m-Health as an important part of e-health has recently become one of the most influential initiative in healthcare sector all over the world. In developing countries healthcare service providers started to provide m-health services from the last few years. Despite the widespread acceptance of mobile phones, the adoption of m-health among elderly is significantly low in developing countries. However, little research has been conducted to explore factors influencing elderly's intention to use m-health services particularly in developing countries. The objective of this study is to identify the factors that influence the elderly's intention to use m-health services.
Materials and Methods:
To assess elderly's intention to use m-health services, this study applied the Unified Theory of Acceptance and Use of Technology (UTAUT). Data were collected from participants of age 60 years and above. The partial least square method based on structural equation modeling was used to analyze data.
Results:
The study found that performance expectancy, effort expectancy, social influence, and perceived credibility (p < 0.05) had significant influence on elderly's intention to use m-health services. However, facilitating condition (p > 0.05) had no significant influence on elderly's intention to use m-health services.
Conclusions:
The findings of this study may become beneficial for the governments, policy makers, and healthcare service providers in developing countries.
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References
1.
ConsultingVW.m-Health for development: The opportunity of mobile technology for healthcare in the developing world. Washington DC and Berkshire, UK: UN Foundation-Vodafone Foundation Partnership, 2009.
2.
KhatunF, HeywoodAE, RayPK, HanifiSM, BhuiyaA, LiawST. Determinants of readiness to adopt m-Health in a rural community of Bangladesh. Int J Med Inform, 2015; 84:847–856.
3.
UN.. World Population Prospects: The 2015 Revision, Department of Economic, United Nations Publications, New York, NY, 2015.
4.
KhanamMA, StreatfieldPK, KabirZN, QiuC, CorneliusC, WahlinÅ. Prevalence and patterns of multimorbidity among elderly people in rural Bangladesh: A cross-sectional study. J Health Popul Nutr, 2011; 29:406–414.
5.
AldrichN, BensonWF. Disaster preparedness and the chronic disease needs of vulnerable older adults. Prev Chronic Dis, 2008; 5:A27.
6.
WickramasingheNS, FadlallaAM, GeislerE, SchafferJL. A framework for assessing e-health preparedness. Int J Elect Healthc, 2005; 1:316–334.
7.
HuY, Shyam SundarS. Effects of online health sources on credibility and behavioral intentions. Commun Res, 2010; 37:105–132.
8.
Mueller‐JohnsonK, TogliaMP, SweeneyCD, CeciSJ. The perceived credibility of older adults as witnesses and its relation to ageism. Behav Sci Law, 2007; 25:355–375.
9.
VenkateshV, MorrisMG, DavisGB, DavisFD. User acceptance of information technology: Toward a unified view. MIS Q, 2003; 27:425–478.
10.
CarlssonC, CarlssonJ, HyvonenK, PuhakainenJ, WaldenP. Adoption of mobile devices/services—searching for answers with the UTAUT. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. HICSS'06. IEEE, New Jersey, 2006; 6:132a–132a.
11.
HoqueR, SorwarG. Understanding factors influencing the adoption of m-Health by the elderly: An extension of the UTAUT model. Int J Med Inform, 2017; 101:75–84.
12.
HoqueMR, AlbarA, AlamJ. Factors influencing physicians' acceptance of e-health in developing country: An empirical study. Int J Healthc Inf Syst Inform, 2016; 11:58–70.
13.
KijsanayotinB, PannarunothaiS, SpeedieSM. Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. Int J Med Inform, 2009; 78:404–416.
14.
SunY, WangN, GuoX, PengZ. Understanding the acceptance of mobile health services: A comparison and integration of alternative models. J Electr Commerce Res, 2013; 14:183.
15.
PanS, Jordan-MarshM. Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Hum Behav, 2010; 26:1111–1119.
16.
AggelidisVP, ChatzoglouPD. Using a modified technology acceptance model in hospitals. Int J Med Inform, 2009; 78:115–126.
17.
MunYY, JacksonJD, ParkJS, ProbstJC. Understanding information technology acceptance by individual professionals: Toward an integrative view. Inform Manag, 2006; 43:350–363.
18.
KarjaluotoH.Selection criteria for a mode of bill payment: Empirical investigation among Finnish bank customers. Int J Retail Distrib Manag, 2002; 30:331–339.
19.
LallmahamoodM.Privacy over the internet in Malaysia: A survey of general concerns and preferences among private individuals (Doctoral dissertation, Malaysian Institute of Management), 2008.
20.
OniAA, AyoCK. An empirical investigation of the level of users' acceptance of e-banking in Nigeria. J Internet Bank Commerce, 2010; 15:1–13.
21.
BhattacherjeeA, HikmetN. Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector. J Comp Inform Syst, 2008; 48:69–76.
22.
VenkateshV, DavisFD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag Sci, 2000; 46:186–204.
23.
BoontarigW, ChutimaskulW, ChongsuphajaisiddhiV, PapasratornB.Factors influencing the Thai elderly intention to use smartphone for e-Health services. IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, New Jersey, 2012:479–483.
24.
VenkateshV, ThongJY, XuX. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q, 2012; 36:157–178.
25.
WangYS, WangYM, LinHH, TangTI. Determinants of user acceptance of internet banking: An empirical study. Int J Serv Ind Manag, 2003; 14:501–519.
26.
TungFC, ChangSC, ChouCM. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform, 2008; 77:324–335.
27.
TaylorS, ToddP. Assessing IT usage: The role of prior experience. MIS Q, 1995; 19:561–570.
28.
DavisFD, VenkateshV. Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Trans Eng Manage, 2004; 51:31–46.
29.
GötzO, Liehr-GobbersK, KrafftM.Evaluation of structural equation models using the partial least squares (PLS) approach. In: VinziV, ChinWW, HenselerJ, WangH, eds. Handbook of partial least squares. Berlin Heidelberg: Springer, 2010:691–711.
30.
HairJFJr., HultGTM. A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications, 2016.
31.
BagozziRP, YiY, PhillipsLW. Assessing construct validity in organizational research. Admin Sci Q, 1991; 421–458.
32.
HairJFJr., AndersonRE, TathamRL, WilliamC, Black. Multivariate data analysis with readings. Upper Saddle River, NJ: Prentice Hall: 1995.
33.
HenselerJ, RingleCM, SinkovicsRR, The use of partial least squares path modeling in international marketing. In: SinkovicsRR, GhauriPN, eds. New challenges to international marketing. Bingley, UK: Emerald Group Publishing Limited, 2009:277–319.
34.
HoqueMR, BaoY, SorwarG. Investigating factors influencing the adoption of e-Health in developing countries: A patient's perspective. Inform Health Soc Care, 2017; 42:1–17.
35.
HoqueMR, BaoY. Cultural influence on adoption and use of e-Health: Evidence in Bangladesh. Telemed J E Health, 2015; 21:845–851.
36.
BaoY, HoqueR, WangS. Investigating the determinants of Chinese adult children's intention to use online health information for their aged parents. Int J Med Inform, 2017; 102:12–20.
37.
VeerAJ, PeetersJM, BrabersAE, SchellevisFG, RademakersJJJ, FranckeAL. (2015). Determinants of the intention to use e-Health by community dwelling older people. BMC Health Serv Res, 2015; 15:103.
