Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency

• URL: http://arxiv.org/abs/2409.14197v1
• Date: Sat, 21 Sep 2024 16:58:23 GMT
• Title: Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency
• Authors: Rakshitha Jayashankar, Mahesh Balan,
• Abstract summary: This study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance. Synthetic data provides a detailed and accurate picture of employee activities. We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Success in todays data-driven corporate climate requires a deep understanding of employee behavior. Companies aim to improve employee satisfaction, boost output, and optimize workflow. This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics. Synthetic data provides a detailed and accurate picture of employee activities while protecting individual privacy thanks to cutting-edge methods like agent-based models (ABMs), Generative Adversarial Networks (GANs), and statistical models. Through the creation of multiple situations, this method offers insightful viewpoints regarding increasing teamwork, improving adaptability, and accelerating overall productivity. We examine how synthetic data has evolved from a specialized field to an essential resource for researching employee behavior and enhancing management efficiency. Keywords: Agent-Based Model, Generative Adversarial Network, workflow optimization, organizational success

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