Abstract
In today’s business world, human resource management is becoming increasingly important and human resource processes are becoming more complex. Companies are implementing many new practices to increase employee engagement. The common goal of these efforts is to positively affect labor turnover by increasing employee happiness and job satisfaction. However, it is quite difficult to predict the tendency to quit. Since employees do not share their decision to leave with their employers, employers are caught off guard when they learn about the decision to leave. In this context, artificial intelligence technologies offer employers the opportunity to predict employee turnover trends and take measures accordingly. The aim here should be to identify the reasons that trigger turnover and enable them to make improvements in these areas, rather than identifying the employee who will leave. Artificial intelligence algorithms and mathematical modeling allow companies to analyze employee data and learn the underlying causes of employee turnover. In addition, human resources analytics studies include a series of processes from employee recruitment to performance evaluation, from training to turnover management. With artificial intelligence and HRIA applications, these processes are managed more efficiently and effectively. In this way, HRIA helps businesses increase their competitive advantage.
Keywords
Artificial Intelligence Human Resources Human Resource Analytics Employee Engagement Resignation Tendency
References
- Zulla Consulting & Partners, (2017). “Should I stay or should I go - Why your employees have this doubt?” Zulla Consulting & Partners. [Online] April 26. Available at: < https://www. linkedin.com/pulse/should-i-stay-go-why-your-employees-havedoubt- daniele-zulla/ [Accessed February 15, 2024].
- Girmanová L. & Gašparová Z., (2018). “Analysis of Data on Staff Turnover Using Association Rules and Predictive Techniques” Lenka Girmanová, Zuzana Gašparová,.
- Orth, M. & Volmer, J., (2017). “Daily within-person effects of job autonomy and work engagement on innovative behavior: the crosslevel moderating role of creative self- efficacy”, European Journal of Work and Organizational Psychology.
Details
| Primary Language | English |
|---|---|
| Subjects | Adversarial Machine Learning, Machine Learning (Other) |
| Journal Section | 2024 4/2 (October) |
| Authors | |
| Publication Date | October 31, 2024 |
| Submission Date | May 23, 2024 |
| Acceptance Date | June 3, 2024 |
| Published in Issue | Year 2024 Volume: 4 Issue: 2 |
