From technology acceptance to artificial intelligence adoption: comparative insights from the United States, South Korea and Turkiye

Purpose- This study explores the conceptual transition from the Technology Acceptance Model (TAM) toward emerging Artificial Intelligence acceptance perspectives while comparatively examining technological development and its associations with sustainability indicators in the United States, South Korea and Turkiye. Despite growing literature on AI adoption, cross-country assessments linking AI capacity with environmental performance remain limited. This study addresses this gap by evaluating the alignment between national AI development trajectories and sustainability outcomes. Methodology- The research adopts a comparative descriptive design based on secondary data from internationally recognized reports and statistical databases. Country-level indicators related to AI investment, innovation capacity, carbon intensity, renewable energy use and environmental performance are systematically compared. The study does not aim to establish causal relationships but rather to identify associative patterns across countries at different stages of technological development. Findings- Results suggest that higher levels of AI investment and technological capacity are descriptively associated with improvements in energy efficiency, reductions in carbon emissions and enhanced environmental performance. However, the strength of this relationship varies according to national technological readiness and institutional capacity. The United States shows the strongest alignment between AI development and sustainability improvement, South Korea demonstrates moderate progress, and Turkiye shows ongoing technological development, although sustainability gains remain comparatively limited due to structural and implementation challenges. Conclusion- The study extends technology acceptance discussions beyond the individual level by presenting a comparative national framework linking AI adoption with sustainable development outcomes. Findings indicate that technological investment alone is insufficient; effective sustainability performance requires institutional capacity and coordinated policy implementation. The results offer practical insights for policymakers seeking to align digital transformation strategies with long-term sustainability goals, while future research should broaden comparative scope and examine sectoral and long-term impacts of AI adoption on sustainability performance. Keywords: Artificial intelligence, artificial intelligence acceptance, sustainability, technology acceptance model, cross-country comparison.