درخت حوزه‌های تخصصی

With the widespread use of technology in today's society, the effective integration of technology in education has become a vital area of research, particularly in teacher education. Teacher variables play a crucial role in the successful incorporation of technology in the classroom. Consequently, several empirical studies have explored the factors influencing teachers' intentions to use technology. This study aims to contribute to this research by examining the roles of teacher self-efficacy and technology self-efficacy in predicting technology use among Iranian EFL instructors. A sample of 353 Iranian EFL instructors participated in an online survey, completing three self-report scales measuring the mentioned constructs. Structural Equation Modeling (SEM) was employed to analyze the collected data and test the relationships between the variables. The findings indicated that teacher technology self-efficacy had a more significant impact on intentions to use technology compared to teacher self-efficacy. Both predictor variables, however, showed unique effects on intentions to use technology. The paper concludes with practical implications and recommendations for further research.

In economies where banks play a key role in aggregating savings and allocating credit to various sectors, it is crucial to evaluate the performance of the banking system using appropriate methods. This research paper presents a model for evaluating the efficiency of commercial banks listed in the Tehran Stock Exchange during the period from 2015 to 2020, with a focus on the impact of credit risk. The study employs a two-step descriptive-correlation retrospective method to rank the banks and explain the role of credit risk in their efficiency. Specifically, the efficiency of the banks is determined using inputs and outputs based on DEA (Data Envelopment Analysis) models. The calculation of efficiency using ideal SBM (Slacks-Based Measure) and DEA methods reveals that Mellat, Saderat, and Tejaret banks were the most efficient during the study period. Furthermore, Tobit and logistic regression models are used to investigate the relationship between the main determinants of credit risk and the efficiency of commercial banks. The findings indicate a statistically significant relationship between the two factors. Overall, this paper highlights the importance of evaluating the efficiency of the banking system in bank-oriented economies and provides a useful model for doing so. The research paper highlights the significant impact of credit risk on bank efficiency, emphasizing its role in shaping effective risk management strategies within the banking sector. It suggests that banks should prioritize these factors to enhance their operational efficiency.

Technological advancement in recent years has compelled central banks to look for new ways to effectively provide payment systems that are fast and more convenient compared to traditional paper-based payment systems. It has led to the birth of the concept of digital currencies issued solely by central banks called Central Bank Digital Currency (CBDC). As this concept is relatively new, there is ongoing research being conducted by several central banks worldwide, and while the research work done thus far has answered many questions, there remains a number of unsettled issues and questions. This paper aims to delve into the literature on this concept and focus on several key questions like the reasons for issuing digital currency, payment systems efficiency, and the advantages and disadvantages of digital currencies issued by central banks. Answering these questions could substantially help figure out the usefulness of creating a new payment method alongside the current payment system. However, extensive future research is required for a comprehensive understanding the effectiveness and usefulness of digital currencies.

This paper intends to model the volatilities of returns of 20 different cryptocurrencies using daily data from 08/03/2018 to 09/20/2022. The multivariate factor stochastic volatility model (MFSV) within the framework of the nonlinear space-state approach is used. In this method, the cryptocurrency return volatility is decomposed into volatility rooted in latent factors and idiosyncratic volatility, and the time-varying pairwise correlation and dynamic covariance matrix are estimated in four sub-periods. The MFSV model’s results revealed that each sub-period contains a distinct number of latent factors, 2, 5, 4 and 2, which generally have a favorable impact on all cryptocurrency volatilities. The time-varying positive correlations between the return volatility of all cryptocurrencies are confirmed. Indeed, the strongest pairwise correlations belong to Ethereum, Litcoin, EOS, and VET in each sub-period, respectively. The DOGE, DOGE, Filecoin, and XRP, on the other hand, showedthe weakest correlations . As the pairwise correlations of cryptocurrency volatilities get strenger, especially during descending periods, it seems that the benefits of diversifying a crypto portfolio are getting less and less over time.

The daily unofficial exchange rate is crucial role in economic agents' decision-making and expectations in Iran. Hence, policymakers have tried to manage the unofficial market by promoting the official exchange rates as a leading price. In this regard, they have established official platforms for foreign exchange transactions. This study investigates the causal relationship between exchange rates discovered on the platforms and unofficial exchange rates by employing the Hong time-varying causality test based on the DCC-MGARCH method. Empirical results show a unilateral causal effect between the unofficial and official exchange rates. The instantaneous causality test results show an instantaneous unilateral causality from unofficial to official exchange rate over the whole period. However, the reverse is only found for some special sub-periods, such as when the extent of sanctions decreases, and the possibility of the Central Bank's intervention increases.

This paper focuses on the fragmentation of the production stages in a related production process. Comparing alternative technologies that produce similar goods, one with fewer production stages and the other with a greater number requires a holistic perspective for the entire production sequence. What matters is not only the strength of production links but also the length of the links, determined by the number of production stages. In this study, using the Inter-Country Input-Output table (ICIO) including Iran in 2016, We quantify the production status of 42 activities for 68 countries and the rest of the world, with a focus on the Iranian economy. To achieve this, we concentrate on the global output supply chain and input demand chains, specifically considering Iran. We use the output upstreamness (OU) measure and input downstreamness (ID) measure to quantitatively determine the relative positions of Iran's economic activities along the global output supply chain and input demand chain, respectively. This study provides insights into countries with the highest and lowest IDs globally, attributing them to industrial and agricultural activities, respectively. Iran's lower ID is explained by its role as a main supplier of natural resources, particularly in activities like oil and natural gas extraction.