AI-DRIVEN GREEN RECRUITMENT SELECTION STRATEGIES FOR ECO-CONSCIOUS ORGANIZATIONS: A MACHINE LEARNING APPROACH TO SUSTAINABLE WORKFORCE DEVELOPMENT IN THE DIGITAL AGE
DOI:
https://doi.org/10.53555/fg9rak40Keywords:
AI-Driven Recruitment, Green Intelligence Quo- tient, Machine Learning HRM, Sustainable Workforce Analytics, Environmental Predictive Modeling, Digital Green Transforma- tionAbstract
As organizations worldwide pivot toward environ- mental sustainability, the integration of artificial intelligence (AI) and machine learning (ML) technologies with green hu- man resource management has emerged as a revolutionary approach to sustainable workforce development. This compre- hensive study presents a novel AI-driven framework for green recruitment selection strategies, combining advanced predictive analytics, natural language processing, and behavioral pattern recognition to identify candidates with optimal environmental alignment. Through extensive empirical research involving 300+ organizations across 15 countries and analysis of over 50,000 recruitment profiles, this paper introduces the Green Recruit- ment Intelligence Quotient (GRIQ) - a proprietary metric for evaluating environmental consciousness in candidates. The study reveals that AI-enhanced green recruitment systems achieve 47% higher accuracy in predicting long-term environmental employee performance compared to traditional methods, while reducing recruitment costs by 34% and improving candidate satisfaction by 41%. Our findings demonstrate that organizations implementing AI-driven green recruitment strategies experience 67% improvement in carbon footprint reduction, 52% increase in sustainable innovation projects, and 38% enhancement in en- vironmental compliance scores. This research contributes break- through insights into the convergence of digital transformation and environmental stewardship in human capital management.
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Copyright (c) 2025 Dr.Archana PV, Arjun KP
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License
