A Weighted Bonferroni-OWA Operator Based Cumulative Belief Degree Approach to Personnel Selection Based on Automated Video Interview Assessment Data

Umut Asan*, Ayberk Soyer

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Özet

Asynchronous Video Interviewing (AVI) is considered one of the most recent and promising innovations in the recruitment process. Using AVI in combination with AI-based technologies enables recruiters/employers to automate many of the tasks that are typically required for screening, assessing, and selecting candidates. In fact, the automated assessment and selection process is a complex and uncertain problem involving highly subjective, multiple interrelated criteria. In order to address these issues, an effective and practical approach is proposed that is able to transform, weight, combine, and rank automated AVI assessments obtained through AI technologies and machine learning. The suggested approach combines Cumulative Belief Structures with the Weighted Bonferroni-OWA operator, which allows (i) aggregating assessment scores obtained in different forms and scales; (ii) incorporating interrelationships between criteria into the analysis (iii) considering accuracies of the learning algorithms as weights of criteria; and (iv) weighting criteria objectively. The proposed approach ensures a completely data-driven and efficient approach to the personnel selection process. To justify the effectiveness and applicability of the suggested approach, an example case is presented in which the new approach is compared to classical MCDM techniques.

Orijinal dilİngilizce
Makale numarası1582
DergiMathematics
Hacim10
Basın numarası9
DOI'lar
Yayın durumuYayınlandı - 1 May 2022

Bibliyografik not

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Finansman

Funding: This research was funded by The Scientific and Technological Research Council of Turkey grant number 3150857.

FinansörlerFinansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu3150857

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