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

Saeed Nosratabadi · Roya Khayer Zahed · Vadim Vitalievich Ponkratov · Evgeniy Vyacheslavovich Kostyrin

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Abstract

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.

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References

2010Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement 2021The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis 2022Artificial intelligence and knowledge sharing: Contributing factors to organizational performance 2001A Taxonomy of Business Process Modeling and Information Systems Modeling Techniques 2020Predicting Employee Attrition Using Machine Learning Techniques 2020Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods 2018Employee Turnover Prediction with Machine Learning: A Reliable Approach 2020Explaining and predicting employees’ attrition: a machine learning approach

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