<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Background and purpose:</bold> 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.
<bold>Methods:</bold> 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.
<bold>Results:</bold> 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.
<bold>Conclusion:</bold> 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.
</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.

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

Hungarian University of Agriculture and Life Sciences

Department of Management, Faculty of Administrative Sciences and Economics

Financial University under the Government of the Russian Federation

Organizacija

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Background and purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL)...

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

Pubblicato online: 23 set 2022

Pagine: 181 - 198

Ricevuto: 15 set 2021

Accettato: 23 lug 2022

DOI: https://doi.org/10.2478/orga-2022-0012

Parole chiave
Artificial intelligence, Deep learning, Machine learning, Human resource management, Employee lifecycle, PRISMA, Systematic literature review

© 2022 Saeed Nosratabadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Pubblicato online: 23 set 2022

Pagine: 181 - 198

Ricevuto: 15 set 2021

Accettato: 23 lug 2022

DOI: https://doi.org/10.2478/orga-2022-0012

Parole chiaveArtificial intelligence, Deep learning, Machine learning, Human resource management, Employee lifecycle, PRISMA, Systematic literature review

© 2022 Saeed Nosratabadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Artificial intelligence, Deep learning, Machine learning, Human resource management, Employee lifecycle, PRISMA, Systematic literature review