Abstract
This paper proposes an approach that combines data envelopment analysis (DEA) with the analytic hierarchy process (AHP) and conjoint analysis, as multi-criteria decision-making methods to evaluate teachers’ performance in higher education. This process of evaluation is complex as it involves consideration of both objective and subjective efficiency assessments. The efficiency evaluation in the presence of multiple different criteria is done by DEA and results heavily depend on their selection, values, and the weights assigned to them. Objective efficiency evaluation is data-driven, while the subjective efficiency relies on values of subjective criteria usually captured throughout the survey. The conjoint analysis helps with the selection and determining the relative importance of such criteria, based on stakeholder preferences, obtained as an evaluation of experimentally designed hypothetical profiles. An efficient experimental design can be either symmetric or asymmetric depending on the structure of criteria covered by the study. Obtained importance might be a guideline for selecting adequate input and output criteria in the DEA model when assessing teachers’ subjective efficiency. Another reason to use conjoint preferences is to set a basis for weight restrictions in DEA and consequently to increase its discrimination power. Finally, the overall teacher’s efficiency is an AHP aggregation of subjective and objective teaching and research efficiency scores. Given the growing competition in the field of education, a higher level of responsibility and commitment is expected, and it is therefore helpful to identify weaknesses so that they can be addressed. Therefore, the evaluation of teachers’ efficiency at the University of Belgrade, Faculty of Organizational Sciences illustrates the usage of the proposed approach. As results, relatively efficient and inefficient teachers were identified, the reasons and aspects of their inefficiency were discovered, and rankings were made.
Highlights
The sector of higher education and development, faced with competitive pressure, carries a great responsibility to increase the efficiency of its activities continuously
data envelopment analysis (DEA) empirically identifies the data-driven frontier of efficiency, which envelopes inefficient decision-making units (DMUs) while efficient DMUs lie on the frontier
In the second case, when the number of criteria is not too high, importance values FIk are used for the weight restrictions to better discriminate between DMUs. This decision leads to the classification of selected criteria into the subsets of m inputs and n outputs depending on their nature, data collection, and DEA model selection
Summary
The sector of higher education and development, faced with competitive pressure, carries a great responsibility to increase the efficiency of its activities continuously. The theoretical foundations and methodological frameworks of conjoint analysis and AHP methods are different, they can be used independently and comparatively in similar or even the same studies [9,10,11,12,13,14,15] Both methods can be used to measure respondents’ preferences as well as to determine the relative importance of attributes, but still the choice of the appropriate one depends on the particular problem and aspects of the research [16]. The proposed approach provides the determination of scores and ranking of relatively efficient and inefficient teachers as well as strong and weak aspects of their teaching and research work.
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