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

Creative Commons License

Asan U., Soyer A.

MATHEMATICS, cilt.10, ss.1-33, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.3390/math10091582
  • Dergi Adı: MATHEMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-33
  • Anahtar Kelimeler: asynchronous video interviewing, personnel selection, multi-criteria decision making, cumulative belief structures, Bonferroni mean, ordered weighted averaging operator, machine learning, automated assessment, DECISION-MAKING
  • İstanbul Teknik Üniversitesi Adresli: Evet

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.