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

Rakshitha Jayashankar, Mahesh Balan

TL;DR

This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics.

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

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

TL;DR

This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performance, flexibility, cooperation, and team dynamics.

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
Paper Structure (13 sections, 12 equations, 5 figures)

This paper contains 13 sections, 12 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Advantages of Synthetic Data in the Field of People Analytics
  3. Privacy and Realism
  4. Scalability and Bias-free Generation
  5. Cost efficiency and versatility
  6. Methodology
  7. Statistical Methods
  8. Multivariate Method
  9. Bootstrapping Method
  10. Copula Method
  11. Agent Based Method
  12. Generative Adversarial Network
  13. Conclusion

Figures (5)

  • Figure 1: Heatmap for Multivariate Method
  • Figure 2: Heatmap for Bootstrapping
  • Figure 3: Heatmap for Copula
  • Figure 4: Trends Graph for Agent-Based Model
  • Figure 5: Pair plot for GAN