Impact of Artificial Intelligence on Workforce Management of Nigeria National Petroleum Corporation (NNPC), Rivers State

Read full text
Authors: Peggy Nwamaka Iheagwam, O. Eze Professor Fred, Chigozie Mbah Paulinus
Cited by: 0

Abstract

The study evaluated the Impact of Artificial Intelligence on workforce management of Nigeria National Petroleum Corporation (NNPC). The specific objectives are to: Evaluate the impact of machine learning on output and Examine the impact of Natural language processing (NLP) on tracking work hours of Nigeria National Petroleum Corporation (NNPC). The area of the study was Nigeria National Petroleum Corporation (NNPC)Rivers state. The study used the descriptive survey design approach. The primary source of data was the administration of questionnaire. A total population of 5042staff was used. The adequate sample size of 357, using Freund and William's statistic formula at 5 percent margin of error was used. 281 staff returned the questionnaire and accurately filled. Data was presented and analyzed using Likert Scale and the hypotheses was tested using Z - test. The findings indicated Machine learning had significant positive impact on output. Z = 10.738, P. = 0.05 and Natural language processing (NLP) had significant positive impact on Tracking work hours of Nigeria National Petroleum Corporation (NNPC). Z = 11.737, P. = 0.05. The study concluded that Machine learning and Natural language processing (NLP) had significant positive impact on output and Tracking work hours of Nigeria National Petroleum Corporation (NNPC). The study recommended among others that Nigeria National Petroleum Corporation (NNPC) should invest in the adoption and integration of machine learning tools to enhance data-driven decision-making, predictive maintenance, and workforce performance analytics

Fields of Study & Subjects

AI and HR Technologies Impact of AI and Big Data on Business and Society Artificial Intelligence Applications

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
12 February 2026
Language
EN