Online from: 1991
Subject Area: Information and Knowledge Management
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|Title:||An integrated e-recruitment system for automated personality mining and applicant ranking|
|Author(s):||Evanthia Faliagka, (Computer Engineering and Informatics Department, University of Patras, Patras, Greece), Athanasios Tsakalidis, (Computer Engineering and Informatics Department, University of Patras, Patras, Greece), Giannis Tzimas, (Department of Applied Informatics in Management & Finance, Faculty of Management and Economics, Technological Educational Institute of Messolonghi, Messolonghi, Greece)|
|Citation:||Evanthia Faliagka, Athanasios Tsakalidis, Giannis Tzimas, (2012) "An integrated e-recruitment system for automated personality mining and applicant ranking", Internet Research, Vol. 22 Iss: 5, pp.551 - 568|
|Keywords:||Analytic hierarchy process, Data mining, E-recruitment, Human resource management, Personality, Personality mining, Recommendation systems, Recruitment, Selection, Social networking sites|
|Article type:||Research paper|
|DOI:||10.1108/10662241211271545 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
Purpose – The purpose of this paper is to present a novel approach for recruiting and ranking job applicants in online recruitment systems, with the objective to automate applicant pre-screening. An integrated, company-oriented, e-recruitment system was implemented based on the proposed scheme and its functionality was showcased and evaluated in a real-world recruitment scenario.
Design/methodology/approach – The proposed system implements automated candidate ranking, based on objective criteria that can be extracted from the applicant's LinkedIn profile. What is more, candidate personality traits are automatically extracted from his/her social presence using linguistic analysis. The applicant's rank is derived from individual selection criteria using analytical hierarchy process (AHP), while their relative significance (weight) is controlled by the recruiter.
Findings – The proposed e-recruitment system was deployed in a real-world recruitment scenario, and its output was validated by expert recruiters. It was found that with the exception of senior positions that required domain experience and specific qualifications, automated pre-screening performed consistently compared to human recruiters.
Research limitations/implications – It was found that companies can increase the efficiency of the recruitment process if they integrate an e-recruitment system in their human resources management infrastructure that automates the candidate pre-screening process. Interviewing and background investigation of applicants can then be limited to the top candidates identified from the system.
Originality/value – To the best of the authors’ knowledge, this is the first e-recruitment system that supports automated extraction of candidate personality traits using linguistic analysis and ranks candidates with the AHP.
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