Artificial Intelligence, Sustainable Human Resource Management, and Organisational Sustainability: A Multi-Level Integrative Framework - Masyhuri Masyhuri; Iqbal Lhutfi; Siswanto Siswanto; Amlan Haque (Journal of Economics, Entrepreneurship, Management Business and Accounting, 2026)
Purpose – This study proposes a multilevel integrative framework explaining how AI capabilities are transformed into sustainability outcomes through HRM architectures and employee mechanisms under institutional and governance contingencies. Design/methodology/approach – A systematic literature review (SLR) was conducted following PRISMA guidelines. This study identified 326 records, of which 36 studies met the inclusion criteria and were included in the final review. Finding/Results –The findings indicate that AI enhances sustainable HRM by strengthening employee abilities, motivation, and opportunities, while simultaneously enabling organisational dynamic capabilities such as sensing, seizing, and transforming. From a socio-technical perspective, effective AI implementation depends on the alignment between technological systems and human factors. Originality/Value – This study provides theoretical and practical implications by demonstrating that the integration of the AMO framework, dynamic capabilities, and socio-technical systems strengthens the understanding of how AI-driven HRM contributes to sustainability has implication for managers, policy makers and regulators.