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

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.

Implementing ABC as Cost Management Model for the Human Resources Department: Evidence From a Romanian Entity

“…Recruitment could be successful if it responds to challenges through flexibility, partnerships, experience, adequate time, innovation and, especially, knowing how to determine its costs because it is a costly process. Many studies were made to determine the costs of the recruitment process, but they were determined only those linked to the advertising recruitment costs (AlHeresh et al, 2021; Nkimbeng et al, 2020; Pinto & Dunsiger, 2021; Shet & Nair, 2022), not all the material costs and personnel costs and salaries which are the largest from the administrative HR costs, or it was analyzed the reduction of recruitment costs using online recruitment (Mazurenko et al, 2022; Sherman et al, 2022; Smith et al, 2022). Thus, such a study, where authors determine all the costs implied in having a successful recruitment, selection, employment and integration process, based on modeling and simulation, may indicate for professionals and experts a possible way to improve these processes, starting from the costs.…”

Machine learning to enhance employee recruitment

“…In light of the above, Shet and Nair (2023) analyze HR data from global technology innovator IBM. The aim is to utilize P-E fit to determine which quality of hire elements optimize the employee selection process.…”

Fusing talent horizons: the transformative role of data integration in modern talent management

“…Turning our attention to machine learning and predictive analytics, these technological advances are revolutionizing traditional HR practices [66]. The strength of machine learning resides in its capability to dissect vast datasets, unveiling patterns crucial for predicting employee behavior and enhancing talent management strategies [67,68]. In this vein, Shet and Nair [68] underscore the prowess of clustering algorithms in assessing the person-environment fit, thus optimizing candidate selection processes.…”

“…The strength of machine learning resides in its capability to dissect vast datasets, unveiling patterns crucial for predicting employee behavior and enhancing talent management strategies [67,68]. In this vein, Shet and Nair [68] underscore the prowess of clustering algorithms in assessing the person-environment fit, thus optimizing candidate selection processes. The incorporation of natural language processing (NLP) into HR analytics offers a novel approach, particularly in personality assessment, reducing inherent biases in conventional methods [67].…”