An integrated e‐recruitment system for automated personality mining and applicant ranking

Abstract

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.

Citation

Faliagka, E., Tsakalidis, A. and Tzimas, G. (2012), "An integrated e‐recruitment system for automated personality mining and applicant ranking", Internet Research, Vol. 22 No. 5, pp. 551-568. https://doi.org/10.1108/10662241211271545