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zahra maleki; zahra karimi takanlou; hossein asgharpour
Abstract
Although different economic schools have different views on the role of money in the economy, empirical evidence and the results of many previous studies indicate that monetary policies can affect production in various ways. Given the important role of banks, as financial intermediaries, in financing ...
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Although different economic schools have different views on the role of money in the economy, empirical evidence and the results of many previous studies indicate that monetary policies can affect production in various ways. Given the important role of banks, as financial intermediaries, in financing enterprises, examining the role of bank credits in production and value added of enterprises is of great importance. Therefore, the aim of this study is to examine the effect of the role of bank credits on the effect of monetary policies on the value added of the industrial and mining sectors of the provinces of Iran. For achieve this purpose, we used Generalized Moment Method) GMM with panel of 31 provinces during the period (2011-2021). First we survey the effect of monetary policies on bank credits and then the role of bank credits on the value added of this sector. The results of this study show that an increase in bank interest rates causes a decrease in given credits, while bank deposits and the number of bank branches cause an increase in credits. Also, bank credits have a positive and significant effect on the value added of the industry and mining sector, and inflation has a negative effect on the value added of the industrial section. Finally, the overall effect of monetary policy (through bank credits) on the value added of the industrial provinces of Iran is negative.
Mina Naeimi; Somayeh Azami
Abstract
Digital transformation is one of the main drivers of change in manufacturing industries; however, the mechanisms through which it influences sustainable development in developing countries are not yet fully understood. Focusing on Iran’s industrial sector, this study examines a causal framework ...
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Digital transformation is one of the main drivers of change in manufacturing industries; however, the mechanisms through which it influences sustainable development in developing countries are not yet fully understood. Focusing on Iran’s industrial sector, this study examines a causal framework in which digital transformation facilitates sustainable development through technological innovation. The conceptual model was developed based on the dynamic capabilities perspective and the four dimensional sustainability approach and was analyzed using partial least squares structural equation modeling with SmartPLS software. Survey data were collected from manufacturing industries in Iran, and three main paths of the model were tested. The findings show that digital transformation has a positive and significant effect on technological innovation, and technological innovation significantly strengthens sustainable development. The direct effect of digital transformation on sustainable development was not significant, although the explanatory power of the model was acceptable. Mediation analysis confirmed the full mediating role of technological innovation, indicating that the effect of digital transformation on sustainable development is mainly transmitted through strengthening innovation, organizational learning, and university–industry collaboration. From a policy perspective, it is recommended that industrial digitalization programs be aligned with innovation and sustainability goals and that their institutional foundations be strengthened through improved data governance and regulatory stability. The study empirically tests an integrated causal model in Iran’s industrial sector using quantitative data analysis and validation procedures, providing statistical evidence explaining the relationship between digital transformation, technological innovation, and sustainable development.
Economic Growth
Rafi Hassani Moghaddam
Abstract
Intellectual capital, through strengthening human capital, structural capital, and relational capital, can lay the groundwork for innovation, enhance resource productivity, and drive the development of clean technologies; however, its effectiveness is also contingent upon the institutional conditions ...
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Intellectual capital, through strengthening human capital, structural capital, and relational capital, can lay the groundwork for innovation, enhance resource productivity, and drive the development of clean technologies; however, its effectiveness is also contingent upon the institutional conditions of countries. Hence, the present study aims to investigate the impact of national intellectual capital on green economic growth and the moderating role of institutional quality in selected BRICS+ countries over the period 2000–2025. In this research, national intellectual capital is measured using three components—human capital, structural capital, and relational capital—and the Principal Component Analysis (PCA) method. Additionally, financial development, trade openness, government expenditure on education, renewable energy consumption, and foreign direct investment are considered control variables. Following an examination of the data characteristics, the model is estimated using the robust regression method. The results indicate that both national intellectual capital and institutional quality have a significant positive effect on green economic growth at the 5% significance level. In contrast, the interaction effect of national intellectual capital and institutional quality is negative and statistically significant at the 5% level. Thus, under the investigated circumstances, institutional quality does not necessarily strengthen the effect of intellectual capital on green economic growth. Furthermore, at the 5% significance level, financial development, trade openness, government expenditure on education, and renewable energy consumption exert significant positive impacts on green economic growth, whereas the effects of foreign direct investment are not statistically significant
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