Leave Policies and Service Delivery Quality among Non-Academic Staff in a Nigerian Public University: Evidence from Ebonyi State University

Abstract

Leave is commonly treated as an employee entitlement, yet its administrative significance extends to employee recovery, work-life management and the conditions that support sustainable service performance. This study examines the association between leave policies and service delivery quality among non-academic staff of a Nigerian public university. Survey data were obtained from 270 non-academic staff of Ebonyi State University, Abakaliki, and analyzed using descriptive statistics, Spearman rank-order correlation and simple linear regression. The findings indicate that leave policies constitute a meaningful organizational resource within the work-life environment, although their practical value is shaped by the conditions under which employees can access and use them. Inferential analysis revealed a moderate, positive and statistically significant association between leave policies and service delivery quality (ρ = .512, p < .001). The regression analysis further showed a positive standardized coefficient (β = .523), with the model explaining 27.4% of the observed variance in service delivery quality (R² = .274, F(1, 268) = 101.048, p < .001). Among the work-life balance dimensions examined in the broader empirical framework, leave policies demonstrated the strongest statistical association with the corresponding employee performance outcome. The findings suggest that the administrative relevance of leave lies not simply in the existence of formal entitlements, but in their effective implementation through accessible, predictable and supportive arrangements that enable employee recovery while protecting continuity of essential services. The study contributes to public-sector Human Resource Management by linking a specific organizational resource to a specific administrative service outcome in a Nigerian public-university context. Given the cross-sectional design and self-reported measures, the findings are interpreted as statistical associations rather than causal effects.