Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Abstract

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

The dark sides of people analytics: reviewing the perils for organisations and employees

“…Following people analytics' predictions, organisations and managers can take decisions which create conditions that ultimately realise these very predictions (Gal et al, 2017;Rainie & Anderson, 2017). For instance, firms can use people analytics to forecast the future performance, the expected retention, or return on investment in their recently hired employees (Boudreau & Cascio, 2017;Chalutz, 2019;Kellog et al, 2020;Pessach et al, 2020). Then, based on these predictions, companies might allocate training resources only to the employees they perceive as promising, which will result in selected employees receiving additional training, while others receive no training (Cowgill & Tucker, 2020;Gal et al, 2017).…”

An Objective-Based Entropy Approach for Interpretable Decision Tree Models in Support of Human Resource Management: The Case of Absenteeism at Work

“…Our focus on interpretable models is motivated by the superior trust that human beings have in such models, meaning that they tend to be preferred over non-interpretable models [ 22 , 23 , 24 ]. In fact, it has been argued that interpretable models should be favored over non-interpretable models with comparable or even slightly better performance [ 25 , 26 ].…”

Optimal manpower recruitment and promotion policies for the finitely graded systems using dynamic programming

“…To solve problems of these sorts, they applied a modified GP approach to minimize the capacity shortage involving multitasking, overtime cost, and under time preference hierarchies among the goals. On improving operations of recruitment and placement decisions, [32] developed a comprehensive analytics framework-a decision support tool for Human Resource (HR) recruiters. The first stage was an application of Machine Learning (ML) to a large recruitment dataset to determine a local prediction plan for recruitment success.…”