Benchmarking of academic departments using data envelopment analysis (DEA)

Abstract

PurposeThe main objective of the paper is to develop an investment model using data envelopment analysis (DEA) that provides a decision-making framework to allocate resources efficiently, such that the relative efficiency is improved within an available investment budget.Design/methodology/approachFirstly, DEA models are used to evaluate the efficiency of the departments relative to their peers and providing benchmarks for the less efficient departments. Secondly, the inefficiencies in departments are identified. Finally, for the less efficient departments, a decision-support system is introduced for optimizing resource allocation to improve efficiency.FindingsFive of the 18 academic departments were determined to be inefficient, and benchmark departments were found for those departments. The most prevalent causes for inefficiency were the number of undergraduate students per faculty and the number of graduate students. Results from the investment model for department 12 suggest increasing the number of faculty by 2 units and H-Index by 0.5 units, thereby, improving the relative efficiency of the department by 6.8% (88%–94%), using $290,000 out of $500,000 investment budget provided.Originality/valueWhen an investment budget is available, no study has used DEA to develop a decision-support framework for resource allocation in academic departments to maximize relative efficiency.