M, Almunawar MN, Lim SA, Al-Mudimigh A. Customer relationship management and big data enabled: personalization & customization of services. Appl Comput Informatics. 2019;15:94–101. https://doi.org/10.1016/J.ACI.2018.05.004.CrossRef

15.

D. Buhalis, A. Amaranggana, Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services, in: Inf. Commun. Technol. Tour. 2015, Springer international publishing, Cham, 2015: pp. 377–389. doi:https://doi.org/10.1007/978-3-319-14343-9_28.

16.

Pelletier J-F, Rowe M, François N, Bordeleau J, Lupien S. No personalization without participation: on the active contribution of psychiatric patients to the development of a mobile application for mental health. BMC Med Inform Decis Mak. 2013;13:78. https://doi.org/10.1186/1472-6947-13-78.CrossRef

17.

Kamel Boulos MN, Wheeler S. The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Inf Libr J. 2007;24:2–23. https://doi.org/10.1111/j.1471-1842.2007.00701.x.CrossRef

18.

Higgins JP. Smartphone applications for patients’ health and fitness. Am J Med. 2016;129:11–9. https://doi.org/10.1016/J.AMJMED.2015.05.038.CrossRef

19.

Kang X, Zhao L, Leung F, Luo H, Wang L, Wu J, et al. Delivery of Instructions via Mobile Social Media App Increases Quality of Bowel Preparation. Clin Gastroenterol Hepatol. 2016;14:429–435.e3. https://doi.org/10.1016/J.CGH.2015.09.038.CrossRef

20.

C.L. Ventola, Mobile devices and apps for health care professionals: uses and benefits., P T 39 (2014) 356–64. http://www.ncbi.nlm.nih.gov/pubmed/24883008 (accessed December 30, 2018).

21.

P. Aditya, V. Erdélyi, M. Lentz, E. Shi, B. Bhattacharjee, P. Druschel, EnCore, in: Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv. - MobiSys ‘14, ACM press, New York, New York, USA, 2014: pp. 135–148. doi:https://doi.org/10.1145/2594368.2594374.

22.

Li H, Wu J, Gao Y, Shi Y. Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. Int J Med Inform. 2016;88:8–17. https://doi.org/10.1016/j.ijmedinf.2015.12.010.CrossRef

23.

Oliveira T, Thomas M, Baptista G, Campos F. Mobile payment: understanding the determinants of customer adoption and intention to recommend the technology. Comput Hum Behav. 2016;61:404–14. https://doi.org/10.1016/J.CHB.2016.03.030.CrossRef

24.

Wang Y-S, Li H-T, Li C-R, Zhang D-Z. Factors affecting hotels’ adoption of mobile reservation systems: a technology-organization-environment framework. Tour Manag. 2016;53:163–72. https://doi.org/10.1016/J.TOURMAN.2015.09.021.CrossRef

25.

Ardolino M, Rapaccini M, Saccani N, Gaiardelli P, Crespi G, Ruggeri C. The role of digital technologies for the service transformation of industrial companies. Int J Prod Res. 2018;56:2116–32. https://doi.org/10.1080/00207543.2017.1324224.CrossRef

26.

S. Cavalieri, Z.M. Ouertani, J. Zhibin, A. Rondini, Service transformation in industrial companies,(2017). doi:https://doi.org/10.1080/00207543.2017.1378830.

27.

F. Davis, A technology acceptance model for empirically testing new end-user information systems: theory and results, (1985). https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf (accessed October 27, 2018).

28.

Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci. 2000;46:186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.CrossRef

29.

Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27:425. https://doi.org/10.2307/30036540.CrossRef

30.

E.M. Rogers, Diffusion of innovations, Free Press, 2003.

31.

E. Rogers, Diffusion of innovations, Free Press, 1995. https://www.amazon.com/Diffusion-Innovations-Fourth-Everett-1995-02-01/dp/B01N0BQR1V/ref=sr_1_fkmr0_1?ie=UTF8&qid=1546588728&sr=8-1-fkmr0&keywords=Rogers%2C+E.+%281995%29.+Diffusion+of+innovations.+New+York%3A+Free+Press ().

32.

Dinev T, Hart P. An extended privacy Calculus model for E-commerce transactions. Inf Syst Res. 2006;17:61–80. https://doi.org/10.1287/isre.1060.0080.CrossRef

33.

V.H. Vroom, Work and motivation, Jossey-Bass Publishers, 1995.

34.

Delone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. J Manag Inf Syst. 2003;19:9–30. https://doi.org/10.1080/07421222.2003.11045748.CrossRef

35.

Hung M-C, Jen W-Y. The adoption of Mobile health management services: An empirical study. J Med Syst. 2012;36:1381–8. https://doi.org/10.1007/s10916-010-9600-2.CrossRef

36.

Xue L, Yen CC, Chang L, Chan HC, Tai BC, Tan SB, et al. An exploratory study of ageing women’s perception on access to health informatics via a mobile phone-based intervention. Int J Med Inform. 2012;81:637–48. https://doi.org/10.1016/j.ijmedinf.2012.04.008.CrossRef

37.

S. Fosso Wamba, S. Akter, Impact of Perceived Connectivity on Intention to Use Social Media: Modelling the Moderation Effects of Perceived Risk and Security, in: Springer, Cham, 2016: pp. 219–227. doi:https://doi.org/10.1007/978-3-319-45234-0_20.

38.

Guo X, Zhang X, Sun Y. The privacy–personalization paradox in mHealth services acceptance of different age groups. Electron Commer Res Appl. 2016;16:55–65. https://doi.org/10.1016/J.ELERAP.2015.11.001.CrossRef

39.

H. Sheng, F. Fui, H. Nah, K. Siau, An experimental study on ubiquitous commerce adoption: impact of personalization and privacy concerns * An experimental study on ubiquitous commerce adoption: impact of personalization and privacy concerns, 2008. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1444&context=jais (accessed January 2, 2019).

40.

Nasir S, Yurder Y. Consumers’ and physicians’ perceptions about high tech wearable health products. Procedia Soc Behav Sci. 2015;195:1261–7. https://doi.org/10.1016/J.SBSPRO.2015.06.279.CrossRef

41.

Deng Z, Hong Z, Ren C, Zhang W, Xiang F. What predicts patients’ adoption intention toward mHealth Services in China: empirical study. JMIR MHealth UHealth. 2018;6:e172. https://doi.org/10.2196/mhealth.9316.CrossRef

42.

Lee J, Kim HJ, Ahn MJ. The willingness of e-government service adoption by business users: the role of offline service quality and trust in technology. Gov Inf Q. 2011;28:222–30. https://doi.org/10.1016/J.GIQ.2010.07.007.CrossRef

43.

Nicolaou AI, McKnight DH. Perceived information quality in data exchanges: effects on risk, trust, and intention to use. Inf Syst Res. 2006;17:332–51. https://doi.org/10.1287/isre.1060.0103.CrossRef

44.

Komiak SYX, Benbasat I. The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Q. 2006;30:941. https://doi.org/10.2307/25148760.CrossRef

45.

Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V. A study on medical internet of things and big data in personalized healthcare system. Heal Inf Sci Syst. 2018;6:14. https://doi.org/10.1007/s13755-018-0049-x.CrossRef

46.

O’Leary DP, Zaheer A, Redmond HP, Corrigan MA. Integration of advances in social media and mHealth technology are pivotal to successful cancer prevention and control. MHealth. 2016;2:38. https://doi.org/10.21037/mhealth.2016.09.02.CrossRef

47.

S. An, H. Lee, Adoption of Mobile Apps for Mental Health: Socio-psychological and Technological Factors, in: 2018: pp. 29–37. doi:https://doi.org/10.1007/978-3-319-98551-0_4.

48.

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 e-Health. 2015;21:735–41. https://doi.org/10.1089/tmj.2014.0148.CrossRef

49.

Hsiao C-H, Chang J-J, Tang K-Y. Exploring the influential factors in continuance usage of mobile social apps: satisfaction, habit, and customer value perspectives. Telemat Informatics. 2016;33:342–55. https://doi.org/10.1016/J.TELE.2015.08.014.CrossRef

50.

Lim S, Xue L, Yen CC, Chang L, Chan HC, Tai BC, et al. A study on Singaporean women’s acceptance of using mobile phones to seek health information. Int J Med Inform. 2011;80:e189–202. https://doi.org/10.1016/j.ijmedinf.2011.08.007.CrossRef

51.

Zhang Y, Wen N, Chao N. Effects of mobile information-seeking on the intention to obtain reproductive cancer screening among chinese women: testing an integrative model. Chinese J Commun. 2018:1–20. https://doi.org/10.1080/17544750.2018.1528291.

52.

G. Byomire, G. Maiga, Adoption and Use of Mobile Phones for Maternal Healthcare Service Delivery, in: 2015: pp. 103–114. doi:https://doi.org/10.1007/978-3-319-16886-9_11.

53.

Ahadzadeh AS, Pahlevan Sharif S, Ong FS, Khong KW. Integrating health belief model and technology acceptance model: An investigation of health-related internet use. J Med Internet Res. 2015;17:e45. https://doi.org/10.2196/jmir.3564.CrossRef

54.

Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a technology acceptance model for apps in medical education. J Med Syst. 2015;39:176. https://doi.org/10.1007/s10916-015-0352-x.CrossRef

55.

Cho J. The impact of post-adoption beliefs on the continued use of health apps. Int J Med Inform. 2016;87:75–83. https://doi.org/10.1016/j.ijmedinf.2015.12.016.CrossRef

56.

Cho J, Quinlan MM, Park D, Noh G-Y. Determinants of adoption of smartphone health apps among college students. Am J Health Behav. 2014;38:860–70. https://doi.org/10.5993/AJHB.38.6.8.CrossRef

57.

Lin K-Y, Lu H-P. Predicting mobile social network acceptance based on mobile value and social influence. Internet Res. 2015;25:107–30. https://doi.org/10.1108/IntR-01-2014-0018.CrossRef

58.

Zhang M, Ng V, Zhao D, Li S, Zan H, editors. Natural language processing and Chinese computing. Cham: Springer International Publishing; 2018. https://doi.org/10.1007/978-3-319-99501-4.CrossRef

59.

Choi J, Lee JH, Vittinghoff E, Fukuoka Y. mHealth physical activity intervention: a randomized pilot study in physically inactive pregnant women. Matern Child Health J. 2016;20:1091–101. https://doi.org/10.1007/s10995-015-1895-7.CrossRef

60.

Krahe M, Milligan E, Reilly S. Personal health information in research: perceived risk, trustworthiness and opinions from patients attending a tertiary healthcare facility. J Biomed Inform. 2019;95:103222. https://doi.org/10.1016/J.JBI.2019.103222.CrossRef

61.

Marra MC, Hubbell BJ, Carlson GA. Information quality, technology depreciation, and Bt cotton adoption in the southeast. J Agric Resour Econ. 2001;26:1–18 https://ideas.repec.org/a/ags/jlaare/31158.html (CrossRef

62.

Yang Z, Cai S, Zhou Z, Zhou N. Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Inf Manag. 2005;42:575–89. https://doi.org/10.1016/j.im.2004.03.001.CrossRef

63.

Dabholkar PA. Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality. Int J Res Mark. 1996;13:29–51. https://doi.org/10.1016/0167-8116(95)00027-5.CrossRef

64.

Chen Y, Yang L, Zhang M, Yang J. Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. Int J Med Inform. 2018;116:33–45. https://doi.org/10.1016/J.IJMEDINF.2018.04.008.CrossRef

65.

Dholakia UM, Bagozzi RP, Pearo LK. A social influence model of consumer participation in network- and small-group-based virtual communities. Int J Res Mark. 2004;21:241–63. https://doi.org/10.1016/J.IJRESMAR.2003.12.004.CrossRef

66.

D.F. Galletta, Y. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Human-Computer Interaction for Electronic Commerce: A Program of Studies to Improve the Communication Between Customers and Online Stores, (2014) 33–44. doi:https://doi.org/10.4324/9781315703626-10.

67.

Wang S-J, Hsu C-P, Huang H-C, Chen C-L. How readers’ perceived self-congruity and functional congruity affect bloggers’ informational influence. Online Inf Rev. 2015;39:537–55. https://doi.org/10.1108/OIR-02-2015-0063.CrossRef

68.

Shaw LH, Gant LM. In defense of the internet: the relationship between internet communication and depression, loneliness, self-esteem, and perceived social support. CyberPsychology Behav. 2002;5:157–71. https://doi.org/10.1089/109493102753770552.CrossRef

69.

D. Li, P.Y.K. Chau, H. Lou, Understanding individual adoption of instant messaging: An empirical investigation *, 2005. https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1455&context=jais (accessed January 4, 2019).

70.

X. Luo, A. Gurung, J.P. Shim, Understanding the determinants of user acceptance of enterprise instant messaging: An empirical study, 2010. http://mds.marshall.edu/mis_faculty (accessed January 4, 2019).

71.

Lee Y-K, Park J-H, Chung N, Blakeney A. A unified perspective on the factors influencing usage intention toward mobile financial services. J Bus Res. 2012;65:1590–9. https://doi.org/10.1016/j.jbusres.2011.02.044.CrossRef

72.

Ko E, Kim EY, Lee EK. Modeling consumer adoption of mobile shopping for fashion products in Korea. Psychol Mark. 2009;26:669–87. https://doi.org/10.1002/mar.20294.CrossRef

73.

Hubert M, Blut M, Brock C, Backhaus C, Eberhardt T. Acceptance of smartphone-based Mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychol Mark. 2017;34:175–94. https://doi.org/10.1002/mar.20982.CrossRef

74.

Xu DJ. The influence of personalization in affecting consumer attitudes toward mobile advertising in CHina. J Comput Inf Syst. 2006:9–19. https://doi.org/10.1080/02650487.2007.11072997.

75.

Baaren E, Van De Wijngaert LD, Huizer E, Van De Wijngaert L. Understanding technology adoption through individual and context characteristics: the case of HDTV. J Broadcast Electron Media. 2011;55:72–89. https://doi.org/10.1080/08838151.2011.546257.CrossRef

76.

Arora N, Dreze X, Ghose A, Hess JD, Iyengar R, Jing B, et al. Putting one-to-one marketing to work: personalization, customization, and choice. Mark Lett. 2008;19:305–21. https://doi.org/10.1007/s11002-008-9056-z.CrossRef

77.

T. Bandyopadhyay, P. Meso, S. Negash, Information Technology for Development Mobile IT in health-the case of short messaging service in an HIV awareness program, (2017). doi:https://doi.org/10.1080/02681102.2017.1363029.

78.

Robins F. The marketing of 3G. Mark Intell Plan. 2003;21:370–8. https://doi.org/10.1108/02634500310499239.CrossRef

79.

L.R. Vijayasarathy, Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model, (2003). doi:https://doi.org/10.1016/j.im.2003.08.011.

80.

R. Schnall, T. Higgins, W. Brown, A. Carballo-Dieguez, S. Bakken, Trust, Perceived Risk, Perceived Ease of Use and Perceived Usefulness as Factors Related to mHealth Technology Use., Stud. Health Technol. Inform. 216 (2015) 467–71. http://www.ncbi.nlm.nih.gov/pubmed/26262094 (accessed January 4, 2019).

81.

Bensaou M, Venkatraman N. Inter-organizational relationships and information technology: a conceptual synthesis and a research framework. Eur J Inf Syst. 1996;5:84–91. https://doi.org/10.1057/ejis.1996.15.CrossRef

82.

Horst M, Kuttschreuter M, Gutteling JM. Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in the Netherlands. Comput Hum Behav. 2007;23:1838–52. https://doi.org/10.1016/J.CHB.2005.11.003.CrossRef

83.

Lee M-C. Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electron Commer Res Appl. 2009;8:130–41. https://doi.org/10.1016/J.ELERAP.2008.11.006.CrossRef

84.

Cocosila M, Archer N. Adoption of mobile ICT for health promotion: an empirical investigation. Electron Mark. 2010;20:241–50. https://doi.org/10.1007/s12525-010-0042-y.CrossRef

85.

Featherman MS, Pavlou PA. Predicting e-services adoption: a perceived risk facets perspective. Int J Human-Computer Stud. 2003;59:451–74. https://doi.org/10.1016/S1071-5819(03)00111-3.CrossRef

86.

CUNNINGHAM, M. S., The major dimensions of perceived risk, Risk Tak. Inf. Handl. Consum. Behav. (1967). https://ci.nii.ac.jp/naid/10021015678/ (accessed January 4, 2019).

87.

Laufer RS, Wolfe M. Privacy as a concept and a social issue: a multidimensional developmental theory. J Soc Issues. 1977;33:22–42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x.CrossRef

88.

G. Bansal, D. Gefen, The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online, (2010). doi:https://doi.org/10.1016/j.dss.2010.01.010.

89.

Kaptein M, Markopoulos P, de Ruyter B, Aarts E. Personalizing persuasive technologies: explicit and implicit personalization using persuasion profiles. Int J Hum Comput Stud. 2015;77:38–51. https://doi.org/10.1016/J.IJHCS.2015.01.004.CrossRef

90.

M. Dion, P. AbdelMalik, A. Mawudeku, Big data and the global public health intelligence network (GPHIN)., Can Commun Dis Rep 41 (2015) 209–214. http://www.ncbi.nlm.nih.gov/pubmed/29769954 (accessed August 18, 2019).

91.

B. Gökçay, Ethical overview of Health Research in the view of the protection of personal health data, Turk Kardiyol. Dern. Arsivi-Archives Turkish Soc. Cardiol. (2019). doi:https://doi.org/10.5543/tkda.2019.15957.

92.

Rodrigues LF, Oliveira A, Costa CJ. Does ease-of-use contributes to the perception of enjoyment? A case of gamification in e-banking. Comput Hum Behav. 2016;61:114–26. https://doi.org/10.1016/J.CHB.2016.03.015.CrossRef

93.

Badri M, Al Rashedi A, Yang G, Mohaidat J, Al Hammadi A. Students’ intention to take online courses in high school: a structural equation model of causality and determinants. Educ Inf Technol. 2016;21:471–97. https://doi.org/10.1007/s10639-014-9334-8.CrossRef

94.

Carlos Roca J, José García J, José de la Vega J. The importance of perceived trust, security and privacy in online trading systems. Inf Manag Comput Secur. 2009;17:96–113. https://doi.org/10.1108/09685220910963983.CrossRef

95.

Martins C, Oliveira T, Popovič A. Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int J Inf Manag. 2014;34:1–13. https://doi.org/10.1016/J.IJINFOMGT.2013.06.002.CrossRef

96.

T.D. Nguyen, T.C.H. Nguyen, The role of perceived risk on intention to use online banking in Vietnam, in: 2017 Int. Conf. Adv. Comput. Commun. Informatics, IEEE, 2017: pp. 1903–1908. doi:https://doi.org/10.1109/ICACCI.2017.8126122.

97.

Li J, Ma Q, Chan AH, Man SS. Health monitoring through wearable technologies for older adults: smart wearables acceptance model. Appl Ergon. 2019;75:162–9. https://doi.org/10.1016/J.APERGO.2018.10.006.CrossRef

98.

Upadhyay P, Jahanyan S. Analyzing user perspective on the factors affecting use intention of mobile based transfer payment. Internet Res. 2016;26:38–56. https://doi.org/10.1108/IntR-05-2014-0143.CrossRef

99.

Yoo CW, Goo J, Huang CD, Nam K, Woo M. Improving travel decision support satisfaction with smart tourism technologies: a framework of tourist elaboration likelihood and self-efficacy. Technol Forecast Soc Change. 2017;123:330–41. https://doi.org/10.1016/J.TECHFORE.2016.10.071.CrossRef

100.

M. Nakayama, S. Leon, Association for Information Systems AIS electronic library (AISeL) communication-driven usefulness hypothesis for online healthcare applications recommended citation, n.d. https://aisel.aisnet.org/ukais2018/7 (accessed January 6, 2019).

101.

Wang M, Cho S, Denton T. The impact of personalization and compatibility with past experience on e-banking usage. Int J Bank Mark. 2017;35:45–55. https://doi.org/10.1108/IJBM-04-2015-0046.CrossRef

Titel: An empirical investigation of the adoption of mobile health applications: integrating big data and social media services
verfasst von: Tahereh Saheb
Publikationsdatum: 14.04.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Health and Technology / Ausgabe 5/2020
Print ISSN: 2190-7188
Elektronische ISSN: 2190-7196
DOI: https://doi.org/10.1007/s12553-020-00422-9

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