ERP Staff versus AI recruitment with employment real-time big data

Abstract

Abstract The purpose of this study was to evaluate the effectiveness of using natural language processing (NLP) artificial intelligence (AI) in enterprise resources planning (ERP) to identify specialized job candidates in real-time big data—globally across the internet. The central problem was that companies using traditional methods for recruiting remote specialists were missing good candidates because the skilled employees may not be looking for a job yet they may be receptive to an offer. The auxiliary problem was too much data on the internet for human resources management (HRM) staff to make sense of to find the best-fitting candidate. Thus, the research question was: could NLP AI identify good candidates for ERP remote specialist jobs using internet real-time big data? Job criteria were developed using machine learning to identify key skills from existing staff in a case study company. The skills were transformed into ERP remote specialists hiring criteria. The NLP AI software was activated to find the best candidate. The HRM staff at the case study company evaluated the effectiveness of the candidate selected by the NLP AI. The case study company set 70% as the acceptable mean evaluation score. ANOVA was used to determine if HRM staff agreed about their evaluation scores. A Z-test was used to determine if the NLP AI was faster than the mean time needed for HRM to select ERP candidates. The results were that the NLP AI outperformed the humans by a factor of almost 8 h. All HRM staff agreed that the NLP AI was effective in selecting a candidate to match the hiring criteria. The proposed approach might facilitate the research and development of big data, data analytics, NLP AI, and HRM process improvement.

Keywords

• Human resource management (HRM)
• Big data
• Natural language processing (NLP)
• Artificial intelligence (AI)
• Machine learning (ML)
• Z-Test