An analysis of determinants of the adoption of Mobile Health (mHealth)

Main Article Content

Nayra Leandro Miguel Martins
Paulo Duarte
José Carlos M. R. Pinho


Given the increasing use of the Internet and mobile technologies, this study is pertinent as it seeks to analyze the factors
that determine the adoption of Mobile Health (mHealth). To that end, the proposed conceptual model integrates the Unified
Theory of Acceptance and Use of Technology (UTAUT2), Perceived Health Condition, eHealth Literacy and Perceived Health
Competence as determinants of the adoption of mHealth. To answer the research questions, we used an online questionnaire
administered to a non-probabilistic sample of Brazilian and Portuguese individuals who have or have not used mHealth. Data
were analyzed using SPSS and SmartPLS3 software. The results indicate that adoption of mHealth is heavily impacted only by
some UTAUT2 variables. The 'Performance Expectancy' dimension was found to heavily impact the adoption of mHealth among
both users and non-users.


Download data is not yet available.


Metrics Loading ...

Article Details

How to Cite
MIGUEL MARTINS, N. L.; DUARTE, P. .; M. R. PINHO, J. C. An analysis of determinants of the adoption of Mobile Health (mHealth). RAE - Revista de Administracao de Empresas , [S. l.], v. 61, n. 4, p. 1–17, 2021. DOI: 10.1590/S0034-759020210403. Disponível em: Acesso em: 17 jun. 2024.


Agarwal, N. K., Wang, Z., Xu, Y., & Poo, D. C. C. (2007). Factors

affecting 3G adoption: An empirical study. PACIS 2007

Proceedings, Paper 3, 256-270. Retrieved from http://aisel.

Anantraman, V., Mikkelsen, T., Khilnani, R., Kumar, V. S.,

Pentland, A., & Ohno-Machado, L. (2002). Open source

handheld-based EMR for paramedics working in rural areas.

AMIA - Annual Symposium Proceedings. AMIA Symposium, 12-

doi: D020002441[pii]

Bachmann, J. M., Goggins, K. M., Nwosu, S. K., Schildcrout, J.

S., Kripalani, S., & Wallston, K. A. (2016). Perceived health

competence predicts health behavior and health-related

quality of life in patients with cardiovascular disease. Patient

Education and Counseling, 99, 2071-2079. doi: 10.1016/j.


Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural

equation models. Journal of the Academy of Marketing

Science, 16(1), 74-94. doi: 10.1007/bf02723327

Barrett, J. R., Strayer, S. M., & Schubart, J. R. (2004). Assessing

medical residents’ usage and perceived needs for personal

digital assistants. International Journal of Medical Informatics,

(1), 25-34. doi: 10.1016/j.ijmedinf.2003.12.005

Basu, A., & Dutta, M. J. (2008). The relationship between

health information seeking and community participation:

The roles of health information orientation and

efficacy. Health Communication, 23(1), 70-79. doi:


Becker, M. H., & Janz, N. K. (1984). The health belief model: A

decade later. Health Education Quarterly, 11(1), 1-47. doi:


Bernhardt, J. M., McClain, J., & Parrott, R. L. (2004). Online health

communication about human genetics : Perceptions and

preferences of internet users. CyberPsychology and Behavior,

(6), 728-733. doi: 10.1089/cpb.2004.7.728

Biesdorf, S., & Niedermann, F. (2014). Healthcare’s digital future.

McKinsey & Company.

Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000).

Technology infusion in service encounters. Journal of

the Academy of Marketing Science, 28(1), 138-149. doi:


Bodie, G. D., & Dutta, M. J. (2008). Understanding health literacy

for strategic health marketing: eHealth literacy, health

disparities, and the digital divide. Health Marketing Quarterly,

(1-2), 175-203. doi: 10.1080/07359680802126301

Brown, S. A., & Venkatesh, V. (2005). Model of adoption and

technology in households: A baseline model test and

extension incorporating household life cycle. MIS Ouarterly,

(3), 399-426. doi: 10.2307/25148690

Burkhardt, M. E., & Brass, D. J. (1990). Changing patterns or

patterns of change: The effects of a change in technology on

social network structure and power. Administrative Science

Quarterly, 35(1), 104-127. doi: 10.2307/2393552

Cameron, J. D., Ramaprasad, A., & Syn, T. (2017). An ontology

of and roadmap for mHealth research. International

Journal of Medical Informatics, 100, 16-25. doi: 10.1016/j.


Carlos, D. A. O., Magalhães, T. O., Vasconcelos, J. E., Filho, Silva, R.

M., & Brasil, C. C. P. (2016, setembro). Concepção e avaliação

de tecnologia mHealth para promoção da saúde vocal. Risti -

Revista Ibérica de Sistemas e Tecnologias de Informação, (19),

-60. doi: 10.17013/risti.19.46-60

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic

and utilitrian motivations for online retail shopping behavior.

Journal of Retailing, 77, 511-535. doi: 10.1016/S0022-


Cho, J., Park, D., & Lee, H. E. (2014). Cognitive factors of using

health apps: Systematic analysis of relationships among

health consciousness, health information orientation,

eHealth literacy, and health app use efficacy. Journal of

Medical Internet Research, 16(5), e125. doi: 10.2196/jmir.3283

Chong, A. Y. L. (2013). A two-staged SEM-neural network

approach for understanding and predicting the determinants

of m-commerce adoption. Expert Systems with Applications,

(4), 1240-1247. doi: 10.1016/j.eswa.2012.08.067

Cotten, S. R., & Gupta, S. S. (2004). Characteristics of online

and offline health information seekers and factors that

discriminate between them. Social Science and Medicine,

(9), 1795-1806. doi: 10.1016/j.socscimed.2004.02.020

Déglise, C., Suggs, L. S., & Odermatt, P. (2012). Short Message

Service (SMS) applications for disease prevention in

developing countries. Journal of Medical Internet Research,

(1), e3. doi: 10.2196/jmir.1823

Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of

price, brand, and store information on buyers’ product

evaluations. Journal of Marketing Research, 28(3), 307-319.

doi: 10.2307/3172866

Duarte, P. A. O., & Raposo, M. L. B. (2010). A PLS model to study

brand preference: An application to the mobile phone market.

In Handbook of partial least squares, 449-485. Springer,

Berlin, Heidelberg. doi: 10.1007/978-3-540-32827-8_21

Duque, C., Mamede, J., & Morgado, L. (2017). Iniciativas de

mHealth em Portugal. In: CISTI 2017: 12th Iberian Conference

on Information Systems and Technologies. 1-6, IEEE. doi:


Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., &

Weerakkody, V. (2016). A generalised adoption model for

services: A cross-country comparison of mobile health

(m-health). Government Information Quarterly, 33(1), 174-187.

doi: 10.1016/j.giq.2015.06.003

Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online

purchasing tickets for low cost carriers: An application of the

unified theory of acceptance and use of technology (Utaut)

model. Tourism Management, 43, 70-88. doi: 10.1016/j.


Finkelstein, M. M. (2000). Hypertension, self-perceived health

status and use of primary care services. Canadian Medical

Association Journal, 162(1), 45-46. Retrieved from: https://

Fornell, C., & Larcker, D. F. (1981). Structural equation models

with unobservable variables and measurement error: Algebra

and statistics. Journal of Marketing Research, 18(3), 382. doi:


Fox, S., & Duggan, M. (2012). Mobile Health 2012. Pew

Research Center’s Internet & American Life Project. Retrieved


Free, C., Phillips, G., Felix, L., Galli, L., Patel, V., & Edwards,

P. (2010). The effectiveness of M-health technologies for

improving health and health services: a systematic review

protocol. BMC Research Notes, 3, 250. doi: 10.1186/1756-


Gadelha, C. A. G., & Costa, L. S. (2012). Saúde e desenvolvimento

no Brasil: Avanços e desafios. Revista de Saúde Pública, 46(1),

-20. doi: 10.1590/s0034-89102012005000062

Heijden, H. Van der. (2004). User acceptance of hedonic

information systems. MIS Quarterly, 28(4), 695-704. doi:


Katz, R., & Tushman, M. (1979). Communication patterns,

project performance, and task characteristics: An empirical

evaluation and integration in an R&D setting. Organizational

Behavior and Human Performance, 23(2), 139-162. doi:


Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of

continued IS use: An integrative view of four mechanisms

underlying postadoption phenomena. Management Science,

(5), 741-755. doi: 10.1287/mnsc.1040.0326

Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research

note – Two competing perspectives on automatic use: A

theoretical and empirical comparison. Information Systems

Research, 16(4), 418-432. doi: 10.1287/isre.1050.0070

Koop, A., & Mösges, R. (2002). The use of handheld computers

in clinical trials. Controlled Clinical Trials, 23(5), 469-480. doi:


Kotz, D., Avancha, S., & Baxi, A. (2009). A privacy framework for

mobile health and home-care systems. Proceedings of the

First ACM Workshop on Security and Privacy in Medical and

Home-Care Systems – Spimacs ’09, November 1, 1-12. doi:


Kratzke, C., & Cox, C. (2012). Smartphone technology and apps:

Rapidly changing health promotion. International Electronic

Journal of Health Education, 15, 72-82. doi: ISSN-1529-1944

Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the

behavioral intention to use 3G mobile value-added services.

Computers in Human Behavior, 25(1), 103-110. doi: 10.1016/j.


Laxminarayan, S., & Istepanian, R. S. H. (2000). Unwired E-MED:

The next generation of wireless and Internet telemedicine

systems. IEEE Transactions on Information Technology in

Biomedicine, 4(3), 189-193. doi: 10.1109/TITB.2000.5956074

Leal, S. A. (2009). Estado de saúde auto-percebido: Índice

de massa corporal e percepção da imagem corporal em

utentes dos cuidados de saúde primários (Dissertação de

mestrado, Faculdade de Psicologia e Ciências de Educação ,

Universidade de Lisboa, Lisboa, Portugal).

Lee, K., Hoti, K., Hughes, J. D., & Emmerton, L. M. (2015).

Consumer use of “Dr Google”: A survey on health informationseeking behaviors and navigational needs. Journal of Medical

Internet Research, 17(12), e288. doi: 10.2196/jmir.4345

Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits

the predictive power of intention: The case of information

systems continuance. MIS Quarterly, 31(4), 705-737. doi:


Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the

behavioral intention to use mobile banking. Computers

in Human Behavior, 21(6), 873-891. doi: 10.1016/j.


Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multidimensional trust and multi-faceted risk in initial acceptance

of emerging technologies: An empirical study of mobile

banking services. Decision Support Systems, 49(2), 222-234.

doi: 10.1016/j.dss.2010.02.008

Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., &

Pounders, K. (2016). Health literacy and health information

technology adoption: The potential for a new digital divide.

Journal of Medical Internet Research, 18(10), e264. doi:


Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants

of end-user acceptance of biometrics: Integrating the “big

” of technology acceptance with privacy context. Decision

Support Systems, 56, 103-114. doi: 10.1016/j.dss.2013.05.010

Neter, E., & Brainin, E. (2012). eHealth literacy: Extending the

digital divide to the realm of health information. Journal of

Medical Internet Research, 14(1), e19. doi: 10.2196/jmir.1619

Norman, C. D., & Skinner, H. A. (2006a). eHEALS: The eHealth

literacy scale. Journal of Medical Internet Research, 8(4), e27.

doi: 10.1525/cmr.2014.57.1.67

Norman, C. D., & Skinner, H. A. (2006b). eHealth literacy:

Essential skills for consumer health in a networked world.

Journal of Medical Internet Research, 8(2), 1-11. doi: 10.2196/


Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.).

New York, USA: McGraw-Hill.

Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014).

Extending the understanding of mobile banking adoption:

When Utaut meets TTF and ITM. International Journal of

Information Management, 34(5), 689-703. doi: 10.1016/j.


Ong, J. W., Poong, Y. S., & Ng, T. H. (2008). 3G services adoption

among university students: Diffusion of innovation theory.

Communications of the IBIMA, 3(16), 114-121.

Rai, A., Chen, L., Pye, J., & Baird, A. (2013). Understanding

determinants of consumer mobile health usage intentions,

assimilation, and channel preferences. Journal of Medical

Internet Research, 15(8), e149. doi: 10.2196/jmir.2635

Research2guidance. (2016). mHealth App Developer Economics

Retrieved from


Riffai, M. M. M. A., Grant, K., & Edgar, D. (2012). Big TAM in

Oman: Exploring the promise of on-line banking, its adoption

by customers and the challenges of banking in Oman.

International Journal of Information Management, 32(3), 239-

doi: 10.1016/j.ijinfomgt.2011.11.007

Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S.

M., & Mermelstein, R. (2011). Health behavior models in the

age of mobile interventions: Are our theories up to the task?

Translational Behavioral Medicine, 1(1), 53-71. doi: 10.1007/


Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3:

SmartPLS GmbH, Boenningstedt. Journal of Service Science

and Management, 10(3). Retrivied from: https://www.scirp.


Shareef, M. A., Kumar, V., & Kumar, U. (2014). Predicting mobile

health adoption behaviour: A demand side perspective.

Journal of Customer Behaviour, 13(3), 187-205. doi: 10.1362/1


Shields, M., & Shooshtari, S. (2001). Determinants of selfperceived health. Health Reports – Statistics Canada,

(1), 35-52. Retrieved from: https://pubmed.ncbi.nlm.nih.


Smith, M. S., Wallston, K. A., & Smith, C. A. (1995). The

development and validation of the perceived health

competence scale. Health Education Research, 10(1), 51-64.

doi: 10.1093/her/10.1.51

Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging

field of mobile health. Science Translational Medicine, 7(283),

rv3-283rv3. doi: 10.1126/scitranslmed.aaa3487

Sweileh, W. M., Al-Jabi, S. W., AbuTaha, A. S., Zyoud, S. H.,

Anayah, F. M. A., & Sawalha, A. F. (2017). Bibliometric analysis

of worldwide scientific literature in mobile – health: 2006 –

BMC Medical Informatics and Decision Making, 17 (1),

doi: 10.1186/s12911-017-0476-7

Tan, P. J. B. (2013). Applying the Utaut to understand factors

affecting the use of english e-learning websites in Taiwan.

SAGE Open, 3(4), 1-12. doi: 10.1177/2158244013503837

Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of postadoption beliefs on the expectation-confirmation model for

information technology continuance. International Journal

of Human Computer Studies, 64(9), 799-810. doi: 10.1016/j.


Tomás, C. C., Queirós, P. J. P., & Ferreira, T. J. R. (2014). Análise

das propriedades psicométricas da versão portuguesa de um

instrumento de avaliação de e-Literacia em saúde. Revista

de Enfermagem Referência, série IV(2), 19-28. Retrieved from

arttext&pid=S0874-02832014000200003&lang=pt. doi:


Venkatesh, V., & Morris, G. M. (2000). Why don’t men ever stop

to ask for direction? Gender, social influence and their role in

technology acceptance and usage behaviour. MIS Quarterly,

(1), 115-139. doi: 10.2307/3250981

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003).

User acceptance of information technology: Toward a unified

view. MIS Quarterly, 27(3), 425-478. doi: 10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer

acceptance and use of information technology: Extending

the unified theory of acceptance and use of technology. MIS

Quarterly, 36(1), 157-178. doi: 10.2307/41410412

Wei, T. T., Marthandan, G., Chong, A. Y. L., Ooi, K. B., & Arumugam,

S. (2009). What drives Malaysian m-commerce adoption? An

empirical analysis. Industrial Management & Data Systems,

(3), 370-388. doi: 10.1108/02635570910939399

World Health Organization. (2019). WHO guideline: Recommendations on digital interventions for health system strengthening. Geneva, Switzerland. Licence: CC BY-NC-SA 3.0 IGO.

Xin, X. (2004). A model of 3G adoption. AMCIS 2004 Proceedings.

Paper 329, 2755-2762. Retrieved from