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VOL. 12, ISSUE 3 (2026)
Artificial Intelligence–driven human resource management and employee performance: Evidence from Nigerian deposit money banks
Authors
Dr. Olachi Chuks Ronnie, Dr. Igatta Thomas Ogbonna, Dr. Onodugo Chinwe Felicia, Dr. Igatta Amaka Evangeline
Abstract
This study examined the effect of artificial intelligence (AI)-driven human
resource management practices on employee performance in Nigerian deposit money
banks, with particular emphasis on AI-driven recruitment and selection,
training and development, performance management, and compensation and reward
management. A quantitative cross-sectional survey design was adopted. Data were
obtained from 765 respondents using a structured questionnaire measured on a
five-point Likert scale. Descriptive statistics, Cronbach's alpha reliability
analysis, Pearson product-moment correlation and multiple regression analysis
were employed in analysing the data. The descriptive findings revealed
relatively high adoption of AI-driven HRM practices, with AI-driven training
and development recording the highest mean (M = 3.89, SD = 0.70), followed by
recruitment and selection (M = 3.82, SD = 0.74), performance management (M =
3.74, SD = 0.76), and compensation and reward management (M = 3.56, SD = 0.81).
Employee performance was also rated highly (M = 4.00, SD = 0.67). Reliability
coefficients ranged from 0.82 to 0.89, indicating satisfactory internal
consistency. Correlation analysis established positive relationships between
all four AI-HRM dimensions and employee performance, with AI-driven training
and development exhibiting the strongest bivariate association (r = .62).
Multiple regression analysis showed that the AI-HRM dimensions jointly
explained 53.4% of the variance in employee performance (R² = .534; Adjusted R²
= .532), with the overall model statistically significant, F (4,760) = 220.4, p
< .001. AI-driven training and development emerged as the strongest
predictor (β = .282), followed by performance management (β = .248),
recruitment and selection (β = .164), and compensation and rewards (β = .110).
The study concludes that effective integration of AI into HRM can significantly
enhance employee performance and recommends increased investment in AI-enabled
employee development, performance analytics, recruitment systems and
transparent reward-management technologies in Nigerian deposit money banks.
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Pages:234-239
How to cite this article:
Dr. Olachi Chuks Ronnie, Dr. Igatta Thomas Ogbonna, Dr. Onodugo Chinwe Felicia, Dr. Igatta Amaka Evangeline "Artificial Intelligence–driven human resource management and employee performance: Evidence from Nigerian deposit money banks". International Journal of Commerce and Management Research, Vol 12, Issue 3, 2026, Pages 234-239
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