Identification of Personality Traits for Recruitment of Unskilled Occupations using Kansei Engineering Method

Bong, Chih How and Tan, Jia Kae and Lee, Nung Kion and Ahmad Sofian, Shminan (2017) Identification of Personality Traits for Recruitment of Unskilled Occupations using Kansei Engineering Method. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9 (9). pp. 1-7. ISSN 2289-8131

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Abstract

—Job recruitment portals become the main recruitment channel in most of the organizations nowadays because they offer many advantages to recruiters and job applicants. An outstanding recruitment system should be able to filter and recommend the best potential candidates for a job vacancy so that it can avoid hiring of inappropriate individuals or miss out the good candidates. Nevertheless, most of the existing job portals do not cover the unskilled job sectors. Matching unskilled jobs to applicants is challenging because the selection criteria can be very subjective and difficult to specify in terms of professional qualifications. In this paper, Kansei Engineering (KE) Model is applied to find the most prominent personality traits that are preferred by employers in different unskilled job categories in Malaysia. We have identified most prominent 20 Kansei words related to personality traits that are important to six main industries of unskilled workers. The six unskilled sectors involved are construction, hotel, manufacturing, restaurant, sales, and service. 60 employers from the six sectors were interviewed to rank the 50 personality traits identified. Those ranked personality traits can potentially be used for recruitment selection and filtering of unskilled job applicants.

Item Type: Article
Uncontrolled Keywords: Terms—E-recruitment; Kansei Engineering; Personality Traits; Unskilled Occupation, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Gani
Date Deposited: 23 Jul 2020 07:16
Last Modified: 22 Jun 2021 13:28
URI: http://ir.unimas.my/id/eprint/30693

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