Fuzzy multicriteria model to support decision making during the selection process of teaching and research staff in higher education

Abstract

Selecting human resources represents complex tasks and ultimately determines an organisation's future stability and development. High-quality selection procedures serve as the basis for proper employee utilisation and individual career development, which generate organisational success. To be most effective, in addition to applicant competency in the field, decision makers use various selection techniques and tools. These are applied to reveal and distinguish applicants' characteristics, qualifications and competencies, as well as to predict their success and organisational contribution specified for each profession and position. The main aim of this paper is to develop a fuzzy multicriteria model based on the technique for order preference by similarity to ideal solution (TOPSIS) as support for decision making when teaching and research staff are selected for employment within higher education. The model is based on hierarchically structured selection criteria fortified by the different qualitative and quantitative nature of each criterion, as well as the influence of required experts' competencies. We apply this model in the higher education sector for selecting teaching and research staff in Croatia.

Data-driven Decision Making in Higher Education Institutions: State-of-play

“…Another approach proposed is the use of fuzzy multicriteria decision support models. The study by Tadić et al [89] developed a fuzzy multi-criteria decision support model for research and faculty staff in HEIs' appointments based on the technique for order preference by similarity to the ideal solution (TOPSIS). The model uses hierarchically structured quantitative and qualitative selection criteria and the competencies of the experts.…”