Cited by 144Mobile Health and mHealth ApplicationsHealth, Technology, Consumer BehaviorTechnology Adoption and User Behaviour

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Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach

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Alam, Mirza Mohammad Didarul, Alam, Mohammad Zahedul, Rahman, Syed Abidur, Taghizadeh, Seyedeh Khadijeh·2021·Journal of Biomedical Informatics

DOI 10.1016/j.jbi.2021.103722PMC PMC8970651

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Abstract

The objectives of this study are to examine the factors affecting the intention and actual usage behavior on mHealth adoption, investigate the effect of actual usage behavior of mHealth on mental well-being of the end-users, and investigate the moderating role of self-quarantine on the intention-actual usage of mHealth under the coronavirus disease (COVID-19) pandemic situation. The required primary data were gathered from the end-users of mHealth in Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study has confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions have a positive influence on behavioral intention whereas health consciousness has an impact on both intention and actual usage behavior. mHealth usage behavior has an affirmative and meaningful effect on the mental well-being of the service users. Moreover, self-quarantine has strong influence on actual usage behavior but does not moderate the intention-behavior relationship. In addition, due to the existence of a non-linearity problem in the data set, the Artificial Neural Network (ANN) approach was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). However, this study contributes to the emergent mHealth literature by revealing how the use of the mHealth services elevates the quality of patients' mental well-being under this pandemic situation.

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Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,417
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Neural networks: A comprehensive foundation
Tadeusiewicz, R. (1995) · Control Engineering Practice · doi:10.1016/0967-0661(95)90080-2 · Cited by 7,217
Customer Loyalty: Toward an Integrated Conceptual Framework
Dick, A. S., & Basu, K. (1994) · Journal of the Academy of Marketing Science · doi:10.1177/0092070394222001 · Cited by 7,137
Consumer Perceived Value: The Development of a Multiple Item Scale
Sweeney, J. C., & Soutar, G. N. (2001) · Journal of Retailing · doi:10.1016/s0022-4359(01)00041-0 · Cited by 6,643

Show all 77 references

Research works printed but not linked: 3

Alam, 2014, Factors affecting consumers’ adoption of mobile banking in Bangladesh: an empirical study, TNI J. Bus. Administ. Languages, 2, 31
Bhatt, 2020, Importance of digitech adoption for providing efficient healthcare services during COVID-19, Int. J. Emerg. Technol., 11, 1
H. Hong, Scale development for measuring health consciousness: Re-conceptualization, in: Paper presented at the 12th Annual International Public Relations Research Conference: Research that Matters to the Practice, Coral Gables, Florida, 2009.

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Authors: Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K.
Affiliations: United International University (BD)
Sultan Qaboos University (OM)
Corresponding author: Alam, Mirza Mohammad Didarul
ORCID: Alam, Mirza Mohammad Didarul
Alam, Mohammad Zahedul
Rahman, Syed Abidur
Taghizadeh, Seyedeh Khadijeh
Venue: Journal of Biomedical Informatics
Publisher: Elsevier BV
Published: 2021-04
Volume: 116
Pages: 103722
ISSN: 1532-0464 (ISSN-L)
DOI: 10.1016/j.jbi.2021.103722
PMC: PMC8970651
OpenAlex: W3093020079
Cited by: 144
References: 77
Citations by year: 2026: 16 · 2025: 26 · 2024: 37 · 2023: 29 · 2022: 27 · 2021: 9
FWCI: 28.65
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Journal of Biomedical Informatics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords: mHealth adoption, psychological well-being, COVID-19 pandemic, self-quarantine, UTAUT2, behavioral intention, health consciousness, performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions, moderating effect
MeSH terms: Latent Class Analysis, COVID-19, SARS-CoV-2, Adolescent, Adult, Bangladesh, Female, Humans, Male, Medical Informatics, Mental Health, Middle Aged, Patient Acceptance of Health Care, Quarantine, Surveys and Questionnaires, Health Behavior, Neural Networks, Computer, Telemedicine, Intention, Cell Phone, Young Adult, Pandemics, Mobile Applications

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Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach

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