PREDICTING OVERSEAS TALENT JOB RETENTION FOR A SMALL-MEDIUM ENTERPRISE: UTILIZING BACK PROPAGATION NEURAL NETWORK MODEL

To avoid the inaccuracy and cognitive bias of the existing employee retention predictive model, a robust database with sufficient mining techniques has attracted great attention in the field of human resources management, especially in optimizing the recruitment decisions of small and medium enterprises (SMEs). However, choosing optimum overseas talent that could maximize the profit while simultaneously minimizing trial and error costs is still a challenging task. Most studies are dedicated to examining “from inside out” recruitment practices among potential foreign employees of largesize or multinational enterprises employers. However, the present research examines the impact of the “from outside in” viewpoint held by an SME on the retention of a newly employed, basic-level management overseas workforce. The proposed back propagation neural networks prediction model helped the firm foresee the job retention willingness by 100% for new overseas employees, while precisely measuring the weakness points of the firm for the working environment improvement task.