درخت حوزه‌های تخصصی

Objective: Consumers have recently become more concerned about environmental protection and have turned to eco-friendly products, also known as green products. To explore the factors that determine consumers’ green product purchase intentions, this study aims to measure the effects of TPB variables (e.g., perceived behavioural control, attitude, and subjective norms) on green product purchase intentions. Through this study, we build a theoretical framework that combines the ideas and perspectives of previous studies on sustainable consumption.Methods: In order to validate our research hypotheses, questionnaires were used to collect primary data from a sample of 219 participants in Algeria. Therefore, this research uses quantitative study method to design the research model which was tested using structural equation modeling (SEM) method. We analyzed the statistical data using SPSS v22.0 and Statistica v08.Results: The results show that purchasing intention of green products is positively affected by attitude (ATT), subjective norms (SN), and perceived behavior control (PBC). Furthermore, our findings discovered that the influence of PBC on intention to purchase green products was more significant than the effects of SN and ATT, enhancing our understanding of the key drivers of green product purchase intentions.Conclusion: These findings can generate more suitable managerial implications and policy contributions to promote green product consumption. It also highlights the key factors determining consumers’ eco-friendly purchasing intentions in Algeria. Therefore, identifying and understanding these factors enables the organization to develop effective strategies that guide consumers towards sustainable consumption and mitigate the negative repercussions of environmentally harmful consumption.

Financial sanctions have many economic consequences for the oil exporting economies. The sanctioned economy adopts economic policies to deal with it. This paper examines the relationship between financial sanctions, oil revenues and monetary and fiscal policies in Iran and explicates how financial sanctions have affected Iran's access to oil revenues. It also examines the role of fiscal and monetary policies in financial stability and resilience in Iran's economy. To this end, we employed a DSGE model with the new Keynesian approach. The results indicate that the interest rate, consumption, imports and inflation have a positive reaction to the oil revenue shock resulting from financial sanctions. However, the production, export, private sector investment and oil sales indicate a negative reaction to the oil revenues’ shock. Regarding the monetary policy shock, the reaction of production and consumption to the shock is positive. However, the reaction of oil sales and interest rate to this shock is negative. In terms of financial policy shock, production, consumption, investment and export indicated a positive reaction to this shock. However, the interest rate, imports and oil sales indicated a negative reaction to the fiscal policy shock. Monetary and fiscal policy shocks increase the effect of financial sanctions for a short period, while monetary policy shock has reduced the effect of financial sanctions for three periods. Therefore, monetary policy has been more effective than fiscal policy in reducing the effect of financial sanctions.

Financing decisions such as capital structure have gained much attention in literature of financial development over the last decade. Capital structure deviations from its optimal level can occur for various reasons. According to the dynamic trade-off theory, continuous adjustment of capital structure to maximize company value is essential. If companies adjust their capital structure quickly towards the target leverage, past financing activities and historical market conditions will only have short-term effects on the current capital structure. Conversely, if companies adjust their capital structure slowly, the opposite is true. We examine the relationship between organizational and performance characteristics and capital structure, as well as the speed of adjustment, in financial and non-financial firms listed on the Tehran Stock Exchange and compare these effects between the two categories of companies. The statistical population consists of companies listed on the Tehran Stock Exchange from 2017 to 2022. The results show that the speed of capital structure adjustment is lower in the financial sector compared to the non-financial sector. Additionally, three performance variables—profitability, growth opportunities, and liquidity—are statistically significant and impact capital structure and its adjustment speed in both financial and non-financial sectors. However, the growth opportunities variable has a different effect direction in the financial sector compared to the non-financial sector, while the age variable does not have a significant effect. Regarding organizational characteristics—complexity, institutional ownership, and size—only organizational complexity in the financial sector is significant at a 95% confidence level, while all organizational characteristics are significant in the non-financial sector

The present study aims to examine the relationship between cost stickiness and conservatism levels according to the type of industry. This purpose is achieved using the combined data technique and three models of non-operating accruals, the Basu model, and the market value model with a sample consisting of 113 companies listed on the Tehran Stock Exchange (TSE) over the period 2011 to 2020. Findings show that based on the model of non-operating accruals in the automotive, pharmaceutical, chemical, and food industries, cost stickiness decreases the level of conservatism. However, in the machinery industry, the relationship is significantly based on this model, but cost stickiness does not reduce the level of conservatism. According to the Basu model in the chemical, food, and basic metals industries, cost stickiness reduces the level of conservatism. But in the pharmaceutical industry and machinery and equipment, there is a significant relationship according to this model, but cost stickiness does not decrease the level of conservatism in companies. Also, based on the market value model in the automotive, chemical, machinery, and basic metal industries, there is a significant association, but cost stickiness does not decrease the level of conservatism in companies.

This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the smooth transition regression (STR) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first.

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .