مطالب

This study examines Russia’s strategic culture in West Asia and its implications for bilateral relations with Iran from the post-Soviet period (1991) to recent developments in 2025. Employing a qualitative framework rooted in strategic culture analysis, the research demonstrates that Russia’s approach toward Iran is shaped by pragmatic opportunism, historical experience, and a desire to maintain regional influence without overcommitting militarily. Findings reveal that Moscow engages with Tehran episodically, balancing cooperation against shared threats with strategic caution to preserve flexibility in relations with other regional actors, including Israel, Saudi Arabia, and Turkey. Brief episodes such as the U.S. and Israeli strikes on Iranian nuclear facilities, the Syrian crisis, and the ongoing war in Ukraine illustrate how Russia mediates, condemns actions diplomatically, and selectively coordinates with Iran without offering binding security guarantees. The study identifies a persistent divergence in perceptions: Russia often views Iran as a secondary, conditional partner, whereas Tehran expects collaboration on an equal footing. Despite these differences, mutual interests in regional stability, energy cooperation, and strategic depth create occasions for tactical alignment, particularly under external pressures. Overall, the results demonstrate that the Russia–Iran relationship is conditional, adaptive, and strategically ambiguous, with cooperation shaped less by ideological convergence and more by pragmatic calculations. By situating these interactions within the framework of strategic culture, the study provides a nuanced understanding of the limits and possibilities of Russia–Iran engagement in West Asia, highlighting how historical legacies, geopolitical constraints, and contemporary crises collectively define a partnership that is influential yet inherently fragile.

Objective : In the competitive global retail industry, achieving sustainable competitive advantage is a key factor for long-term success. This advantage arises when companies effectively utilize their unique resources and capabilities to outperform competitors. Operational efficiency and financial performance are critical for evaluating competitiveness and investment attractiveness. Data Envelopment Analysis (DEA) is a standard method for measuring efficiency, but classical DEA cannot fully rank efficient units. Integrating DEA with multi-criteria decision-making (MCDM) methods addresses this limitation, considering investor-relevant financial ratios. This study proposes a two-stage approach to evaluate and rank retail companies comprehensively.  Methodology: In the first stage, an input-oriented CCR model of DEA is applied, with assets, operating expenses, and the number of employees as inputs, and total revenue and net profit as outputs, to assess relative efficiency. In the second stage, financial indicators—asset turnover, dividend yield, return on equity (ROE), return on assets (ROA), and return on investment (ROI)—alongside DEA efficiency scores are evaluated using the PROMETHEE II method to generate a complete preference-based ranking of retailers. Results : DEA in the first stage provides relative efficiency insights but cannot rank efficient units. Employing PROMETHEE II in the second stage, and considering financial ratios, overcomes this limitation and produces a comprehensive ranking. Validation against DEA, hybrid DEA–PROMETHEE II, and hybrid DEA–AHP rankings demonstrates a strong alignment of the results with the actual market positions of retailers. Conclusion : The proposed method enables investors to identify high-performing companies and provides retailers with a strategic tool to monitor competitiveness, identify strengths and weaknesses, optimize resource allocation, and achieve a sustainable competitive advantage.

Purpose : This research aims to develop an advertising policy model in digital marketing based on eye tracking. Method : The research method is qualitative and based on grounded theory. Semi-structured interviews were used to collect information, and data analysis was performed using the Strauss and Corbin method and the paradigm model. Sampling was theoretical sampling using targeted (judgmental) techniques, based on which 15 interviews were conducted with managers and marketing and advertising experts. Findings : The findings of the research show that during the process of open, central and selective coding, the advertising policy model based on the use of eye tracking consists of Causal categories that include Advertising content, Quality of environmental advertising and Promotional features; contextual categories consist of advertising slogans,  billboard elements, analysis of customer eye movements;  intervening categories consist of online advertising,  advertising costs, advertising through media and mass communication; Central categories that consist of environmental advertising status, development of advertising influence, effective advertising on customers' intentions; category of strategies includes advertising message attractions, evaluation of target environmental advertising selection process, advertising based on customers' taste; consequence category includes competitive advantage, currency innovation, Promotion of strategic marketing decisions. Conclusion : The study concludes that the eye tracking-based advertising policy model provides a comprehensive framework for enhancing advertising strategies. The model emphasizes the importance of considering various factors such as advertising content, consumer behavior, and contextual elements in developing effective advertising campaigns. The research highlights the potential of eye tracking in optimizing advertisements and gaining a competitive edge in the market. From a managerial perspective, the model offers actionable insights for marketers to improve customer engagement and increase advertising effectiveness by leveraging eye tracking data. It also suggests that advertisements should be tailored to consumer preferences and the context in which they are viewed. The study advocates for further research in this area to broaden the application of eye tracking technology in other marketing domains and service sectors. From an academic standpoint, this research contributes to the limited body of knowledge regarding environmental advertising and the use of eye tracking in marketing. It provides a foundation for future studies to explore the relationship between visual marketing and consumer behavior, offering insights that could foster innovative advertising strategies in the digital marketing field.

The motivations driving cash holdings have a profound influence on corporate decision-making and performance. Exploring the dynamics between risk-taking behaviour, cash reserves, and their adjustment pace provides valuable insights into effective financial resource management. This study examines the impact of corporate risk-taking on the adjustment speed of cash holdings, with a focus on the moderating effect of CEO tenure. A sample of 151 firms listed on the Tehran Stock Exchange from 2011 to 2023 (1,963 firm-year observations) was analysed using multiple regression and the Generalized Method of Moments (GMM) estimator. Results indicate that the adjustment speed of cash holdings is 49.5%. A significant negative relationship exists between corporate risk-taking and the speed of cash holdings adjustment, suggesting that elevated risk-taking decelerates the alignment of cash reserves with optimal levels. Moreover, the findings highlight the moderating role of CEO tenure in the relationship between corporate risk-taking and the speed of cash holdings adjustment; in other words, in firms with longer-tenured CEOs, the negative association between corporate risk-taking and cash holdings adjustment speed is weaker than in firms with shorter-tenured CEOs. These findings suggest that risk-taking hinders swift cash adjustment, necessitating a precise determination of optimal cash levels to prevent liquidity shortages in high-risk scenarios. Additionally, the experience of long-tenured CEOs appears to facilitate better liquidity management, aligning corporate interests with strategic financial goals.

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.