Quality of hire: expanding the multi-level fit employee selection using machine learning
International Journal of Organizational Analysis
ISSN: 1934-8835
Article publication date: 16 February 2022
Issue publication date: 7 November 2023
Abstract
Purpose
Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.
Design/methodology/approach
The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.
Findings
The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.
Research limitations/implications
Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.
Originality/value
The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.
Keywords
Citation
Shet, S. and Nair, B. (2023), "Quality of hire: expanding the multi-level fit employee selection using machine learning", International Journal of Organizational Analysis, Vol. 31 No. 6, pp. 2103-2117. https://doi.org/10.1108/IJOA-06-2021-2843
Publisher
:Emerald Publishing Limited
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