Artificial intelligence-driven human resource practices and employee well-being: Examining the mediating effect of employee engagement
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DOIhttp://dx.doi.org/10.21511/ppm.23(4).2025.18
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Article InfoVolume 23 2025, Issue #4, pp. 247-263
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Type of the article: Research article
Abstract
Artificial intelligence can be strategically integrated into human resource management to enhance employee well-being in the information technology (IT) industry. The paper deals with a quantitative approach using structural equation modeling (SEM) to examine the relationships between AI-enabled HR practices, employee engagement, and well-being. Data were collected through an online survey conducted between October 2024 and February 2025, employing a stratified random sampling method to select IT professionals across various roles and levels from leading IT companies in Chennai, India, a major hub for technology-driven enterprises. A standardized questionnaire was administered to collect data from 323 IT employees. The results indicate that AI-enabled training and development, performance appraisal, compensation, and reduced workload significantly and positively impact employee engagement (β = 0.560, 0.351, 0.366, 0.292; p = 0.000, 0.001, 0.000, 0.000), which in turn has a significant effect on employee well-being (β = 0.451; p = 0.001). Moreover, these HR practices directly enhance employee well-being (β = 0.253, 0.258, 0.330, 0.241; p = 0.000). Employee engagement was found to significantly mediate the relationship between AI-enabled HR practices and well-being (β = 0.194, 0.149, 0.121, 0.132; p = 0.000, 0.003, 0.000, 0.000), while technology readiness positively moderates the relationship between AI practices and engagement (β = 0.252, 0.158, 0.165, 0.212; p = 0.002, 0.000). These findings demonstrate AI’s positive impact on employee well-being and provide practical guidance for HR professionals in Chennai’s IT sector to effectively leverage AI technologies for a healthier, more engaged, and productive workforce.
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JEL Classification (Paper profile tab)M10, M12, M15
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References60
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Tables12
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Figures1
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- Figure 1. Conceptual framework
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- Table 1. Demographic characteristics
- Table 2. Measurement scales
- Table 3. Reliability
- Table 4. Correlation analysis
- Table 5. Factor loading, AVE, CR, and Cronbach’s alpha
- Table 6. Discriminant validity (Fornell-Larcker)
- Table 7. R-Squared
- Table 8. Statistical analysis (Direct effects)
- Table 9. Statistical analysis (Direct effects)
- Table 10. Mediation effects
- Table 11. Moderation effect of AI-enabled HR practices and employee engagement
- Table A1. Questionnaire
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