Application of a maximum classification consensus approach for construction of a group ordinal classification of applicants in employee recruitment

Hengjie Zhang, Wenfeng Zhu, Jing Xiao and Haiming Liang

Journal of the Operational Research Society, 2024, vol. 75, issue 4, 742-765

Abstract: With the intensification of market competition, recruiting appropriate employees has become one of the most critical factors for organisation development. In employee recruitment, it may be sufficient for recruiters to classify applicants into several ordinal classes according to their performances in various aspects, which can be regarded as an ordinal classification-based multi-criteria group decision-making (MCGDM) problem. Particularly, due to the differences in preferences, knowledge and experience, recruiters may provide substantially divergent assessments over applicants. Integrating a consensus-reaching model into employee recruitment process is an efficient way to improve the quality of employee recruitment and avoid individual biases. To this end, this study proposes an ordinal classification consensus-based employee recruitment framework. In the proposed framework, an information violation-based maximum ordinal classification consensus model (IVMOCCM) is presented to obtain the ordinal classification of applicants. Then, an ordinal classification consensus reaching model with minimum adjustments (OCCRMMA) is developed to improve the ordinal classification consensus level among all recruiters, and this is done by minimising the distance between original assessments and reassessments over applicants. Further, an interactive employee recruitment process with ordinal classification consensus is designed. Finally, some theoretical analyses, an application case and a detailed comparative analysis are conducted to demonstrate the validity of the proposal.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2023.2207596 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:75:y:2024:i:4:p:742-765

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2023.2207596

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().