Abstract
Increasing competition in higher education forces universities to take steps to improve the quality of service. Pre-determining crucial factors in the provision of educational services is significant to ensure student satisfaction, and thus to strengthen the bond of students with the university. The perception of students and employers about the quality of the education service offered is a key factor in positioning students, who are future employees, in the labor market. The agility of university services plays a key role in the continuation of education and training activities, especially in challenging situations such as pandemics. At this point, different perceptions about the quality of the service emerge for different universities. In this study, the problem of evaluating the service quality of universities is discussed. The aim is to find out the factors that involve all stakeholders in the evaluation process and prioritize the factors based on stakeholders’ belief in overall service quality. In this context, the traditional dimensions of service quality models were examined, and the model was extended to address comprehensive service quality dimensions for higher educations that are especially to be used in extraordinary processes such as pandemics. Then, the weights of the determined service quality evaluation dimensions were determined by a multi-criteria decision making (MCDM) method, considering the expert opinions. At this point, Pythagorean fuzzy sets were preferred to effectively reflect experts uncertainty that the consulted may experience in the decision-making process. With this study, the first service quality model is introduced in the literature for higher education institutions, supported by a fuzzy MCDM method.
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This work is supported by Karadeniz Technical University Scientific Research Projects Coordination Unit. Project Number: FAY-2022–10123.
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Sahin, A., Murat, M., Imamoglu, G., Buyukozkan, K., Ayyildiz, E. (2023). Exploring the Driven Service Quality Dimensions for Higher Education Based on MCDM Analysis. In: Hemanth, D.J., Yigit, T., Kose, U., Guvenc, U. (eds) 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering. ICAIAME 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-031-31956-3_16
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