Determinants of mHealth success: An empirical investigation of the user perspective
Open accessAbstract
Mobile health (mHealth) applications have become an important tool to support public health, especially in times of increased health awareness in the midst of the COVID-19 pandemic. However, there is still uncertainty about what factors determine successful mHealth services from the users' perspective. Based on the results of a systematic literature review, a qualitative content analysis of available apps and semi-structured user and expert interviews, we derive a structural model with antecedents on user attitudes towards mHealth and user satisfaction with the mHealth application. These variables determine users' intention to continue using the application and their intention to recommend it to others. For verification, we tested the model with a sample of 249 German mHealth users from the "MyFitnessPal" community using structural equation modelling and found that all derived path relations have significant coefficients.
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- Authors
- Birkmeyer, S., Wirtz, B. W., & Langer, P. F.
- Affiliations
- German University of Administrative Sciences (DE)
- Corresponding author
- Wirtz, Bernd W.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 59
- Pages
- 102351
- ISSN
- 0268-4012 (ISSN-L)
- PMC
- PMC9756086
- OpenAlex
- W3156218361
- Cited by
- 115
- References
- 117
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 28 · 2023: 27 · 2022: 22 · 2021: 2 · 2017: 1
- FWCI
- 22.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- International Journal of Information Management (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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
- mobile health, user attitudes, user satisfaction, continuance intention, recommendation intention, MyFitnessPal, health apps, COVID-19 pandemic
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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