Quality of hire: expanding the multi-level fit employee selection using machine learning

Sateesh Shet*, Binesh Nair

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)



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.


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.


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.


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.

Original languageEnglish
Pages (from-to)2103-2117
Number of pages15
JournalInternational Journal of Organizational Analysis
Issue number6
Early online date16 Feb 2022
Publication statusPublished - 7 Nov 2023
Externally publishedYes

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