Non-Teaching Staff Performance Analysis Using Multi-Criteria Group Decision Making Approach

Abstract

The major assets of an education sector are manpower, money, machine and time. The manpower, while serving as the major driver of the other assets have three components, namely academic staff, administrative staff and non-teaching staff. It is proven that the poor management of the non-teaching staff often leads to the poor procurement and management of other assets. The rapid expansion of higher education has considerable impact on the quality of education. One of the most important issues impacting it is “nonteaching staff quality”. The quality of education is primarily determined by the competences, skills, abilities along with motivation of non-teaching staff. Quality assessment refers to the examination and evaluation of employee’s work via predetermined standards and procedures to assess his committee men to the post, the individual capability and performance [1].Various methods of appraisal have been used based on the management practices and the theoretical categorization of them. The most commonly applied methods are Balanced Scorecard (BSC), 360 Degree Appraisal, Management by Objectives (MBO) and Key Performance Indicators (KPI) [2].Currently, quality analyzes majority of the universities still consist of the conventional annual work report and the assessment after a tenure is completed, which is generally a quantifiable description of an academic staff’s teaching and research work. The deficiency of such a method lies in a lack of overall assessment of the staff’s performance; therefore it is one of the great significance to design a new mathematical model to analyze the performance of a non-teaching staff based on the existing information system while giving full consideration of several Experts’ opinion. The system is characterized by the scientifically designed criteria using multi-criteria decision making to analyze the performance of a non-teaching staff with the help of eight methodologies containing AHP, COPRAS, SAW, TOPSIS, Fuzzy TOPSIS, PROMETHEE-2, Compromise Programming and Weighted Average, comparing the results and finally determines the rank using group decision making method.