IDEAS home Printed from https://ideas.repec.org/a/vrs/organi/v55y2022i3p181-198n1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/orga-2022-0012
    Download Restriction: no
    File URL: https://libkey.io/10.2478/orga-2022-0012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeed Nosratabadi & Roya Khayer Zahed & Vadim Vitalievich Ponkratov & Evgeniy Vyacheslavovich Kostyrin, 2022. "Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review," Papers 2209.07335, arXiv.org.
    2. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.
    3. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    4. Ewa Ropelewska & Kadir Sabanci & Muhammet Fatih Aslan, 2021. "Discriminative Power of Geometric Parameters of Different Cultivars of Sour Cherry Pits Determined Using Machine Learning," Agriculture, MDPI, vol. 11(12), pages 1-12, December.
    5. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    6. Al-Emran, Mostafa, 2023. "Beyond technology acceptance: Development and evaluation of technology-environmental, economic, and social sustainability theory," Technology in Society, Elsevier, vol. 75(C).
    7. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
    8. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.
    9. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    10. Han, Wucheng & Li, Xiaoyu & Zhu, Weijie & Lu, Ruoyu & Zu, Xu, 2024. "Knowledge digitization and high-tech firm performance: A moderated mediation model incorporating business model innovation and entrepreneurial orientation," Technology in Society, Elsevier, vol. 77(C).
    11. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).
    12. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    13. Davide La Torre & Danilo Liuzzi & Marco Repetto & Matteo Rocca, 2024. "Enhancing deep learning algorithm accuracy and stability using multicriteria optimization: an application to distributed learning with MNIST digits," Annals of Operations Research, Springer, vol. 339(1), pages 455-475, August.
    14. Palmié, Maximilian & Parida, Vinit & Mader, Anna & Wincent, Joakim, 2023. "Clarifying the scaling concept: A review, definition, and measure of scaling performance and an elaborate agenda for future research," Journal of Business Research, Elsevier, vol. 158(C).
    15. Oliver Hümbelin & Lukas Hobi & Robert Fluder, 2021. "Rich Cities, Poor Countryside? Social Structure of the Poor and Poverty Risks in Urban and Rural Places in an Affluent Country. An Administrative Data based Analysis using Random Forest," University of Bern Social Sciences Working Papers 40, University of Bern, Department of Social Sciences, revised 10 Nov 2021.
    16. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
    17. Saeed Nosratabadi & Nesrine Khazami & Marwa Ben Abdallah & Zoltan Lackner & Shahab S. Band & Amir Mosavi & Csaba Mako, 2020. "Social Capital Contributions to Food Security: A Comprehensive Literature Review," Papers 2012.03606, arXiv.org.
    18. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    19. Ke-Liang Wang & Ting-Ting Sun & Ru-Yu Xu, 2023. "The impact of artificial intelligence on total factor productivity: empirical evidence from China’s manufacturing enterprises," Economic Change and Restructuring, Springer, vol. 56(2), pages 1113-1146, April.
    20. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:organi:v:55:y:2022:i:3:p:181-198:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.