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Resource allocation efficiency in Ethiopian hospitals: technology utilization as mediator and staff competency as
1College of Business and Economics, Department of Management, Hawassa University, P. O. Box 05, Hawassa City, Ethiopia. sintayehuassefa58@yahoo.com.
Background:
Efficient resource allocation remains a major challenge for hospitals in low-resource healthcare systems, where increasing service demand often exceeds the availability of financial, technological, and human resources. Understanding how organizational capabilities improve resource allocation efficiency is therefore essential. This study examined the effects of resource availability, process efficiency, and patient-centered care on resource allocation efficiency in Ethiopian hospitals, while investigating the mediating role of technology utilization and the moderating role of staff competency.
Methods:
A quantitative cross-sectional survey was conducted among healthcare professionals working in public and private hospitals in Ethiopia. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess the direct, mediating, and moderating relationships among the study variables. The measurement model was evaluated for reliability and validity, while the structural model was assessed using standardized path coefficients, coefficients of determination (R²), effect sizes (f²), predictive relevance (Q²), and bootstrapping procedures.
Results:
The findings indicated that resource availability, process efficiency, and patient-centered care positively and significantly influenced resource allocation efficiency. Technology utilization partially mediated these relationships, demonstrating that greater adoption of digital technologies enhances the efficient use of hospital resources by improving coordination and reducing operational inefficiencies. Staff competency also significantly moderated the relationship between technology utilization and resource allocation efficiency, indicating that the benefits of technology are strengthened when healthcare professionals possess higher levels of knowledge and skills. Overall, the structural model demonstrated substantial explanatory and predictive power for resource allocation efficiency.
Conclusion:
The findings suggest that improving resource allocation efficiency requires a balanced investment in both technological capabilities and workforce competency. Hospitals should prioritize digital transformation alongside continuous staff training to maximize the effective utilization of limited healthcare resources. This study extends the healthcare management literature by integrating the Resource-Based View, Socio-Technical Systems Theory, and Dynamic Capabilities Theory to explain how organizational resources, technological capability, and human competency jointly improve resource allocation efficiency in hospitals within a resource-constrained setting.
Clinical Trial Number:
Not applicable.
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