فهرست نشریات علمی دسترسی آزاد ایران

This study explores the effects of artificial intelligence (AI) investment on total factor productivity (TFP) in Iranian industries from 1997 to 2020, utilizing a comprehensive dataset organized by four-digit International Standard Industrial Classification (ISIC) codes. We employ the generalized method of moments (GMM) approach to address challenges such as endogeneity and collinearity within a dataset comprising over 200 cross-sectional variables.Our results reveal that both physical and intangible investments significantly influence TFP; a 1% increase in physical investment results in a 0.514% rise in TFP, while intangible investment leads to a 0.288% improvement. A key innovation of this research is the introduction of an AI measurement variable in the production function, employing the Corrado, Hulten, and Sichel (CHS) methodology for a clearer assessment of AI's productivity effects.Although AI investment positively correlates with TFP, its current impact is limited, reflecting the gradual adoption of advanced technologies in Iranian industries. This highlights the need for a comprehensive strategy to fully realize the productivity benefits of AI. We recommend policies aimed at facilitating technology integration and workforce specialization, including investing in training, providing incentives for AI adoption, and promoting collaboration between industry and educational institutions to enhance productivity and competitiveness in the global market.