درخت حوزه‌های تخصصی

The primary objective of this study is to develop a predictive model for money laundering risk in Iranian listed firms. Initially, firm-level money laundering risk is measured using auditor assessments of anti-money laundering (AML) activities disclosed in annual audit reports. Subsequently, a quantitative modeling approach is employed, using financial and governance-related variables identified in prior research. To validate the quantitative findings, a qualitative approach based on grounded theory is also applied to identify additional explanatory factors. This research follows a mixed-methods design, incorporating both quantitative and qualitative phases. In the quantitative phase, a panel logit regression model is estimated using data from 1,680 firm-year observations covering the period 2012–2023. Independent variables include firm size, return on equity, leverage, investment opportunities, board independence, and board size. In the qualitative phase, semi-structured interviews were conducted with 10 experts to identify key risk factors, followed by the design and administration of an 18-item questionnaire distributed to 110 professionals. Exploratory factor analysis was then used to extract latent variables. The quantitative analysis reveals significant relationships between money laundering risk and several variables, such as firm size (positive), return on equity (negative), leverage (positive), and board independence (negative). The qualitative analysis identifies three core factors: (1) organizational culture and employee training, (2) corporate governance, and (3) a composite factor comprising compliance, organizational complexity, financial performance, firm size, and capital structure. Together, these factors explain over 50% of the variance in expert responses. The convergence of results from both methodological approaches confirms the robustness of the proposed model. Corporate governance indicators—particularly board size and independence—alongside financial attributes such as firm size, profitability, and capital structure, are found to be significant predictors of firm-level money laundering risk. The findings underscore the importance of strengthening internal control mechanisms and compliance structures in reducing money laundering risk.

Sustainable Communications (SC) is a strategic approach in industries, especially in high-risk sectors, and has gained great importance today. This study explores the non-linear dynamics (U-shaped and S-shaped) between sustainable communication (SC) and financial performance (FP) in high-risk industries, including oil and gas, petrochemicals, mining, and transportation, listed on the Tehran Stock Exchange and Iran Fara Bourse over the period 2015–2024 (48 companies). Utilizing panel regression, cross-sectional regression, quantile panel regression, and Granger causality tests, the analysis integrates organizational learning, supply chain perspectives, and stakeholder theory. Findings confirm U- and S-shaped relationships, suggesting that moderate SC enhances FP, whereas excessive communication may undermine stakeholder trust, with sustained efforts yielding long-term benefits. Financial sector development amplifies the positive effects of SC, while ESG rating divergence exacerbates the adverse impacts of over-communication. Sustainability reporting requirements reinforce the benefits of balanced SC. Quantile regressions reveal heterogeneity, with stronger SC effects in high-performing firms. Granger causality tests indicate unidirectional causality from SC to FP. Industry-specific analyses highlight superior performance in petrochemicals and challenges in transportation. The study offers practical implications for optimizing SC, strengthening financial sector development, and standardizing ESG reporting. Future research should incorporate granular ESG data and dynamic modeling approaches.

Iran's gas industry plays a strategic role in supplying and distributing natural gas energy to society. The project-based, operational, and service-oriented nature of this industry, combined with its large workforce, underscores the necessity to address the risks confronting its human resource management system. This study was conducted to identify and analyse these human resource management risks. This study employed a mixed-methods approach. In the qualitative phase, the researcher's lived experience was utilized to identify and categorize 35 human resource management risk factors in Iran's gas industry. For the quantitative phase, a risk management framework was applied to evaluate each identified factor based on two key dimensions: risk probability and risk impact. The assessment results were subsequently visualized through a heat map. The study results demonstrated that none of the indicators fell within the low-risk zone, with 15 indicators classified as medium-risk and 20 as high-risk, while two critical indicators - imposition of salary and benefit restrictions with a risk rating of 9 and dependence on political affiliations for filling key positions with a risk rating of 8 - were identified as the most substantial challenges facing the human resource management system in Iran's gas industry.