AI-enabled and gamified green HRM practices: Impacts on employee environmental engagement
AI-enabled and gamified green HRM practices: Impacts on employee environmental engagement

Summary/Abstract: Research background: As climate concerns intensify globally, organizations face increasing pressure to integrate sustainability into their core human resource strategies. Although green human resource management has received growing scholarly attention, the role of digital enablers, particularly gamification and AI-driven personalization, remains insufficiently examined. In particular, limited empirical evidence exists on how technologically enabled green HR interventions contribute to the development of employee environmental literacy through organizational learning mechanisms. Purpose of the article: This study investigates how technologically enabled green human resource management practices influence employee environmental literacy. It further examines the sequential mediating roles of sustainable learning engagement and green knowledge internalization, while also exploring the moderating influence of contextual and individual factors within organizations. Methods: Using survey data collected from HR managers working in multinational corporations, the study applies a dual analytical approach. Partial least squares structural equation modeling (PLS-SEM) is used to test the hypothesized structural relationships, while machine learning algorithms (XGBoost, LASSO, and Random Forest) are employed to evaluate predictive relevance and identify nonlinear relationships within the data. Findings & value added: The results indicate that gamified green human resource management practices and AI-driven green human resource personalization significantly enhance employee environmental literacy. The analysis also confirms the sequential mediating roles of sustainable learning engagement and green knowledge internalization, demonstrating that technology-enabled human resource interventions influence environmental literacy primarily through learning-based mechanisms. Machine learning results further support the predictive relevance of the proposed framework, with XGBoost achieving the strongest predictive performance, followed by Random Forest and LASSO regression. The partial least squares structural equation modeling analysis confirms sequential mediation (the gamified green human resource management path; the AI-driven green human resource personalization path), and green organizational climate moderation is significant. In addition, green organizational climate and environmental values strengthen key relationships in the model, highlighting the importance of contextual and individual factors. By integrating ability-motivation-opportunity theory, the knowledge-attitude-behavior model, and social cognitive theory within a unified framework, this study contributes to the green human resource management literature and provides practical insights for human resource leaders seeking to design technology-enabled sustainability learning systems.

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