Determinants of technology adoption among healthcare professionals at Mogadishu hospitals using an extended UTAUT model
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Omar Osman Haji Abdi1,2, Mohamed Jama Mohamed1,3, Mohamed Ali Osman3
1Graduate School, SIMAD University, Mogadishu, Somalia.
Abstract:
The study used the expanded Unified Theory of Acceptance and usage of Technology (UTAUT) to identify the variables influencing the usage of technology by medical staff in hospitals in Mogadishu. This study aimed to identify the key factors influencing technology usage among hospital staff in Mogadishu, Somalia, using an expanded Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative research design was employed, based on survey data collected from 470 hospital staff members across selected hospitals in Mogadishu. Structural equation modeling (SEM) was used to examine the relationships between variables and assess the validity of the measurement model. The findings show convergent validity and good indicator reliability across the majority of components, with outer loadings ranging from 0.742 to 0.897. Overall construct reliability and convergent validity were excellent, supporting its preservation, despite age and technological innovation did not significantly moderate the relationships between UTAUT factors and perceived usefulness, suggesting that these effects are consistent across different user groups. The results also showed the p values of the UTAUT factors are less than 0.05 which means that they significantly enhance perceived usefulness, but Age and technological innovation, had no statistically significant moderating effects on the associations between UTAUT predictors and perceived usefulness, suggesting that the effects were consistent across user groups. The study offers empirical insights for the use of technology in hospital settings and helps validate an expanded UTAUT paradigm in healthcare. Future studies should include other moderating factors, incorporate longitudinal data, and adjust technological innovation criteria to improve model robustness.
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