This paper explores the performance efficiency of natural and technical science departments at Austrian universities using Data Envelopment Analysis (DEA). We present DEA as an alternative tool for benchmarking and ranking the assignment of decision-making units (organisations and organisational units). The method applies a multiple input and output variables approach, which is a clear advantage to other approaches using simple performance ratios. To deliver reasonable results, suitable input and output variables have been determined in a previous step using correlation analyses and OLS regression. The results validate the methods applied, and reveal performance differences and scale effects. The use of multiple output variables enables the revealing of detailed improvement or reduction amounts of each input and output of the evaluated units and furthermore for identifying the specialisation of teaching, research, and industrial cooperation. We find significant evidence that the size of a department influences its overall and specialisation performance; both small and large departments perform above average, which proves that simple linear scale effects do not exist.
Purpose -The purpose of this paper is to demonstrate the usefulness of data envelopment analysis (DEA) as a consulting and management tool that fulfils the requirements of quantitatively and comprehensively evaluating and benchmarking the efficiency of intellectual capital (IC). Design/methodology/approach -DEA is applied for a sample of input and output data of all technical and natural science departments of Austrian universities. Correlation and factor analyses are carried out to select appropriate variables of the sample. DEA estimates the production function of the units under evaluation in relation to peer units, which are identified as fully efficient. Findings -Results illustrate the existence of scale efficiencies of Austrian university departments and show a large heterogeneity within and among universities as well as between different fields of study with respect to their efficiency. Research limitations/implications -DEA is mainly appropriate for larger samples inside an organisation or among different organisations. The method can be easily transferred to similar management situations in other types of organisations or industries, where the efficiency of IC should be assessed. Practical implications -The results reveal detailed improvement or reduction amounts of each input and output of the evaluated organisational units and indicate areas for managerial action at Austrian universities. Originality/value -For the first time DEA is applied for evaluating and benchmarking IC of Austrian universities. DEA is proposed as consulting and management tool for evaluation IC performance.
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