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Cited by 304AI and HR TechnologiesEmployer Branding and e-HRMOnline Learning and Analytics

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

Open access
Pessach, Dana, Singer, Gonen, Avrahami, Dan, Chalutz Ben-Gal, Hila, Shmueli, Erez, Ben-Gal, Irad·2020·Decision Support Systems
DOI 10.1016/j.dss.2020.113290PMC PMC7252110
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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.

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  • Predictive analytics have enabled people analytics teams to effectively enhance their recruitment practices, becoming more diverse and inclusive when screening and selecting candidates.

    “Regarding gender equality (SDG 5); predictive analytics have enabled people analytics teams to effectively enhance their recruitment practices; becoming more diverse and inclusive when screening and selecting candidates (Pessach et al., 2020).”

    From Reframing people analytics value creation: Balancing organizational performance and common good HRM values (2026) · cites this paper as Pessach et al., 2020

    The Introduction reports that the proposed prediction-and-optimization model improved diversity and recruitment success compared with recruiters’ actual selections. But this was an evaluation of a proposed decision-support approach, not evidence that people analytics teams had adopted it and made their screening and selection practices more inclusive. The cited passages also do not establish a gender-specific outcome. (LLM, conf=0.85)

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Cites

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Research works printed but not linked: 5

  1. [8]Ribes E. Touahri K. Perthame B. Employee turnover prediction and retention policies design: a case study, ArXiv Prepr ArXiv1707 2017 01377
  2. [9]Ribeiro M.T. Singh S. Guestrin C. Model-agnostic interpretability of machine learning, ArXiv Prepr ArXiv1606 2016 05386
  3. [17]Qu X.-L. A decision tree applied to the grass-roots staffs' turnover problem—take CR Group as an example Grey Syst. Intell. Serv. (GSIS), 2015 IEEE Int. Conf 2015 378 382
  4. [26]Li Y.-M. Lai C.-Y. Kao C.-P. Incorporate personality trait with support vector machine to acquire quality matching of personnel recruitment 4th Int. Conf. Bus. Inf 2008 1 11
  5. [71]Steinwart I. Thomann P. {liquidSVM}: A Fast and Versatile SVM Package, ArXiv E-Prints 1702.06899 http://www.isa.uni-stuttgart.de/software 2017 · arXiv

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Authors
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I.
Affiliations
  • Tel Aviv University (IL)
  • Bar-Ilan University (IL)
Corresponding author
Pessach, Dana
ORCID
Venue
Decision Support Systems
Publisher
Elsevier BV
Published
2020-07
Volume
134
Pages
113290
ISSN
0167-9236 (ISSN-L)
DOI
10.1016/j.dss.2020.113290
PMC
PMC7252110
OpenAlex
W3014109009
Cited by
304
References
61
Citations by year
2026: 46 · 2025: 62 · 2024: 74 · 2023: 57 · 2022: 32 · 2021: 26 · 2020: 7
FWCI
32.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Decision Support Systems (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Koret Foundation
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
personnel selection, hiring decisions, model interpretability, prescriptive analytics, recruitment optimization, diversity in hiring

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