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Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers

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  • Cristina Lee

    (Graduate School of Business, Kookmin University, Seoul 02707, Republic of Korea)

Abstract

The role of employee behavior in organizations and their interaction with management is crucial in advancing the economic progress of workers. This study examines the impact of employee behavior and management practices on organizational performance and economic progress, using advanced artificial intelligence techniques to explore complex relationships and provide evidence-based strategies for sustainable workforce development. The research analyzes critical aspects such as job satisfaction, motivation, participation, and communication to uncover the underlying mechanisms that contribute to economic development. It recognizes the dynamic relationship between employees and management, confirming the central role of effective leadership, communication, and teamwork in achieving positive results. The study emphasizes that harmonious cooperation between employees and management is necessary to create a favorable work environment that contributes to the economic development of workers. It utilizes an artificial neural network (ANN) to better understand the interdependencies between different parameters and their effects within the framework of this ongoing project. The results contribute to the existing body of knowledge by providing practical implications for organizations seeking to optimize the employee–employer relationship and increase the overall workforce productivity. By understanding the complex dynamics between employee behavior and management practices, organizations can create a supportive environment that maximizes employee potential and contributes to sustainable economic growth. The findings demonstrate an accuracy of over 70%, indicating that enhancing job satisfaction and communication can significantly improve employee participation, productivity, and overall organizational performance.

Suggested Citation

  • Cristina Lee, 2024. "Artificial Neural Networks (ANNs) and Machine Learning (ML) Modeling Employee Behavior with Management Towards the Economic Advancement of Workers," Sustainability, MDPI, vol. 16(21), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9516-:d:1512098
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    References listed on IDEAS

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    1. Neelam Kaushal & Rahul Pratap Singh Kaurav & Brijesh Sivathanu & Neeraj Kaushik, 2023. "Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis," Management Review Quarterly, Springer, vol. 73(2), pages 455-493, June.
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    3. Yangda Gong & Min Zhao & Qin Wang & Zhihan Lv, 2022. "Design and interactive performance of human resource management system based on artificial intelligence," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    4. Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021. "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125653, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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