Performance measure of academic departments using data envelopment analysis

AIP Conference Proceedings

2015

Measuring university performance is essential for efficient allocation and utilization of educational resources. In most of the previous studies, performance measurement in universities emphasized the operational efficiency and resource utilization without investigating the university's ability to fulfill the needs of its stakeholders and society. Therefore, assessment of the performance of university should be separated into two stages namely efficiency and effectiveness. In conventional DEA analysis, a decision making unit (DMU) or in this context, a university is generally treated as a black-box which ignores the operation and interdependence of the internal processes. When this happens, the results obtained would be misleading. Thus, this paper suggest an alternative framework for measuring the overall performance of a university by incorporating both efficiency and effectiveness and applies network DEA model. The network DEA models are recommended because this approach takes into account the interrelationship between the processes of efficiency and effectiveness in the system. This framework also focuses on the university structure which is expanded from the hierarchical to form a series of horizontal relationship between subordinate units by assuming both intermediate unit and its subordinate units can generate output(s). Three conceptual models are proposed to evaluate the performance of a university. An efficiency model is developed at the first stage by using hierarchical network model. It is followed by an effectiveness model which take output(s) from the hierarchical structure at the first stage as a input(s) at the second stage. As a result, a new overall performance model is proposed by combining both efficiency and effectiveness models. Thus, once this overall model is realized and utilized, the university's top management can determine the overall performance of each unit more accurately and systematically. Besides that, the result from the network DEA model can give a superior benchmarking power over the conventional models.

Section: Data Envelopment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for performance measurement in university using extended network data envelopment analysis (DEA) structures

AIP Conference Proceedings

2015

“…Therefore, in order to provide a more meaningful efficiency measures, the internal structure of a system must be taken into account in the evaluation model (Kao, 2015). Most of the researches focused on one function of the university (Johnes & Yu, 2008;Kao & Hung, 2008;Ahmi & Mohamad, 2016;Kasim et al, 2017), or combined all the functions of the university to form a single-stage DEA model (Alwadood et al, 2011;Wan Husain, 2012;Ismail et al, 2014). When these models were applied to a university, the internal activities in each function of the university were ignored.…”

Section: Introductionmentioning

confidence: 99%

Measuring Efficiency of a University Faculty Using a Hierarchical Network Data Envelopment Analysis Model

Journal of Information and Communication Technology

2018

An efficiency measurement model of a university faculty is proposed with additional new sub-functions that produce new output variables, based on the network Data Envelopment Analysis (DEA) model for systems with a hierarchical structure.For production systems composed of hierarchical processes, the system efficiency is well represented as the aggregated performance of the components involved in the system. It is identified that the conventional DEA model ignores internal process activities in a university. Therefore, an improved DEA model based on a network structure that accounts for more activities in a university is proposed to measure its overall efficiency. The impact of major functions of a university are taken into account to represent the output variables in assessing the efficiency. Currently, collaboration activities have been given more attention, so, this variable is suggested as a new output for the hierarchical production system. In order to show the practicality of the model, a hypothetical set of data of 14 faculties has been used as a numerical example. The results show that none of the faculties is relatively efficient since its functions were found to be inefficient. The proposed model enables to help the management of university faculties to identify weaknesses of each function and thus to plan for suitable actions on improving the overall performance of the university.

“…The DEA is a suitable method to be used in assessing the efficiency of an organization that uses multiple inputs and produces multiple output. Previous studies that used the DEA method for measuring the efficiency of universities in Malaysia are Alwadood et al (2011), Wan Husain (2012) and Ismail et al (2014). In addition, Johnes and Yu (2008) have used four DEA models to a sample of top 109 higher education institutions in China to measure efficiency of the institutions in producing research.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring Efficiency of a University Faculty Using a Hierarchical Network Data Envelopment Analysis Model

2018

JICT

An efficiency measurement model of a university faculty is proposed with additional new sub-functions that produce new output variables, based on the network Data Envelopment Analysis (DEA) model for systems with a hierarchical structure. For production systems composed of hierarchical processes, the system efficiency is well represented as the aggregated performance of the components involved in the system. It is identified that the conventional DEA model ignores internal process activities in a university. Therefore, an improved DEA model based on a network structure that accounts for more activities in a university is proposed to measure its overall efficiency. The impact of major functions of a university are taken into account to represent the output variables in assessing the efficiency. Currently, collaboration activities have been given more attention, so, this variable is suggested as a new output for the hierarchical production system. In order to show the practicality of the model, a hypothetical set of data of 14 faculties has been used as a numerical example. The results show that none of the faculties is relatively efficient since its functions were found to be inefficient. The proposed model enables to help