Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach
Open accessAbstract
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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Hedonic motivation has been found to have a significant influence on user trust and behavioral intention in various contexts, including mobile payments, smartphones, e-health applications, e-banking, and e-commerce platforms.
“Hedonic motivation, which refers to the pleasure or enjoyment derived from using technology, has been found to have a significant influence on user trust and behavioral intention in various contexts, including mobile payments, smartphones, e-health applications, e-banking, and e-commerce platforms [1,33,39,49-53].”
From Unraveling the Determinants of Platform Economy Adoption in Developing Countries: An Extended Application of the UTAUT2 Model with a Privacy Calculus Perspective (2023) · cites this paper as [50]
The Structural model reports a significant association between hedonic motivation and behavioral intention to use mHealth services (β = 0.164, p < 0.01), and the Discussion reiterates that finding. The paper does not establish an effect on user trust or report this finding across the listed mobile-payment, smartphone, e-banking, and e-commerce contexts. Page numbers were not supplied for these passages. (LLM, conf=0.88)
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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
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 116
- Pages
- 103722
- ISSN
- 1532-0464 (ISSN-L)
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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