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Exploring the Impact of AI on Candidate Selection: A Two-Phase Methodological Approach with CRITIC-WASPAS.

Exploring the Impact of AI on Candidate Selection: A Two-Phase Methodological Approach with CRITIC-WASPAS.

Authors :
Contreras Pinochet, Luis Hernan
Lopes, Nathalia Sertorio
Onusic, Luciana Massaro
dos Santos, Marcos
Pardim, Vanessa Itacaramby
Neto Francischini, Andresa Silva
Source :
Procedia Computer Science; 2024, Vol. 242, p920-927, 8p
Publication Year :
2024

Abstract

This study investigates how AI technologies can improve the candidate selection process. It will also use the WASPAS method to analyze the criteria used during the selection stages, aiming to optimize the effectiveness of the process. The article employed a two-phase methodological approach to enhance candidate selection analysis. The CRITIC Method assigned weights to criteria, while the WASPAS Method ranked alternatives based on these weights. A sensitivity analysis, specifically for the WASPAS method, further assessed its robustness and consistency, providing a comprehensive view of the decision-making process. Twelve experienced professionals from various sectors, all decision-makers, participated in the research. As a result of the CRITIC method C5 (31.45%) 'Data Confidentiality' is the most important criteria, followed by C1 (25.92%) 'Justice'. Furthermore, in the selection process using AI with the WASPAS method, statements A14 and A15 stand out in the final stage, automating the identification of the most suitable candidates for the position and carefully evaluating them. Meanwhile, A9, in the cognitive and behavioral testing stage, is crucial for identifying promising candidates based on objective results, ensuring an accurate fit between candidate and position and increasing the efficiency of the selection process. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
242
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
179171558
Full Text :
https://doi.org/10.1016/j.procs.2024.08.274