Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions

Nancy Shamaylah, Suleiman Ibrahim Mohammad, Badrea Al Oraini, Jumana Majed Yaseen Al-Gaafreh, Menahi Mosallam Alqahtani, Asokan Vasudevan, Anber Abraheem Shlash Mohammad and Mohammad Faleh Ahmmad Hunitie

Data and Metadata, 2025, vol. 4, 886

Abstract: Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences and optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations and adaptive learning algorithms to improve workforce training. However, the effectiveness and challenges of these approaches in real-world applications require further investigation. Methods: This study employed a descriptive and analytical research design, utilizing both quantitative and qualitative methods. Data was collected from 385 employees in Jordanian public institutions using structured surveys and sentiment analysis of employee feedback. Statistical techniques, including regression analysis, ANOVA, and correlation analysis, were applied to assess the impact of HR data analytics, AI-based recommendations, and training personalization on training effectiveness. Results: The findings indicate that HR data analytics, AI-based recommendations, and training personalization significantly improve training effectiveness. Skill development emerged as the strongest predictor of training success (β = 0.7282, p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:886:id:1056294dm2025886

DOI: 10.56294/dm2025886

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