Cogent Social Sciences (Dec 2024)

Evaluating the predictors of mobile health acceptance among Zimbabwean university students during the COVID-19 era: an integrated framework

  • Phillip Dangaiso,
  • Divaries Cosmas Jaravaza,
  • Paul Mukucha

DOI
https://doi.org/10.1080/23311886.2023.2299141
Journal volume & issue
Vol. 10, no. 1

Abstract

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AbstractThe Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or popularly the Corona Virus Disease 2019 (COVID-19) pandemic brought significant public health challenges globally. During the crisis, mobile health (m-health) services were implemented to expedite fast, convenient and reliable dissemination of health information to the public. Meanwhile, the adoption of m-health faced high uncertainty alike other COVID-19 regulatory methods. This study examines the antecedents of m-health acceptance among Zimbabwean university students. Whilst research on m-health adoption during the pandemic is still scarce, this study becomes the first to integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behavior (TPB) to predict m-health adoption in a developing country. An explanatory design and a quantitative research approach were employed. Based on 271 valid responses, Structural Equation Modelling was used to estimate the proposed model. The findings revealed the positive effect of performance expectancy, effort expectancy, facilitating conditions, attitudes, behavioral norms, perceived behavioral control on behavioral intention to adopt m-health with a higher R square value (80.4%) than the original UTAUT model. The study also confirmed the positive influence of behavioral intention on m-health usage (R square = 41.5%). Health promoters were urged to design messages that stimulate positive attitudes, appeal to social groupings and improve user’s self-efficacy. M-health services should also foster use simplicity, ease of use and health value to entice m-health adoption among consumers. The study also provides an important baseline for the integration of behavioral theoretical models to enhance their predictive strength.

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