Smartphone use in pharmacy education in Uganda: A cross-sectional pilot study.
DOI:
https://doi.org/10.51168/sjhrafrica.v7i3.2542Keywords:
Technology Acceptance Model, mobile learning, smartphone use, pharmacy education, Uganda, health sciences, pilot studyAbstract
Background
Smartphones are increasingly used in health education. Understanding student acceptance is important for integrating mobile learning into pharmacy training. The Technology Acceptance Model (TAM) provides a framework for examining perceived ease of use, usefulness, attitude, and behavioral intention.
Method
A cross-sectional pilot study was conducted at Fort Portal College of Health Sciences in Uganda. Forty-seven pharmacy students completed a structured TAM-based questionnaire via Google Forms. Data were analyzed using SPSS version 23. Reliability was assessed with Cronbach’s alpha; descriptive statistics summarized perceptions; and Pearson correlations and linear regressions examined relationships among TAM constructs. Bootstrapping with 1,000 resamples generated bias-corrected confidence intervals (BCa CI).
Results
Reliability analysis produced an overall Cronbach’s alpha of .91. Students reported positive perceptions of smartphone use, with mean scores above the midpoint across constructs. TAM variables were positively correlated. Regression analyses showed that perceived ease of use predicted usefulness (β = 0.36), usefulness predicted attitude (β = 0.85), and attitude predicted behavioral intention (β = 0.54). When entered simultaneously, usefulness and attitude did not uniquely predict behavioral intention, likely reflecting construct overlap in this small sample. These findings support the feasibility of applying TAM in larger studies of mobile learning in Ugandan pharmacy education.
Conclusion
TAM was applicable for examining smartphone acceptance in Ugandan pharmacy education. Positive perceptions and reliable measurement suggest feasibility for larger studies.
Recommendations
Future studies should increase the sample size, include multiple cohorts, and apply structural equation modeling to clarify overlapping effects.
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