Research Paper
Other
Soraya Jelvezan; Ataaulah Mohammadi Malqarani; Behrooz Shahmoradi
Abstract
Profit, as presented in financial statements, is one of the most important performance metrics and a key determinant of an economic entity's value. The primary objective of this research is to assess product profitability based on a Product Complexity Index (PCI), considering the product's diversity ...
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Profit, as presented in financial statements, is one of the most important performance metrics and a key determinant of an economic entity's value. The primary objective of this research is to assess product profitability based on a Product Complexity Index (PCI), considering the product's diversity and market reach across different markets. The study analyzes a sample of 500 profitable companies listed in the Fortune, spanning the financial years from 2014 to 2018. The analysis employs both panel and pooled data methods. The study aims to estimate the profitability of each product to help investors identify the most profitable products for investment, based on their profitability as determined by the PCI. The findings suggest that adopting a new approach focused on producing high-complexity products, rather than merely selecting product types or business activities, along with measuring the profitability index of each product, can enhance decision-making. By examining the relationship between product complexity and profitability, and introducing an index to forecast product profitability, investors who are key users of financial information can make more informed decisions. These decisions, based on the optimal combination and selection of products, can foster economic growth and contribute to the development of society. The results show a positive and significant relationship between operating profit and the Product Complexity Index, as well as the estimation of profitability based on product diversity and market reach.
Research Paper
Econometrics
Forough Esmaeily Sadrabadi; Maedeh Khanari
Abstract
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 ...
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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.
