Author
Listed:
- Vidhya Shree V
(VIT University, Chennai, India)
- L.R.K. Krishnan
(Professor (OB/HR, ER & LL), VIT University, Chennai, India)
- Maran Marimuthu
(Associate Professor, UTP, Malaysia)
- Poorani Sundarrajan
(Assistant Professor Junior, VIT University, Chennai, India)
Abstract
The primary goal of this study is to explore the potential of cutting-edge AI and ML technologies in enhancing employee engagement and streamlining Green Human Resource Management (HRM) processes. This study proposes that, given the growing convergence of technology and sustainability challenges, incorporating artificial intelligence (AI) and machine learning (ML) might considerably benefit and improve the practice of Green HRM. Various Scholarly literature was reviewed to understand the AI and ML in employee engagement. Data was collected through the distribution of standardized survey questionnaires; purposive sampling was used to identify people who are thoroughly aware of HR strategy and are involved in sustainable initiatives. Improved working conditions, higher levels of engagement, and more ambitious life goals would be portrayed in employees through the integrative approach of AI and ML technologies. Our research, therefore, contributes to knowledge of how AI-based solutions can raise employee engagement and consequently offer long-term sustainability for HR practices. The findings suggest that AI and ML driven technologies are organizational practices that may be adapted to enhance Green HRM. This, in turn, could attract more engaged employees with more sustainable HR practices and thus be more aligned with the organization’s broader sustainability and technological advancement goals.
Suggested Citation
Vidhya Shree V & L.R.K. Krishnan & Maran Marimuthu & Poorani Sundarrajan, 2024.
"AI and Machine Learning in the HR Ecosystem: Driving Employee Engagement,"
International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(9), pages 804-816, September.
Handle:
RePEc:bcp:journl:v:8:y:2024:i:9:p:804-816
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