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Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments

Author

Listed:
  • Mehreen Azam

    (Department of Information Management, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Salman Bin Naeem

    (Department of Information Management, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Maged N. Kamel Boulos

    (School of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal)

  • Anthony Faiola

    (Department of Health and Clinical Sciences, College of Health Sciences, University of Kentucky, Lexington, KY 40506, USA)

Abstract

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (β = 0.504, CR = 5.064, p < 0.05) and self-concept (β = 0.860, CR = 5.968, p < 0.05) about mHealth technologies. The findings of the structural equation model (SEM) showed that the model is acceptable (χ 2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people’s access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion.

Suggested Citation

  • Mehreen Azam & Salman Bin Naeem & Maged N. Kamel Boulos & Anthony Faiola, 2023. "Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments," IJERPH, MDPI, vol. 20(23), pages 1-17, November.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:23:p:7112-:d:1288216
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    References listed on IDEAS

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    2. Kevin Zhu & Shutao Dong & Sean Xin Xu & Kenneth L Kraemer, 2006. "Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies," European Journal of Information Systems, Taylor & Francis Journals, vol. 15(6), pages 601-616, December.
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    4. Xiu-Fu Tian & Run-Ze Wu, 2022. "Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT," IJERPH, MDPI, vol. 19(16), pages 1-19, August.
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