TY  - JOUR
T1  - Investigation of Turnover Tendency Predictions with Artificial Intelligence and Mathematical Models
AU  - Aycan, Burak
AU  - Bal, Mert
PY  - 2024
DA  - October
Y2  - 2024
JF  - Romaya Journal
JO  - Maya/Roma
PB  - Ebru Bağçı
WT  - DergiPark
SN  - 2791-9099
SP  - 232
EP  - 240
VL  - 4
IS  - 2
LA  - en
AB  - 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.
KW  - Artificial Intelligence
KW  - Human Resources
KW  - Human Resource Analytics
KW  - Employee Engagement
KW  - Resignation Tendency
CR  - 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].
CR  - Girmanová L. & Gašparová Z., (2018). “Analysis of Data on Staff Turnover Using Association Rules and Predictive Techniques” Lenka Girmanová, Zuzana Gašparová,.
CR  - 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.
UR  - https://dergipark.org.tr/tr/pub/romaya-journal/issue//1665593
L1  - https://dergipark.org.tr/tr/download/article-file/4725495
ER  -