Influence of Augmented Artificial Intelligence Platforms on Talent Management in Energy Parastatals in Nairobi, Kenya

Abstract

This purpose of the study was to investigate the influence of augmented artificial intelligence platforms (AAIPs) on talent management in Energy Parastatals in Nairobi, Kenya. The study employed a descriptive research design, in which questionnaires were used to collect data from Human Resource (HR) managers from four energy parastatals, in Nairobi, Kenya. The study target population was 246, from which stratified random sampling was used to obtain a sample size of 152 HR managers. The study employed both descriptive and inferential analysis methods to analyze the data using Statistical Packages for Social Scientists version 25. The statistical method was used to determine measures of central tendencies, data distribution, and descriptive statistics. Inferential analysis including correlation and regression analysis was also done. The study revealed a mean of (M=3.72) and a correlation coefficient of (R=0.75). This statistic indicated that, respondents strongly support the use of AAIPS and recognize their significant influence on talent management. The revealed linear regression and ANOVA values indicated that, AAIPs have the significant contribution to talent management as shown by β= 0.762 and (β=0.762, p<0.05) scores respectively. These findings highlight the significance and importance of augmented artificial intelligence platforms in enhancing talent management practices within the energy parastatals in Nairobi Kenya. The findings of this study will immensely contribute to the growing body of literature on the impact of artificial intelligence on talent management, particularly in the context of energy parastatals in Nairobi, Kenya. It is also inferred that augmented artificial intelligence platforms, have a positive linear relationship with talent management. Moreover, the findings have revealed that the deployment of augmented artificial intelligence platforms yield significant influence on talent management.