Enhanced network slack-based measure data envelopment analysis model with mixed integer values and non-controllable factors for measurement of Public University effectiveness

The measurement of effectiveness in higher education institutions has become increasingly critical in today’s challenging environment. Public universities face a range of performance related challenges driven by evolving global trends such as student enrolment, graduate employability, and the quality of research output. These factors, which are often non-controllable and integer-valued, coexist with conventional continuous variables, thereby complicating the performance measurement process. Traditional Data Envelopment Analysis (DEA) models are limited in capturing such complexities, as they focus on efficiency while overlooking effectiveness and treating universities as black boxes, ignoring internal structures and interrelated functions. Public universities operate through multiple interrelated functions, with faculties allocating resources based on their strategic priorities. Therefore, a comprehensive assessment approach is required to evaluate effectiveness within a unified framework. This study developed an enhanced parallel network slack-based measure DEA model that incorporates mixed-integer values and non-controllable factors to measure university effectiveness. The model began by forming a parallel structure representing the university’s core functions. It was then tested to measure the effectiveness of 26 faculties in a Malaysian public university. The results were subsequently examined and validated through sensitivity analysis by varying model specifications and input– output configurations, enabling the identification of a stable and reliable model for measuring faculty effectiveness. Findings show that 18 faculties were operating effectively, while the remaining faculties were ineffective. The developed network model enables the identification and measurement of the effectiveness of each faculty’s internal functions, thereby allowing for function‑specific performance analysis. This structure provides deeper insights into the sources of ineffectiveness and supports more informed, evidence‑based decision‑making at the faculty level. This study has successfully developed effectiveness measurement model for a public university and proposes necessary actions to improve performance