Job Candidates Filtering using Fuzzy Multiple Criteria Method for Unskilled Occupations
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UNIVERSITI MALAYSIA SARAWAK
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An e-recruitment system is the preferred choice for employers to recruit new workers. The challenges of any recruitment exercise are to specify the job-related and non-job-related requirements and employ those criteria for accurate filtering, ranking and selecting job applicants. In e-recruitment systems, one of the technical challenges is employing the specified job requirements to automate the candidate selection process. Using crisp rules to filter job candidates is too rigid and may miss good candidates. In this study, a framework which employs personal traits coupled with fuzzy logic method to rank job candidates for unskilled job positions in six different job sectors (construction, hotel, manufacturing, restaurant, sales and service) was proposed. The recruitment of unskilled workers is focused due to the high-turnover rate besides hardly-specified recruitment criteria. Furthermore, different employers might also have different sets of criteria in their selections. The contributions in this study are: (a) proposed the use of Kansei engineering approach to identify criteria that are most relevant to the selections of unskilled workers in different job sectors; (b) employed a fuzzy criteria framework that uses the job criteria identified to rank job candidates; (c) developed a web-based system that implements the proposed fuzzy multi-criteria ranking of job candidates. Searching from different sources has identified 50 useful personality traits to be formulated as Kansei words for candidate selections. Sixty (60) supervisors from six different job sectors have been interviewed and 20 traits which are most relevant to unskilled job have been selected. The F-TOPSIS method which implements the fuzzy multiple criteria decision-making using the 20 Kansei words to rank job candidates has also been employed. The results of the proposed system are compared with the ranking results of five experts from a company and the results obtained from the hamming distance v method. The results show that F-TOPSIS is a suitable solution to recommend and rank unskilled job seekers with high inter-raters correlation. In sum, this study proposes a complete framework of using Kansei engineering and F-TOPSIS method to build a recruitment selection system. The results show several key points: (a) Kansei engineering method is effective for the identification and selection of criteria for unskilled workers; (b) F-TOPSIS job candidates ranking has a high correlation with the ranking made by human experts; (c) fuzzy multiple-criteria approach offers a flexible way for recruiters to select and evaluate weightage for different criteria of a job advertised.