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Developing new data envelopment analysis models to evaluate the efficiency in Ontario Universities

Sarad Ghimire, Saman Hassanzadeh Amin and Leslie J. Wardley

Journal of Informetrics, 2021, vol. 15, issue 3

Abstract: Data Envelopment Analysis (DEA) is a popular operations research technique for determining the relative efficiency of organizations. The main goal of this study is to develop a unique stochastic DEA model to evaluate the efficiency of Ontario universities in Canada based on two inputs (i.e., Expenditures and Number of Academic Staff) and four outputs (i.e., Tri-council Grants, Student's Satisfaction Level, Enrolment (number of students), and Number of Publications). The satisfaction level is a unique parameter that has not previously been applied in this field. This study focuses on the stochastic model because of its ability to handle uncertainty in the parameters of the optimization model. Real data are utilized and applied in this research. Three types of universities are investigated including medical/doctoral universities, comprehensive universities, and primarily undergraduate universities. According to the results, there is some room for improvement of the efficiency in Ontario universities. Furthermore, the results of this study show that the selection of inputs and outputs plays a crucial role in determining the ranks of universities using DEA. The results also are discussed with details for each category. For instance, increasing the number of publications is a prominent factor to rise the efficiency scores of undergraduate universities.

Keywords: Data Envelopment Analysis (DEA); Universities; Canada; Stochastic models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.joi.2021.101172

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