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Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review

Author

Listed:
  • Nosratabadi Saeed

    (Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary)

  • Zahed Roya Khayer

    (Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran)

  • Ponkratov Vadim Vitalievich

    (Department of Public Finance, Financial University under the Government of the Russian Federation, Moscow, Russian Federation)

  • Kostyrin Evgeniy Vyacheslavovich

    (Department of Finances, Bauman Moscow State Technical University, Moscow, Russian Federation)

Abstract

Background and purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL management. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AI algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL management problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.

Suggested Citation

  • Nosratabadi Saeed & Zahed Roya Khayer & Ponkratov Vadim Vitalievich & Kostyrin Evgeniy Vyacheslavovich, 2022. "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Organizacija, Sciendo, vol. 55(3), pages 181-198, August.
    • Handle: RePEc:vrs:organi:v:55:y:2022:i:3:p:181-198:n:1
    DOI: 10.2478/orga-2022-0012
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    References listed on IDEAS

    as
    1. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Agriculture, MDPI, vol. 11(5), pages 1-13, May.
    2. Olan, Femi & Ogiemwonyi Arakpogun, Emmanuel & Suklan, Jana & Nakpodia, Franklin & Damij, Nadja & Jayawickrama, Uchitha, 2022. "Artificial intelligence and knowledge sharing: Contributing factors to organizational performance," Journal of Business Research, Elsevier, vol. 145(C), pages 605-615.
    3. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    4. Saeed Nosratabadi & Sina Ardabili & Zoltan Lakner & Csaba Mako & Amir Mosavi, 2021. "Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS," Papers 2104.14286, arXiv.org.
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