Antecedents and moderators of performance impacts of management information systems in Makerere University: a study based on the Task Technology Fit Theory

Abstract

This study examined the antecedents and moderators of performance impact of management information systems (MISs) in Makerere University using Goodhue and Thompson‘s 1995 task–technology fit (TTF) model. Four key systems used in Makerere: academic management information system (ACMIS), integrated financial management information system (IFMIS), human resource management information system (HURIS), and Makerere library information system (MakLIBIS) were evaluated through five constructs: task characteristics (XT), MIS characteristics (XM), task–MIS fit (TMF), utilization (UM), and performance impact (PI). A quantitative approach was adopted, and data were analyzed in SPSS using descriptive statistics and linear regression models to test three hypotheses on the relationships among these constructs. It was established that TMF was the strongest and most significant predictor of PI (β = 0.412, p < 0.001), while UM showed a positive but nonsignificant influence. UM itself was significantly predicted by TMF, and TMF was in turn determined by both task and MIS characteristics, with system characteristics exerting a greater effect. Respondents acknowledged positive MIS contributions but highlighted challenges such as uneven integration across departments, limited user training, and inadequate system flexibility. Theoretically, the study extends the applicability of the TTF model to higher education in the context of a developing country like Uganda. Practically, this study emphasizes the need for MIS design and upgrades to prioritize task–system alignment, enhanced user capacity-building, and institutionalized monitoring of TMF and UM as performance indicators. Notable limitations are the single-institution scope, cross-sectional design, and reliance on self-reported data. Future research should therefore adopt longitudinal and multi-institutional approaches and explore additional predictors such as IT support and organizational culture to broaden insights into MIS effectiveness.