| Summary: | Every organization is concerned about the work performance of its employees as their performances lead to the organization's promising future. The selection process is to choose from a pool of job applicants, those who have appropriate qualifications, knowledge, skills, and abilities to perform well on the job. The human resource selection (HRS) is based on multi-criteria and a group of panel experts (decision makers, DMs). Using the traditional method, it is a complex issue for DMs to evaluate and shortlist applications that satisfy the vacant position requirements. Apart from that, it is a hectic and time-consuming to aggregate each panel expert intuitions on interviewees at the end of the interview sessions. This study aims to model the HRS based on the multi-criteria decision making methods by considering intuitions of the experts while performing their judgments. Fuzzy Set Theory, classical TOPSIS (Technique for Order Preferences by Similarity to Ideal Solution) and FTOPSIS (Fuzzy Technique for Order Preferences by Similarity to Ideal Solution) are adopted. At the beginning of the study, the problem domain is obtained by interviewing the HRS experts from University Sultan Zainal Abidin (UniSZA) and Universiti Malaysia Terengganu (UMT). Then, a framework is constructed that includes four phases; Phase 1 is the initialization of job positions, evaluation criteria and their weight. In Phase 2, ranking the shortlisted applicants is performed using TOPSIS. On-line test is performed during Phase 3 based on the selected number of shortlisted applicants. Fuzzy Set Theory and FTOPSIS are applied in Phase 4 to facilitate the evaluation of applicant during the interview process. A prototype system is developed to evaluate the applicability and usability of the framework. The testing of the prototype system is done empirically and based on the users' assessment. The system rating is accomplished by 110 respondents; 90 potential job seekers, and 20 panel experts who have experienced with the HRS. The respondents are required to test the system and answer the specific questionnaires. The results show that, in terms of empirical evaluation, the prototype system is applicable to automatically shortlisting the applicants, and integrating the individual panel evaluation during the interview process. As for the system usability, 92.2% of the respondents are satisfied with user interface design, and 80% agree that the system is user-friendly. The experts agree that the use of linguistic variables instead of fuzzy numbers is very convenient. 85% of the experts recommend the integrated TOPSIS based models to be used in Decision Support System for HRS. The system is suitable to be adopted as a recruitment mechanism to solve the problem of multi-criteria evaluation, data uncertainty and multi-expert judgement to ensure sustained effective workforce in the organization.
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